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					BSc(B) 6th. semester                                     Course: Bachelor Thesis


                                                                          Author:
                                                            Daniel Andreas Bøhler
                                                                     Mads Larsen

                                                                          Advisor:
                                                               Stig Vinther Møller




                       Bachelor Thesis
   The oil price influence on the stock market
- An industrial perspective for Norway and Denmark between 1990-2010




                         The Aarhus School of Business

                                     2010
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

Executive Summary

        The motivation behind our topic lies in that we want to investigate a field that has drawn a
lot of attention in the previous years, and are likely to continue to draw further attention as the oil
price seems to more volatile and reach new heights more often, but also because most of the
previous literatures have only focused a oil price factor at macro or micro level and few have
focused on industry level. The chose of Norway and Denmark is because of their similarity of how
the nations economy is build-up, but also of there differences in there dependent of oil.

        The paper starts with given an orientation of how the methodology, and how the design of
both the calculations and data collection have been done. It also specified a section in the paper
were it gives critics to previous literatures, but also lists improvements used in this paper. Focus on
using statically concepts and rules in estimation how the monthly stock returns for different
industries are affected by an oil price factor in order to create a good and consistent prediction, the
implementation of the two-factor and three-factor models from the research article from Faff and
Brailsford (1999), which they conducted on the Australian stock market, have been used to solve
the problem statement of this paper; if an oil price factor affect the same industries in Norway and
Denmark in terms of equity returns to an oil price factor expressed in both NOK or DKK, and USD
over the time period 1990:1 – 2010:3. An underlying purpose to our problem statement were to
see if the implemented models used can explain if an oil price factor is significant in both Norway
and Denmark in terms of stock returns for different industries.

        The overall conclusion to the report showed differences in the same industries between
Norway and Denmark. In the explanation of the difference is based on the companies included in
the different industries are different and it seems that general industries in Denmark are more
diversified in there business area then Norwegian industries, but also in the fact that Norway is
more sensitive to a oil price factor then Denmark.




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                                     The oil price influence on the stock market

                 - An industrial perspective for Norway and Denmark between 1990-2010




Table of Contents
Executive Summary .................................................................................................................... 2

1. - Introduction ......................................................................................................................... 6

           1.1 - Problem statement........................................................................................................... 7

           1.2 - Structure and Assumptions .............................................................................................. 8

           1.3 - Delimitation ...................................................................................................................... 8

2. - Methodology ....................................................................................................................... 9

           2.1 - Previous literature ............................................................................................................ 9

           2.2 – Critic of previous literature............................................................................................ 10

           2.3 - Assumptions of the classical linear regression model.................................................... 11

           2.4 - Methodology adopted.................................................................................................... 12

                       2.4.1 - Additional calculations .................................................................................. 15

           2.5 - Research design and testing ........................................................................................... 17

           2.6 - Data collection................................................................................................................ 19

3. - The Oil market ....................................................................................................................19

           3.1 – The oil proxies................................................................................................................ 22

                       3.1.1 - Brent crude .................................................................................................... 23

                       3.1.2 - West Texas Intermediate............................................................................... 23

                       3.1.3 - Difference ...................................................................................................... 23

           3.2 - The Oil Market and the Norwegian Economy ................................................................ 24

           3.3 - The Oil Market and the Danish Economy ....................................................................... 26

                       3.3.1 - The quest for oil ............................................................................................. 26

                       3.3.2 - The impact on the Danish economy .............................................................. 27


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                                    The oil price influence on the stock market

                - An industrial perspective for Norway and Denmark between 1990-2010

4. - The oil price development and volatility 1990:1-2010:3 ........................................................30

          4.1 - Oil price development from 1990:1 – 1999:12 .............................................................. 31

          4.2 - Oil price development from 2000:1 – 2010:3 ................................................................ 32

5. - The oil market in relation to the stock market in general ......................................................32

6. - Industries under consider investigation ................................................................................33

          6.1 - Norwegian Industries ..................................................................................................... 33

          6.2 - Danish Industries ............................................................................................................ 34

7. - Empirical results for Norway ................................................................................................34

          7.1 - Banks .............................................................................................................................. 35

          7.2 - Chemicals........................................................................................................................ 37

          7.3 - Consumer Staples ........................................................................................................... 38

          7.4 - Financials ........................................................................................................................ 39

          7.5 - Food and Beverage ......................................................................................................... 40

          7.6 - Food Products................................................................................................................. 41

          7.7 - Food Producers............................................................................................................... 42

          7.8 - Good and Service............................................................................................................ 44

          7.9 - Industry Transportation ................................................................................................. 45

          7.10 - Industrials ..................................................................................................................... 46

          7.11 - Insurance ...................................................................................................................... 48

          7.12 - Int. Oil and Gas ............................................................................................................. 49

          7.13 - Marine Transportation ................................................................................................. 50

          7.14 - Oil and Gas Production ................................................................................................. 51

          7.15 - Oil and Gas.................................................................................................................... 52

          7.16 – Utilities ......................................................................................................................... 54

8. - Empirical results for Denmark ..............................................................................................55




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                                     The oil price influence on the stock market

                 - An industrial perspective for Norway and Denmark between 1990-2010

           8.1 - Banks .............................................................................................................................. 55

           8.2 - Construction and materials ............................................................................................ 56

           8.3 - Goods and services ......................................................................................................... 58

           8.4 - Hld & Dvlp....................................................................................................................... 59

           8.5 - Industrial transportation ................................................................................................ 61

           8.6 - Oil and gas ...................................................................................................................... 63

           8.7 - Marine Transportation ................................................................................................... 64

           8.8 - Real estate ...................................................................................................................... 65

           8.9 - Telecommunication ........................................................................................................ 65

9. - Hedging oil price and diversify risk .......................................................................................66

10. - Further analysis .................................................................................................................67

11. - Conclusion.........................................................................................................................68

List of references.......................................................................................................................71

Appendix ..................................................................................................................................74




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                             The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

1. - Introduction
         Having an understanding of how the oil price has an impact on equity returns in different
industries are important for the management, shareholders1 and stakeholders. The basic logic of
using oil price movements as a factor of valuating stock return for industries in Norway and
Denmark is straightforward. The cash flows that are generated by the companies in the industries
are affected by macroeconomic events like the influence of changes in the oil price.

         Over the last three decades, oil prices have changed with sequences of very large increases
and decreases, therefore predicting shifts in the oil price can be difficult, even with a high
correlation between growth in GDP and growth in industrial output. Countries with higher
economic growth are most likely to increase the demand for oil, but oil prices may also cause
unwanted inflation pressure on the economy, and thereby affect the stock market. The stock
market may also be seen as a place to protect the money from being eaten by inflation.

         It is stated that stock prices should reflect future net cash flows of a given company, so the
effect of oil price shocks, or the changes in the oil price, is a meaningful and useful measure of the
economic impact it may cause to companies earnings. As the present value of stocks represents
the present discount value of future earnings for companies, both current and expected future
earnings, impacts of changes in the oil price should be discounted in the stock’s price fairly quickly.2
An oil price factor may impact stock returns as can be stated in the economic report of President
2006:

         ”In the long run, households and businesses respond to higher fuel prices by cutting
consumption, purchasing products that are more efficient, and switching to alternative energy
sources. Higher energy prices also encourage entrepreneurs to invest in the research and
development of new energy-conserving technologies and alternative fuels, further expanding the
opportunities available to households and business to reduce energy use and switch to low-cost
sources” (Economic Report of the President, 2006, p. 243)

         The focus on the Norway is particular relevant and interesting given the large amount of oil
export to the world market. Today, Norway is the 5th largest oil exporter in the world, and



1
  Shareholders are both individual investors and institutional portfolio managers.
2
  Market efficiency theory states that stock prices in a perfect efficient market should reflect all available
information.


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                            The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

accounts for 3.77 % on average of the total world supply of oil in the time period 1990-2010. With
the findings of oil in the North Sea around the 1960’s, Denmark also became an oil-producing
nation and accounts for about 0.5 % of the aggregate world supply of oil3.

          It is expected that the prediction signs and relative magnitudes across the specifics
industries in both Norway and Denmark will approximately show the same signs when
implementing an oil price factor. The reason for the prediction is the high trade volume between
Norway and Denmark in terms of GDP, but also the similarity for how the economies are build-up.
Another reason why we find this subject interesting is that, to our knowledge this type of
investigation has never been conducted in either Norway or Denmark.

          The oil has been seen as a one of the most important economic activity drivers, and
therefore, many previous research papers have confirmed that changes in the oil price affect the
stock market. In terms of microeconomics, it is believed that higher oil prices might affect the
domestic economy in terms of lower consumer welfare at the cost of higher producer’s welfare. It
is especially caused by a rise in the cost of production of goods and services, and the oil price
influence on inflation and consumer confidence.

1.1 - Problem statement
          By using previous literature and different econometrics models that have been conducted
in both the global financial markets as well as in individuals markets, this paper will implement
some of the same models, and conduct the models on the Norwegian and Danish stock markets4.

          The objective of this paper is to analyse if an oil price factor affect the same industries5 in
Norway and Denmark in terms of equity returns to an oil price factor expressed in both NOK and
DKK, and USD over the time period 1990:1 – 2010:3. An underlying purpose is therefore to see if
the implemented models used can explain if an oil price factor is significant in both Norway and
Denmark in terms of stock returns for different industries.


3
    From figure 3.1 in appendixes.
4
 The Norwegian stock exchanges goes under the name OSEBX, while the Danish stock exchange is
known as OMXC20.
5
 We have tried to gather information from Norway and Denmark for the corresponding industries.
However, there are some small differences between the two countries, since some data was not
available or the corresponding industry did not exist in the other country.


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                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

        We expect the potential for a negative oil price sensitivity to be greatest in industry sectors
where they have a high proportion of their costs devoted to oil-based inputs or where their
revenue is significant towards oil. On the other hand, we expect an industry like “Oil & Gas” to
benefit from a higher price of oil and therefore have a positive sensitivity, while transportation
industries will react negative. (Faff and Brailsford, 1999)

        We will use two types of crude oil called London Brent crude and West Texas Intermediate
as the oil price factor. However, since we have the perception that we will find the most significant
results by using the London Brent crude due to the close connection to the Norwegian and Danish
economy, our main emphasis will be on LBC.

1.2 - Structure and Assumptions
        We have chosen to keep a simple problem solving structure to ensure a fluent and easy
understanding analysis to come up with possible answers to the problems presented in the our
problem statement. There are no actual chapters, but roughly speaking the paper can be divided
into four parts:

    1. Section one is an introduction to the subject including problem statement, assumptions,
        delimitations and methodology.
    2. Second section covers relevant information about the oil market, which includes an
        analysis of the oil price over the time period 1990:1 – 2010:3. Further there is a description
        about the Danish and Norwegian stock markets and their dependence of oil, but also how
        the exchanges rates between USD/NOK and USD/DKK have developed in the time frame.
    3. The third section will include our data analysis. We will go thorough through our results
        and try to clarify and give an explanation to our findings, and discuss the option of using
        hedging alternatives to protect against an oil price factor.
    4. Fourth, final remarks, includes a section for further analysis, conclusion, references and
        appendix.

1.3 - Delimitation
        As this paper only focuses on two types of crude oil (London Brent and WTI), there may
exists other types that proves to be significant. However, it is believed that the Danish and
Norwegian industries will be most correlated with the chosen two. Further does the paper also
omit analysing the possible effect on our dependent variables by gas, or even other types of energy




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                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

sources even though both Norway and Denmark extracts many of these sorts of energy from the
same fields as the oil.

        Any macroeconomic variables such as inflation, GDP, growth rates etc will not be used,
even though it is statistical proven to be significant when used in connection to an oil price factor.
But also because the paper only uses a two-factor and three-factor model in the investigation of
the oil price sensitivity across the industries in Norway and Denmark.

        All of the data used has been tested separately for unit root problems, but has not been
commented on given the fact that the paper uses first difference data, and not level data where an
unit problem may exist.

        In our industry analysis we will only focus on the industries, which return significant
estimates for either one or both of the equations. Industries which return is seen to have
insignificant estimates for all variables in both equations used will no be commented further upon.



2. - Methodology
        The following section outline and shortly discuss the methodological structure of the
bachelor thesis. It is to give the reader a better understanding of how the results has been
generated by describing the progress of making the analysis of the monthly stock industries in
Norway and Denmark, and which models that has been used. First the paper will go through the
basic structure, and then it will discuss how using different analysis methods have solved the
problems. The section ends with covering which additional tests that have been implemented, and
how the implemented theory fits with the analysis and the data collection process conducted.

2.1 - Previous literature
        There has been a lot of research in the field of how oil prices and oil shocks affects stock
return in the general market such as the main index or benchmarks. Jones and Kaul (1996)
investigated how stock returns in USA, Canada, Japan, and UK reacted to oil shocks on the
economy in these countries. They concluded that the aggregated stock returns in these countries
reacted negatively to the impact of oil shocks. Dreisprong, Jacobsen and Maat (2008) tested if
monthly oil price changes can be used to predict worldwide stock market returns. They used an
extensive amount of data from 48 different markets, and found a significant predictability in most
markets.




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                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

          But few has researched and explored the effect of oil price changes or oil shocks on stock
return in general industries. But in the recent years, there has been a continuing interest by
researchers trying to explain the role and the impact oil and other energy sources have on financial
markets and stock returns of large listed companies. The main paper that focused on how stock
returns in certain industries are affected by the oil price change was first introduced in 1999 by Faff
and Brailsford6 (1999). Faff and Brailsford concluded that the oil price had a significant positive
impact on the Australian oil and gas, and diversified resources industry, while other industries like
paper and packing, banks and transport appeared to have a negative correlation based on high oil
price.

         Other researchers like Al-Mudhaf and Goodwin(1993) made a company-specific study,
where they examined the returns from 29 oil companies listed on the New York Stock Exchange
(NYSE) during the highly volatile oil prices in 1973. They reported that there exists a positive
significance relationship between firms’ stock return and those that had significant assets in
domestic oil production. Almost the same type of research showed that stock returns of Canadian
oil and gas companies reacted positively to oil price increases (Sadorsky, 2001). Another research
that was based on the Canadian oil and gas returns, were Boyer and Filion (2007) who used a
multifactor model determined by the returns. They found a positive relationship between energy
stock returns and an increase in the oil and gas prices. Pollet (2005) used monthly oil price data to
predict excess market returns for certain US industries.

         Other researchers like J. Strong (Strong, 1991) examined how well investors are able to
hedge against oil price risk using oil equity portfolio, while others have examined how well
valuation principles apply to oil and gas equities (Miller & Upton, 1985).

2.2 – Critic of previous literature
         The regression models used in this report seems to be well used in other previous
literatures, where Faff and Brailsford (1999) started using it on the Australian market from 1983-
1999. Even though they used a value-weighted market proxy that included the MSCI and a global
market index combined as there market proxies, this paper have used market proxies that includes
all the listed companies at the respected individual stock exchanges in Norway and Denmark. The
value of the market proxies used by Faff and Brailsford (1999) were in nominal values. However,


6
 The current paper is based on Faff’s and Brailsford’s methods which they used on the Australian market in
1999 that covers the time period 1983-1999.


                                                                                                         10
                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

the return index (RI) is better to use to calculate the monthly returns, because it includes the fact
that the dividends are reinvested. The market index proxies for both Norway and Denmark are
therefore in RI value.

        Further does Faff and Brailsford (1999) not mention which type of oil price factor they used,
and because of different types of oils are extracted at different places around the world, the
believe that an oil price factor that are close to the countries economy’s should give a better value
in determine if some of the different industries the country’s economy are affected. Given the fact
that both Norway and Denmark are significant smaller countries then Australia, both in size and
GDP7, and are more reliable on oil revenue, especially Norway, industries in Norway and Denmark
may react differently than industries in Australia. Even in the same industries like bank and
transportation, there may be differences. It is therefore believed that the model and the results
from Australia or other markets may not be comparable to the results in either Norway or
Denmark as results of large differences in the nation’s economies.

        In using the regression model from Faff and Brailsfords (1999) we have implemented the
changes in the market proxies, and used different oil price factor that suits both Norway’s and
Denmark’s economy better, and therefore it should give a better picture of how an oil price factor
affects the different stock returns across the industries.

2.3 - Assumptions of the classical linear regression model
        Each regression model follows the assumptions of the classical linear regression model8
(Greene, 2003) that consist of a set of assumptions about how the data set will be produced by an
underlying ”data-generating process”. The assumptions will deterministic specify a relationship
between the explained variable, and the explanatory variables.

        1. Linear in parameters - The regression model specifies a linear relationship between
             the explained variable, and the explanatory variables.




7
  According to both Eurostat and Economagic.com, the respected value of GPD in USD for Denmark, Norway
and Australia is 54,015, 76 313, and 253,304. This indicates that Australia economy is twice as large as both
Norway and Denmark together, measured in GDP.
8
  All of the individual variables used in this report have been tested, and does not violate any of the
assumptions. The model used is therefore estimated to be BLUE – Best Linear Unbiased Estimator. See
“delimitation” section for unit root testing – not included as one of the assumptions.


                                                                                                           11
                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

         2. No perfect collinearity - There is no exact linear relationship between the explanatory
             variables, which means that no of the variables used in the regression model is perfect
             correlated with each other.
         3. Zero conditional mean - The expected value of the disturbance at observation in the
             sample is not a function of the explanatory variables observed. The explanatory
             variables will not carry useful information for predicting I that is correlated with any of
             the explanatory variables in the regression model.
         4. Homoscedasticity - Each disturbance i has the same infinite variance, 2 and is
             uncorrelated with other variables, which means the error term, i, is assumed to be
             independent and identically distributed with a zero mean and constant variance.
             Ideally the chosen models should also have no serial correlation in the residuals, which
             we have tested for using DW critical values.
         5. No serial correlation -The data in the regression model may be any mixture of
             constants and random variables.
         6. Normality - The distribution of the data is normally distributed. Testing for normal
             distribution is conducted using the Jarque-Bera test because it is based on the kurtosis
             and skeweness of the samples.


By fulfilling assumptions 1-3, it is reasonable to believe that the OLS estimators are unbiased. If
further strengthened by assumption 4-5, it can be assumed that the OLS estimators are BLUE.
When adding assumption 6 and, it is consistent, the t-statistics can be used for testing significance.

2.4 - Methodology adopted
         The relationship between oil return and stock return, either individual groups or indexes,
may be either positive or negative depending upon whether the country is either importing or
exporting oil, and how the country’s economy is influenced by the oil market in general.

         It is believed that two different primary channels can explain stock prices, and therefore
returns. First, we consider crude oil to be a major input in the production process, and secondly,
that oil price shocks can affect the prices of the stocks through the discounted rate9, and the




9
 A higher expected inflation rate will raise the discount rate, which will have a negative impact on the stock
prices (returns)


                                                                                                             12
                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

expected real interest rate10. The second assumption lies in the fact that both the inflation rate and
interest rate are affected by the oil prices. This again, can affect the exchange rate between the
USD and the domestic currency for both Norway and Denmark as crude oil prices are usually sold
and bought in USD.

        This paper will conduct an econometric analysis to test the significance of our variables.
The quantitative data combined with economic theory will form the basis for our investigation of
how oil prices affect stock returns. By using a multifactor market regression model, previous used
by (Faff and Chan, 1998; Faff and Brailsford, 1999). The two-factor (Equation 1) and the three-
factor (Equation 1) regression model are used to estimate if the oil price return has an effect on
stock returns in both Norway and Denmark. The three-factor model includes an exchange rate
variable that has the purpose to capture some of the hedging effect some of the companies may
use, but also to determine if the model is mis-specified in our equation 1. It should show if the
exchange rate is significant given our significant levels that have been used.

        Consider the two-factor model in equation 111: An oil risk factor in domestic currency


Ri t  i   Rm t   i OI LR( NOK ) t  ei                     t



 Ri t  i   Rm t   i OI LR( DKK ) t  ei                    t



Rit = Monthly return of the industri.

Rmt = Monthly return of the market index measured in RI.

OILR(NOK) = Monthly return in crude oil expressed in Norwegian kroner.

OILR(DKK) = Monthly return in crude oil expressed in Danish kroner.

eit = error term in the regression model.

Where α is constant and e is the usual error term.12



10
   Real interest rate = Nominal Interest rate – Inflation rate
11
   Faff, R. W., & Brailsford, T. J. (1999, p. 76).
12
   Note that in the absence of the variables, the equation reduces to the well-known random walk.




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                                 The oil price influence on the stock market

                 - An industrial perspective for Norway and Denmark between 1990-2010

            We substitute equation 1 into another equation because we believe that the oil risk factor
also carries an exchange rate risk, which Faff and Brailsford (1999) did in their analysis, but also to
see if equation 1 may be mis-specified and therefore the wrong model to use. We calculate an oil
factor in USD converted to either NOK or DKK by using an exchange rate factor. Below is shown
how an oil price factor in NOK and DKK has been converted from being in USD:

OILR(NOK)t            = In ((OIL(USD)t * XR(NOK/USD)t-1))/(OIL(USD)t-1 * XR(NOK/USD)t)

                      = In (OIL(USD)t)/OIL(USD)t-1) + In (XR(USD/NOK)t- 1)/XR(USD/NOK)t-1)

                      = OILR(USD)t + In (XR(USD/NOK)t)/XR(USD/NOK)t-1)

OILR(DKK)t            = In ((OIL(USD)t * XR(DKK/USD)t-1))/(OIL(USD)t-1 * XR(DKK/USD)t)

                      = In (OIL(USD)t)/OIL(USD)t-1) + In (XR(USD/DKK)t- 1)/XR(USD/DKK)t-1)

                      = OILR(USD)t + In (XR(USD/DKK)t)/XR(USD/DKK)t-1)

where XR(USD/NOK)t and XR (USD/DKK)t is the exchange rate at t, which means that 1 USD
expressed in Norwegian Kroner and Danish Kroner, and OILR(USD)t is the oil price return in month t
expressed in USD.

            As mention that an oil price denominated in USD also carries an exchange risk, the paper
uses a second regression model that separates the oil price and the exchange rate, but also to see
if the model is in equation 1 is mis-speicifed given the fact that the exchange rate may have more
explanatory power above the oil price. The exchange rate has been calculated in the following way:

XRt = In (XR(NOK/USD)t) / (XR(NOK/USD)t-1)

Equation 213: An oil risk factor in USD with an exchange rate variable in either USD/NOK or
USD/DKK.


 Ri t  i  Rm t   i OI LRUS D) t  XR  ei
                              (                                               t


Rit = Monthly return of the industry.




13
     Faff, R. W., & Brailsford, T. J. (1999, p. 77).


                                                                                                       14
                              The oil price influence on the stock market

                - An industrial perspective for Norway and Denmark between 1990-2010

Rmt = Monthly return of the market index measured in RI.

OILR(USD) = Monthly return in crude oil in USD

XR = Exchange rate between USD/NOK or USD/DKK.

eit = error term in the regression model.

           The monthly returns in Rit, Rmt OILR(USD) and XR, have been calculating by using the LN. It
is worth mentioning that the coefficient of OILR(USD) () and XR () only will be equal if the
exchange rate has absolutely no influence on the oil returns denominated in USD. If the
coefficients are not equal, equation 1 is mis-specified, because the exchange rate has a stronger
explanatory power above the oil price expressed in USD in explaining monthly stock returns.
Furthermore, the paper uses a Wald-test to check for mis-specification in a hypothesis testing
form, and a second Wald-test14 to test if both the oil price factor and the exchange rate have a
jointly significant impact on the monthly stock returns in the given industry.

           As mention earlier in the methodology section, the paper includes an exchange rate
variable in our regression analysis due to the fact that most of the oil prices are determined in USD,
and not NOK or DKK. An oil factor could be either more or less significant when using an own
variable in the regression analysis. We therefore believe that the relationship between stock return
and exchange rate could be either positive or negative depending of the country is either a net
importer or net exporter of crude oil. The exchange rate can either increase or decrease the
competitiveness of the domestic countries depending if they either are net importer or net export
of crude oil. This can of course damage stock prices (returns) in the different industries that are
trading globally.

2.4.1 - Additional calculations
Unit root
           A “unit root” is found in time series data whose autoregressive parameter is equal to 1. In
order to use OLS on the stochastic process15, we must make sure that the process is stationary, i.e.
has no unit root. If the process is non-stationary, the OLS will probably produce invalid estimates,




     Wald-test 1: H0 =  = , Walt test 2: H0 =  =  = 0
14

15
     Stochastic process is also called a “random process”


                                                                                                      15
                                        The oil price influence on the stock market

                - An industrial perspective for Norway and Denmark between 1990-2010

and therefore makes the estimates worthless. To avoid the unit root problem, the data have used
first difference data, because unit root problem in level data in time-series is common.

Newey-West estimate
        When using time series data in the regression analysis, we do not want autocorrelation of
the residuals because it causes several problems for our estimates. First of all it violates one of the
main assumptions (#4) that the error terms are uncorrelated. By running the regression with the
error terms correlated, the model would risk being biased. If OLS coefficient estimates are biased,
the standard errors tend to be overestimated.16 There are several tests to ensure this, however the
choice fell on the Newey-West HAC standard errors. Compared to White’s test, which assumes that
the residuals are serially uncorrelated, the Newey-West estimator is more consistent in the present
of heteroskedasticity and autocorrelation.17

Durbin-Watson stat
        We have chosen to use the Durbin-Watson test statistic to detect the possibility of
autocorrelation in the residuals from the regression model. Due to the assumptions made above,
normality and that the dependent variable is not in a lagged form, we find that there is reason to
believe that we can use the Durbin-Watson stat as an appropriate test rather than the Breush-
Godfrey test. If the DW-stat falls below 2, there is sign of positive serial correlation. If the statistic is
between 2 and 4, there is sign of negative correlation.

The DW-stat is based on the OLS residuals:18

          n     ^          ^
         (u        t    u t 1 ) 2                                                                ^
DW      t 2
                                       , by using simple algebra, we get approximately: DW  2(1   )
                    n     ^2
                ut
                t 1



The use of DW-critical values as a two-side probability will test if the correlation in error-term is
correlated with the independent variables.
Wald-test




16
   Eviews user guide 4.0. p. 291-293.
17
   Eviews user guide 4.0. p. 292.
18
   Introductory econometrics (Woolridge, 2009, p. 415)


                                                                                                          16
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

        The Wald-test, described by Polit (1996) and Agresti (1990) is one way to test the
significance of whether the parameters associated to their explanatory variables are equal to zero.
If the parameters are jointly significant, then we can conclude that they shall be in the model or if
not, then they can be omitted. We will use the Wald test in two editions in this paper.

R-squared and the adjusted R-squared
        R-squared is also called the explanatory power of the model and tells us how much of the
variability in the data that is accounted for in the model. The problem we could encounter later in
this paper is that as we add more variables to the model, R-squared will no matter what increase.
In an extreme case we could always get a R-squared of 1, if we included as many variables as there
are observations. That is, R-squared does not consider the amount of variables. However, the
adjusted R-squared does. It penalizes the R-squared for variables that do not contribute to the
explanatory power of the model.19


Random variables
        We can with certainly say that the data used in the report qualifies for being random. After
all, we do not know with certainly what the stock index in either Norway or Denmark will be in the
next trading day. So the outcome of the variables used in this report is not foreknown, and
therefore, they should be viewed as random variables. Furthermore, a time series data set can be
seen as one possible outcome, a realization, of the stochastic process.

2.5 - Research design and testing
        This paper is based on a previous study of the Australian and Canadian equity markets (Faff
& Brailsford, 1999) (Faff & Chan, 1998) (Sadorsky P. , 2001). Many other researchers have used the
same two-factor and three-factor model with a slightly modification. They either included other
variables, or used the same model in different markets. It is proven to be able to explain the
variation in the stock returns. The time period covers the period from 1990:1-2010:3 and includes
more than 242 observations20. We have extended the equation model used by Faff and Brailsford
(1999), Faff and Chan (1998) and Sadorsky (2001), which included the oil price return in domestic
currency, and used a regression model that includes the oil price return in USD and another



19
  Eviews user guide 4.0. p. 277
20
  In our chemical industry for Norway we only had 72 observations included and Hld & Dvlp in Denmark has
only 220 observations. Only Hld & Dvlp for Denmark follows the normal distribution assumption.


                                                                                                      17
                             The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

variable that represents the exchange rate return of USD/NOK and USD/DKK. As a result of this, we
believe that the companies can either hedge the oil price risk or the exchange rate risk, but they do
not hedge both of them since exchange rate has a lower volatility then the oil price, and because
different industries are affected differently.

         The data used are based on monthly data from Norway and Denmark respectively. We
have chosen to use monthly intervals to try to avoid disturbing time differences between the
countries and to account for the fact that the stock market is closed for trading on Saturdays and
Sundays. Previous research has further shown that by using daily data, there is a high proportion
of disturbance. The oil price is measured by the closing price of the spot price. 21 While many
research papers have only focused on one of the oil commodities, we will run the same regression
model with two of the most traded crude oil commodities22 to see if they have a different
significance in relation to the different industries in Norway and Denmark.

         All of the data concerning oil prices, London Brent Crude and WTI, stock returns, and
exchange rates have been collected from Thomson DataStream23 and IEA24. We have used
DataStream’s market index for both Norway and Denmark as our market proxies. The market index
for the Norwegian stock benchmark used today, where not created until before 1996. (Sørensen L.
Q., 2009). As a result of this, DataStream has created their own benchmark, Norway-DS Market
that includes all the shares on the Oslo Stock Exchange from the period 1990:1-2010:3. Instead of
using market proxies in terms of nominal returns as Faff and Brailsford (1999), we have used the
total market return index for both countries, because the index is seen at the most accurate
measure of actual performance than if dividend and distributions were to be ignored.

         OLS25 estimates methods will be used because of the potential small-sample error that may
arise if we were to use other types of method estimates. All of the regression models for each of



21
   The reason for why we have chosen spot price instead of future/forward contracts is because spot prices
should give a better picture of the volatility of the change in the price of crude oil, assuming that it fluctuate
more, and because forward or future prices are more useful in determine were the stock prices are heading
towards.
22
   West Texas Intermediate is traded at the NYMEX and is the most traded physical commodity in the world.
23
   Thomson Datastream is a database that provides a large range of equity indices from many countries and
sectors worldwide.
24
   International Energy Agency.
25
   Ordinary Least Square has the purpose of minimizing the sum of squares of the errors made in solving the
regression equation.


                                                                                                                18
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

the industries in Norway and Denmark have been run through a statistical software program called
Eviews 6. In each regression output, the values have been adjusted for heteroskedasticity and
autocorrelation by using the Newey-West test with a 4-lagged model instead of the White test to
overcome the possible problems in the error terms.




2.6 - Data collection
        This paper uses two of the most traded physical commodities, London Brent Crude and
West Texas Intermediate as the proxy of oil price (return) in our models.

        All of the data have been collected from DataStream, and the category of the industries
has been picked from DataStream’s precedence to match the company’s core business areas with
each other. The data type used as the market proxy is the Norway DS and Denmark DS from
DataStream. The oil prices London Brent Crude and West Texas Intermediate are both expressed in
USD/BBL terms. Sample period is from 1st January 1990 to 1st March, 2010. We are using monthly
frequency to estimate.

        It is expected that the oil price factor and the exchange rate factor both have an impact on
the industries, especially oil and gas industries in Norway and Denmark. To see which industries in
Norway and Denmark that are statistically significant when used in the models, we are testing for
three significance levels: 1 % , 5 % and 10 %. The critical value for a two-tailed test with over 120
degrees of freedom is 2.576, 1.96 and 1.645, respectively.



3. - The Oil market
        Historically, many of the most volatile oil price movements have been as a result of oil
shocks in terms of either supply or demand. On the supply side, many military conflicts, where
most of the world production crude oil is located or crises in the OPEC, has created uncertainty
about future delivery of crude oil. From the demand side, a research paper written by Lars Q.
Sørensen (2009) focused on oil price shocks and stock return predictability, where he states that
the recent year’s exceptional growth in China and India are the main driver of the demand of crude
oil. As result of the large demand, it is believed that these economies are responsible for much of
the recent increase in the oil price. Lars Q. Sørensen‘s (2009) paper also showed that most of the
prediction power of how the changes in oil prices are determined comes from certain episodes,




                                                                                                        19
                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

conflicts or crises in OPEC. The author also concludes that the prediction power has decreased in
the previous years, especially during the financial crises.

        The main characteristic of the oil market is that main supply of crude oil to the world is
concentrated in the Middle East. The countries located in the Middle East, with some other oil
exporting countries, have formed a common producer organisation: The Organisation of the
Petroleum Exporting Countries (OPEC). The Member States of OPEC organise their production
volume, which has historically caused a substantial influence on the development of the oil prices.
(OPEC, 2009)

        The purpose of the foundation of OPEC:

“is to coordinate and unify the petroleum policies of Member Countries and
ensure the stabilization of oil markets in order to secure an efficient, economic
and regular supply of petroleum to consumers, a steady income to producers
and a fair return on capital to those investing in the petroleum industry” (OPEC,
1960)

        Today, OPEC consists of 1226 member countries that controls about two-thirds of the
world’s oil reserves, and stands for more than 40 % of the world’s oil supply (Williams, 1999). Even
although this can be argued against, it is stated that must of the oil reserves in the world are in
South- and Central America, they are just not exploded or extracted (Kovarik, 2006). This
unextracted oil does of course have some influence on the oil price, since it can influence the
supply of crude oil in the future.

        As the number of new member states in the OPEC appears to continue to increase in the
future, one can expect that OPEC will have even more influence on the supply of oil over non-OPEC
members to the world market, and thereby influence how the price of oil is determined by
controlling the production of barrels. According to BP, the largest consumers of oil are not the
countries with the largest reserves. They estimated that 60 % of the world’s proved oil reserves are
contained in just five countries27, all located around the Middle East area. (BP, 2006). And the


26
  OPEC were established by five member states in 1960: Islamic Republic of Iran, Iraq, Kuwait, Saudi Arabia
and Venezuela. Other member states joined the OPEC at different years: Qatar (1961), Indonesia (1962),
Socialist People’s Libyan Arab Jamahiriya (1962), the United Arab Emirates (1967), Algeria (1969), Nigeria
(1971), Ecuador (1973), and Angola (2007)
27
   Saudi Arabia, Iran, Iraq, Kuwait, and the United Arab Emirates.


                                                                                                              20
                            The oil price influence on the stock market

               - An industrial perspective for Norway and Denmark between 1990-2010

largest oil-consuming region, North America, the world’s largest single economy, stands for more
then 30 % of the world oil consumption.

           From figure 1, OPEC has always been the largest producer of crude oil. Since OPEC is the
largest single organised producer, OPEC production of crude oil follows the same output pattern to
the total world production. A research that was conducted by Sharon Xiaowen and Micheal
Tamvakis (2010) where they investigated the effect of OPEC announcements on world oil prices by
examining the announcements from both official conference and meetings. They found no
significant differences between OPEC and non-OPEC members’ crude oil prices in relation to
announcements made by OPEC. One would assume that OPEC had more influences on the oil
prices, given their share of the world supply of oil. They further found that it is in the context and
what type of decision that is made by OPEC that has a significant impact on the oil prices. Especially
decisions that are concerning either a cut or increase in the production. There is therefore a lot of
speculation around each meeting. In another research article made by Guidi, Russell and Tarbert,
they conducted a research where they looked at which effects OPEC’s policy decisions had on both
oil and stock prices in the US and UK in the time period 1986-2004. They made results that showed
that decisions made by OPEC, in terms of production volume and price bands, did have different
impact on the oil price whether it was during a conflict, or non-conflict period. The Persian Gulf
war28, between Iraq and Kuwait had major impact on the world supply of oil by lowering the total
world production of 8.8 %29, but also the WTI Crude oil price and London Brent oil price increased
respectively 54 % and 69.31 % from the start to the peak. This high increase in both the oil prices
reflects that oil shock on the price in terms of risks is associated with production. According to a
research article conducted by James Hamilton (2003), he concluded that in each period a
production volume either increased or decreased as a result of an oil shock, it was compensated
somewhere else to get the oil market back in equilibrium. But as he argues, it depends on how
much production of crude oil that has been affected. This argument can also be seen in the oil
price where it took more then 2 years30 before the oil price when back to before the war.




28
 The Persian Gulf War started in August 1990 and ended in February 1991.
29
 Kuwait’s production of crude of was in July 1990 1,858 million barrels per day while Iraq produced 3,454
million barrels per day.
30
     See figure 9 in the appendix.



                                                                                                            21
                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

         The OPEC meeting in 1999 marked a low point for the oil price before it again started to
rise. A terrorist attack in September 2001 led to a sharp and instant decrease in the oil price, but
increased it later on.

         First, because of the recent economic and financial crises there has been a lower demand
for crude oil from the leading economies in Asia. The Asian countries are big users of petroleum
products, and a lower demand from these countries has led to a lower world demand for crude oil
and therefore reduced the price of oil. OPEC on the other hand, has tried to compensate the lower
prices on the world market by lowering their production output, so they can increase the price of
crude oil. For net exporters of crude oil like Russia and Venezuela, they have experienced lower
government revenue as the results of lower prices. The growth in the Asian consumption of oil was
mainly driven by an improvement of the U.S. economy. But also, larger consumption of oil in the
U.S., and a decline in petroleum inventories also helped increasing the pressure on the oil price
(Williams, 1996-2007).




3.1 – The oil proxies
               The use of different oil types when analysing the impact on a country’s economy is
useful to use the oil type that are extracted from oil fields in that country, if it is a net exporter of
oil. Assuming that oil is a homogenise good, net importers of oil are not affected of what type of
oil, because oil can be refined to fit the current demand, but the transportation of oil from the
producer to the consumer may affect the type oil crude oil they import.

               As listed in our critic of previous literature section, we have used different oil types
as an oil price factor in our models. The London Brent Crude is used because it is extracted in the
North Sea, and therefore relevant to both Norway and Denmark. The second oil proxy used is the
WTI, because it has been used in many previous literatures and proved to be significant at both the
macro level and industry level. Furthermore it is also the most traded physical commodity in the
world, and therefore the most reported oil price in the media.

               There should not be a large different in the price (return) between the two oil types,
given the rule of arbitrage, where we assume that companies or speculators in the financial market
will act as a spread closer between the two if the gap becomes too large, or if a profit opportunity
arise.



                                                                                                            22
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

3.1.1 - Brent crude
        Brent crude (named by Shell UK Exploration and Production) is one of the major crude oils.
The oil originates from several fields in the North Sea and about two thirds of oil traded
internationally is priced relatively to this31. Crude oil is characterised by several indicators. The two
most important is the density, whether or not it contains sulphur and how much. Brent crude’s
density or gravity is 38.3 degrees which make it “light”. It also contains a little sulphur making it
“sweet”. The oil is typically refined in Northern Europe, but is occasionally, when the price is
favourable, exported and refined further south or even in the US. We will often refer to it as
London Brent or just Brent crude.

        Before 1997, Brent crude was priced as straight Brent. But due to different problems it was
substituted for a mixture of Brent, Forties and Oseberg crude. The shift did not change much, and
Brent is still traded as Brent, BFO or Brent Blend.




3.1.2 - West Texas Intermediate
        West Texas Intermediate (WTI) is the most important US crude oil benchmark. WTI crude
density or gravity is around 39.6, which also makes it “light”. At the same time it contains about
0.24% sulphur compared to Brent’s 0.37% making it “sweeter”. It would not make any economical
sense to export the crude oil out of the US for refining, when the domestic conditions are almost
ideal. The Midwest and the Gulf coast are the most active refining regions.32 We will typically use
the abbreviation (WTI) to refer to this oil.




3.1.3 - Difference
        It is clear from figure 9, that London Brent and WTI are closely correlated. In fact we have
shown in table 4 that the exact correlation between the two is 0.898172. We have chosen to use
Brent crude and WTI for several reasons: (1) they are the two most important crude oils which is
used to benchmark almost all others. If there are some connection between the oil price and stock



31
  Financial Times: http://www.ft.com/cms/s/0/04d17bd4-51de-11df-a2a2-00144feab49a.html;
http://www.marketoracle.co.uk/Article874.html
32
  http://www.marketoracle.co.uk/Article874.html;
http://www.eia.doe.gov/oil_gas/petroleum/info_glance/petroleum.html


                                                                                                        23
                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

returns in Norway and Denmark, we find it more plausible that there will be a statistical significant
correlation between the Norwegian/Danish stocks and Brent/WTI, than there will probably be
between the two markets and for example the Maya crude oil (crude oil from Venezuela, Mexico
etc.). (2) Brent crude is extracted from the North Sea and contributes many companies directly and
indirectly in Denmark and Norway respectively. Not to mention the great impact the profit from
the sale of oil contributes to the balance of payments in both countries33.

        There are three obvious differences between the two crude oils: (1) location, (2) quality
and (3) price variation. The fact that Brent and WTI are extracted at different locations is quite
straight forward. WTI is produced in the US and Brent is actually compounded oil from 15 different
fields extracted from the North Sea. 34The second dissimilarity is the quality. WTI is both lighter and
sweeter than Brent, and thereby of better quality and naturally more valuable. This leads us to the
third point. The difference in quality is reflected in the oil price since WTI is generally priced higher
than Brent. However, the price difference is not very large. If we look at figure 9 showing the price
of Brent and WTI crude in the period 1990-2010. we can clearly see how the two crude oils are
moving together. They are almost priced the same, but if we look closely it is obvious that WTI
mostly lies above London Brent. The watchful observer would might ask why investors do not take
advantage of this imbalance and spot a possible opportunity for arbitrage. However, the theory of
arbitrage and rational pricing is outside the scope of this paper.35




3.2 - The Oil Market and the Norwegian Economy
        Oil has been produced on Norwegian territory since 15 June 1971, where the Norwegian
Oil fairytale started at the Ekofisk field. The Ekofild is according to the Norwegian Ministry of
Petroleum and Energy still one of the largest oil producing fields and the oil production is
estimated to continue to 2050. Recently there has been an increase in investment in developing
the Ekofisk and other Norwegian oil producing fields to increase the oil extraction.


33
 Danish Energy Agency: http://www.ens.dk/documents/netboghandel%20-%20publikationer/olie-
%20og%20gasressourcer/2009/html/dogp8/html/kap07.htm




35
  Pindyck, Robert S., The present value model of rational commodity pricing, The Economic Journal, Vol. 103,
no. 418, May 1993, p. 511-530.


                                                                                                          24
                             The oil price influence on the stock market

               - An industrial perspective for Norway and Denmark between 1990-2010

           Petroleum activities have contributed to significant economic growth36 in Norway. The
petroleum industry in Norway have existed for over 30 years, and have created value of over NOK
5000 billion, which equals in today’s dollar value of approximately $830 million37. According to the
Norwegian Ministry of Petroleum and Energy, this is 18 times the total value creation of the
primary industries38. (Ministry of Petroleum and Energy, 2007)

           The oil and petroleum industry is a major in the Norwegian economy, and therefore has an
impact on the Norwegian society. According to figure 2, Norway produce more than they actually
consume, and as a result of this exceed production, Norway export most of the crude oil. Norway is
according to Statistic Norway39 and the Norwegian Ministry of Petroleum and Energy the 5th
largest crude oil exporter, and the 10th largest oil producer in the world. Norway produced on
average 2.8 million barrels of crude oil per day in the time period 1990-2008. Although this is a high
number, Norway only produces in the same time frame on average 3.77 % of the world production
of crude oil. Numbers from Statistics Norway showed that production of crude oil declined in 2009
as a result of lower prices and lower demand for crude oil. This of course had impact on the
Norwegian labour market since the oil and petroleum industry accounts for most of the value in
GDP40 in Norway.

            As one of the largest net exporters41 of oil, Norway has an interest in what decisions OPEC
make, since it can influence the oil price. Even though OPEC supports the oil exporting countries,
Norway has never wanted to join the common oil producing union, but is instead member of the
IEA who support the oil importing countries’ interest in the oil market. Still, both IEA and OPEC
have the common interests in keeping stability and predictability in the oil market. Steady and high
prices are to prefer, since it makes it profitable to invest in new oil production capacity. However,




36
   The petroleum activities are also largely financing the Norwegian welfare state.
37
   Exchange rate USD/NOK = 6,011. Source: DataStream
38
   Primary industry consists of extraction and collection of natural resource, as copper and timber, as well as
farming and fishing.
39
   Statistics Norway is a department that produces statistics on important aspects of the Norwegian society
and economy.
40
   In 2006, the petroleum sector stood for 26 % of the value added in GDP. Norwegian Ministry of Petroleum
and Energy. (n.d.). Oil and Gas. Retrieved April 1, 2010 from The Norwegian Ministry of Petroleum and
Energy: http://www.regjeringen.no/en/dep/oed/Subject/Oil-and-Gas.html?id=1003
41
     Net exporter measures a country’s total value export minus the total value of import.



                                                                                                             25
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

too high an oil price can also put a brake on the world economy growth, so it is a tight-rope
walking.

        It is expected that the production of crude oil in Norway will decrease in the coming years,
as the extraction of natural gas is gradually taking over as the most demanded source of energy.
(Ministry of Petroleum and Energy, 2007)




3.3 - The Oil Market and the Danish Economy


3.3.1 - The quest for oil
        The Danish quest for oil began in 1935, where the American F.F. Rawlin got concession to
drill in the Danish underground for occurrence of oil. However, no oil was apparently found even
though drillings from 1935 to about 1950 showed the right potential.

        The breakthrough came in 1962 where A.P. Moller (in co-operation with Gulf and Shell) got
monopoly to exploit all Danish oil and natural gas occurrences. They started drilling at once and
already in 1963 they discovered the first real occurrence of oil in Danish waters. Since then several
oil fields have been discovered including Danfeltet, Gormfeltet, Krakafeltet, Tyrafeltet, Skjoldfeltet
and Halfdanfelt. For this reason Denmark could in 1991 state that it was oil self-sufficient and an oil
net-exporter.

        There is a clear tendency shown in figure 4, which illustrates how the yearly production of
oil has grown. Especially from about 1980 the Danish production has increased dramatically from
100-200.000 cubic meters to over 22.500.000 cubic meter in 2004. The spectacular increase over
these approximately 25 years is primarily due to the continued discovery of new crude oil
occurrences, but also as an expression of how technology has developed. New machinery and new
techniques have made it possible to extract a larger amount of usable oil from the same amount of
sediment.

        It is a fact that Denmark has never extracted as much oil from the North Sea as nowadays.
This can easily be confirmed by looking at figure 4. But even though Denmark produces more than
enough oil to cover its own consumption, it only covers a very small part of the world market.
Approximately about a half % of the aggregate world production. Actually, if the Danish, Norwegian




                                                                                                    26
                            The oil price influence on the stock market

               - An industrial perspective for Norway and Denmark between 1990-2010

and English production from the North Sea is multiplied, it still only covers about 7 % of the
aggregate world production. 42 The main producers still is countries from the Middle East.




3.3.2 - The impact on the Danish economy
          The production of oil has a large impact of the Danish economy. It affects the balance of
trade, the balance of payments and direct and indirectly through taxes from oil companies and
from the jobs the industry creates. From figure 6, we can see how the development of employment
has evolved over the last 40-45 years. The number of people employed in the industry has been
more or less steady through the 90’s but has nevertheless doubled three times in magnitude
compared to the number of employed around 1975. This indicates how the industry has continued
to grow and thereby continued to create more value to the benefit of the Danish economy.

          In figure 5, it is especially interesting to highlight the year 1993. It was the year where the
yearly production for the first time exceeded the yearly consumption, i.e. Denmark was self-
sufficient. At the same time we can see from figure 5 that in the late 1990’s Denmark also became
a net-exporter, i.e. exports more than it imports.

          To understand what impact the oil has on the Danish economy, it is necessary to describe
how oil becomes such a valuable commodity. The value of oil is determined by three factors: (1)
The production; (2) The world market price for crude oil and (3) the exchange rate.

          The size of the production influences the amount of profit that goes to the Danish national
economy. Production prospects are determined between the government and the Danish Energy
Agency for different time periods (typically 3-5 years ahead). So far, production peaked in 2004 and
has decreased since then. Some researchers talk about the Hubbert peak theory43, which state that
for any given geographical area that produces oil, the rate of production tends to follow a bell-
shaped curve. Hubbert states that the increased production in the early stages is due to new
discoveries and the continuously extended infrastructure. However, as time goes by production




42
  http://oliebranchen.dk/da-
DK/Viden/Temaer/Olien%20i%20Danmark/Artikler/Nordsoen%20og%20den%20globale%20olieproduktion.a
spx
43
     E.g. Deffeyes (2003)


                                                                                                        27
                              The oil price influence on the stock market

                - An industrial perspective for Norway and Denmark between 1990-2010

decreases because fewer and fewer new discoveries are made, the current fields are emptied and
no new infrastructure is build.

           The world market determines the price of crude oil from day to day. The two “market
crude” or “benchmark” oils that we are using in this paper are both traded internationally as
commodities. Prices are set by demand and supply, so production has also a role to play here. If
production and thereby supply for some reason suddenly fails to deliver the expected amount of
oil or if the Danish government for some reason unnoticed decides to make a cut in the production,
this action will definitely have an effect of the world market price. To give an example: The US
recently experienced a supply problem concerning the US WTI caused by a bottleneck. The lack of
renewal of infrastructure in the refineries based in Oklahoma (build in the 1920’s) was the reason
that the US at sometimes cannot deliver the needed amount of WTI crude. This incident affected
the price of London Brent44. Another example was during the XX, where OPEC decided to make a
cut in the production. To keep prices steady, several European countries among them Denmark,
decided to sell out some of the oil from the reserves. The markets clearly interact to various
incidents around the world. Even though the incident in the US was not directly related to the oil
from the North Sea, it still affected the price. The point is that as an oil producing country,
Denmark can and must react to the markets.

           The exchange rate has great influence on the value of oil. Since all benchmark crude oils
are traded in USD, it is interesting to follow how the exchange rate fluctuates. According to the
Danish Energy Agency, the average price per barrel Brent oil in 2006 was 65.1 USD. In 2007 the
average price per barrel was increased to 72.5 USD or what correspond to an over 10 % increase.
Seen from figure 9, the price per barrel has clearly continued this tendency except from the large
drop in late 2008 caused by the financial crisis. A higher oil price is typically a benefit for the Danish
economy. The oil is worth more thereby affecting the balance of payments in a positive direction.
But it is important to remember that the increase in the dollar, not necessarily cause a
corresponding increase in DKK. Figure 7 illustrates the oil price expressed in USD and Euro per
barrel respectively. From about 2003 to 2008, we can see that the spread between the two
currencies have widened. And again from 2009 and onwards, the gap has increased again.
Nonetheless, the increase in US dollar per barrel is not correspondingly reflected in Euro per barrel.
The answer is that while the oil price (in USD) has increased, the US dollar rate has lost value from


44
     http://www.cisoilgas.com/article/The-rusty-oil-and-gas-industry-a-tale-of-corrosion/


                                                                                                       28
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

about 6 DKK per USD in 2006 to about 5.4 DKK per USD.45 So the increase in price measured in
Danish kroner has been considerable less significant.

        There are several factors that affect the earnings on oil. It is not only the price itself, but
also the level of production and the exchange rate which counterbalance some of the changes in
the other two. A high oil price is preferred in contrast to a low seen from a national economic point
of view, because it contributes to higher earnings and makes it profitable to develop and build new
infrastructure. There are multiple examples in history where oil has decreased too much, i.e. where
it was not worthwhile to drill due to the heavy fixed costs when extracting oil from the
underground. On the other hand oil can also become too costly. Too high prices will put a brake on
the economic growth and the demand for oil causing prices to drop due to overcapacity.

        There is no doubt that the earnings from oil have positively contributed to the Danish
economy. The direct income comes primarily from corporation tax from the government controlled
oil and gas company DONG and from the Danish Underground Consortium (consisting of A.P.
Moller Maersk, Shell and Texaco).46 Other direct income includes production tax, pipeline tax,
hydrocarbon tax, a special exemption tax and a not inconsiderable share of the total profit (about
20 %).47 In addition the Danish state owns a large block of share in DONG and achieves through
there an indirect income.

        Some have even talked about how the earnings from oil have saved Denmark from a deficit
on the balance of payments.48 The contribution from oil has funded the ever increasing public
spending.49 Denmark has during the last upswing experienced a strong increase in import of
foreign goods. Another tendency which should result in a large deficit but Denmark has not seen
this yet due to the growing production of oil and gas. The problem is (see figure 4) that the
production of oil seems to have peaked. The high earnings are the work of a fairly high world
market price on oil. Unless new discoveries are made in the North Sea or new technology is
introduced to extract more oil from the present fields, the production will keep falling. It is


45

http://193.88.185.141/Graphics/Publikationer/Olie_Gas/danmarks_olie_og_gasproduktion_07/html/kap06.
htm
46
   http://www.business.dk/diverse/olie-og-gas-redder-danmark-fra-underskud
47
   http://www.business.dk/diverse/olie-og-gas-redder-danmark-fra-underskud
48
   http://www.business.dk/diverse/olie-og-gas-redder-danmark-fra-underskud
49
   http://www.business.dk/diverse/olie-og-gas-redder-danmark-fra-underskud




                                                                                                          29
                            The oil price influence on the stock market

               - An industrial perspective for Norway and Denmark between 1990-2010

therefore in Denmark’s interest to have as high price on oil as possible. We believe that there are
good reasons for this to happen. There are at least two main reasons that speak in favour of an oil
price over 80 USD per barrel. (1) The demand for oil has increased dramatically over the last
decades. Especially from China and India who are going through tremendous changes. The demand
will push the price upwards. (2) Oil is a fossil fuel, which can be found in a limited amount. It is an
extremely expensive and investment heavy business. The production from the North Sea, Russia
and Mexico are decreasing making oil an article of short supply – pushing the price further up. A
third factor, which affects the price, is the increase in trade with futures and other derivatives.
Speculative behaviour has caused the rate to fluctuate quite a bit – even short term.

           With the current price level of oil around 90 USD per barrel, and with the current level of
production, oil contributes with a estimated of about 30-33 billion DKK to the national budget a
year. The Danish energy agency has been calculating with an average price per barrel of 60 USD to
ensure not to overestimate future earnings.50 Denmark’s reserves are too small to really affect the
world market price. Nevertheless, is it an important issue for the Danish government to keep
earning as much as possible on the remaining oil since it has and still is very significant revenue to
the national budget.



4. - The oil price development and volatility 1990:1-2010:3
           We have divided this section into one overall and two sub-analyses of the entire time
period on how London Brent Crude and WTI have developed. According to Lee, Ni and Ratti (1995),
they argue that periods with oil shocks are more likely to have a higher significant impact on the
economy, than periods where prices are more stable. As a result of this statement, we have divided
our analysis into two sub-periods that includes the time period 1990:1-1999:12 and 2000:1-2010:3.

           We live in a world that consumes more than 80 million barrels51 of crude oil each day.
Estimates from the IEAMonthly Oil Market Report52 estimate that the world demand for oil in 2009
was 84.9 million barrels per day, which equals a decline in demand of approximately 1 % (See
appendix 2). In 2010. IEA estimate that the world demand for crude oil will be 86.9 million barrels


50
     http://www.business.dk/diverse/olie-og-gas-redder-danmark-fra-underskud
51
     1 barrel = 159 liter crude oil.




                                                                                                         30
                            The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

per day (Statistics Norway, 2010). The consumption of oil has showed an upward trend, and
suppliers have until now managed to supply this demand by increasing the production of crude oil.
From 1990 and until 2008, the demand for oil has increased with 28 %.

          Overall, the fluctuation in the oil price in the time period 1990-2010 were mainly driven by
wars, conflicts and risk in terms of supply of crude oil. The large fluctuations of the oil price from
the time 2005-2010 were driven by a low dollar value and high consumption from emerging
markets, but also an easier access to invest in oil derivatives in the stock exchange. According to
the National Energy Information Center in Washington DC, US (2008), it is stated that the low and
high price cases in the oil price history reflects a wide band of potential oil price paths, ranging
from 10,07 and 141,753 per barrel using both WTI and London Brent Crude.

          It is important to understand the volatility of an oil price risk in economies that are largely
dependent on the price of crude oil, either as a net export or a net importer of crude oil.
Participants on the financial markets in both Norway and Denmark need to understand the risk
involved in order to optimise their portfolio allocation decisions. A change in the oil price, and
large volatility, may have large impact on the significant effect on the Norwegian and the Danish
economy and financial markets. Park and Ratti (2008) concluded that an increase in the volatility of
the oil prices has a negative impact on the real stock return either contemporaneously or with a lag
of more than one month in 14 of the countries they investigated.

4.1 - Oil price development from 1990:1 – 1999:12
          The price of crude oil hit a spiked in 1990 as a result of lower production and uncertainty
with the Iraqi invasion of Kuwait. After the liberation of Kuwait, there was a decline in the oil price
due to a hope for a more stable supply of crude oil to the world market was reached.

          From the time period 1991-2000. the average price of a barrel of crude oil was $17.93 US,
with a standard deviation of $3.07 US. This time period had of course much less fluctuation, than
the time period from 2005-2010. One of the main reason for the wide fluctuation in the oil price
were that OPEC abandoned its price band in 2005, as a result of spare production capacity due to




53
     WTI had the lowest price of USD 11,7 and a highest price of USD 141,06



                                                                                                         31
                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

war or conflicts in the Middle East area. Other major factors that contributed to the climbing oil
prices in 2005 included a weak dollar and high growth and larger consumption of oil in Asia54.




4.2 - Oil price development from 2000:1 – 2010:3
        From the time period 1990-2010. the oil price reached the highest price of $141.70 per
barrel, and had the lowest price of $10.51. Through this time period the oil price had an average of
$34.63, with a standard deviation of $24.88. This high standard deviation is a result of the large
fluctuation in the oil price from the time period 2005-2010. According to the analyse of the oil price
history made by James L. Williams (1996-2007), the fluctuation in the oil prices is a result of OPEC
over and over again managed to mis-calculate the demand for oil, and therefore supplied either
too much or too little to the world market.

        The development in the oil prices behave very much like any other type of commodities,
with wide fluctuations in prices that are usually determined by demand and supply. But for more
recent years, large fluctuations in the oil price had a higher correlation with risk involved in terms
of supply.



5. - The oil market in relation to the stock market in general
        Raising oil price in the absence of complete substitution effects between the factors of
production, increase the costs of producing goods and providing services. An increase in the oil
price would have an increasing impact on the discount rate that is used in the equity formula to
values stocks. Rising oil prices are often indicating of inflationary pressure, and when some central
banks sets interest rate after inflation targets, the economy will experience higher interest rates
which have the purpose of calming down the economic activity, and stabilise the price level. This
increase in interest rate would decrease the consumer spending, which again, will have an impact
of corporate earnings, and therefore stock prices (return).

        According to a research paper made by Shiu-Sheng Chen (2010) where the author
investigate whether a higher oil price push the stock market into a bear market, he concludes in his


54
   Asian consumption accounted for about 300.000 barrels per day. The Asian consumption declined for the
first time in 1998. Williams , J. L. (1996-2007). Oil Price History and Analysis. Retrieved February 29, 2010
from WTRG Economics: http://www.wtrg.com/prices.htm



                                                                                                            32
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

findings that there is strong and robust evidence that a higher oil price creates a bear market. Even
thought his evidence of the length of the bear market is weak, the overall conclusion still holds.



6. - Industries under consider investigation

6.1 - Norwegian Industries
        The Norwegian Stock Exchange is a fairly small exchange compared to other stock
exchanges, and therefore less extensively studied in previous financial research articles. However,
Gjerde and Saettem (1999) made a paper about how the oil prices affect the Norwegian stock
market where they concluded that an oil price had significant impact on stock returns.

        The industries used in the investigation is based on the criteria from DataStream were each
company is put in an industry that is based on their core business areas.

        The descriptive data from table 5 in the appendix, gives overviews of the each industry in
Norway have performed in the time period 1990:1-2010:3. The average monthly stock returns for
the Norwegian Industries were equal to 0, 7 per cent, and with a standard deviation55 of 9, 57
percent. The highest monthly stock return come from the Bank industries with a 43 per cent, and
the lowest came from the Utility industry with a negative monthly stock return of 61 percent.

        The industry that has performed best by looking at the average monthly return is the
Chemical industry with an average of 2, 3 percent monthly return. Even thought that the Chemical
industry has performed best, it is also the industry that has had the highest risk in terms of
standard deviation of 13, 9 percent. Higher risk is usually associated with higher returns in the
financial markets. The industry that has performed the worst in the time period is Industry
transport with a positive monthly stock return of 0, 3 per cent. The Industry transport lies in the
around the average risk of all the industries., which may indicates that the stock returns have not
performed as good at the overall average of the industries




55
 . Standard deviation is method that is used to measure risks in financial markets.




                                                                                                      33
                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

         Figure 11 shows the development of the total return index56 over Norwegians stocks
return measured each month in the time period 1990-2010.

6.2 - Danish Industries
         Our basis is equation 1 used by Faff and Brailsford. The estimation results are reported for
both London Brent crude and WTI. We have further decided to use an extended edition of
equation 1 called equation 2 (also used by Faff and Brailsford) to test the validity of equation 1.
These estimations will also be for both London Brent and WTI.

         We would in general expect construction, industrials, industry transportation, marine
transportation and oil & gas to be statistical significant and thereby correlated with oil. In addition
we would expect all the above-mentioned industries except oil & gas and goods to be negatively
correlated with oil. Higher oil prices will typically mean higher costs. Oil & gas is expected to be
positively correlated with oil, since it makes sense for companies in this industry to benefit from a
higher oil price. “Goods” is a special case but given that Carlsberg is a part of the industry, we
would also expect goods to be positively correlated.

         DESCRIPTIVE

Results are reported in table 6-17 found in the appendix



7. - Empirical results for Norway
We are investigating 21 different industries in Norway.

         According to the table 8 where we have used the London Brent Crude as a measure of an
oil risk factor denominated in NOK, the average risk in each industry according to the whole market
is the average risk beta (systematic risk) of 0.96, which is quite similar to the risk of the whole
market of 1. The highest risk to the market was the Insurance industry with 1.31, and with the
lowest of 0.59 was Holding and Development Industry.

         When using the London Brent Crude as the oil price factor for the different industries in
either NOK or USD with an exchange rate variable, we see that some industries are more affected




56
  The total return index is an index that calculates the performance of a group of stocks assuming that all
dividends and distributions are reinvested.


                                                                                                              34
                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

by the exchange rate than the oil risk factor. We found that 5 industries57 were significant at the 1
% significant level, 8 industries58 were significant at the 5 % significant level, and at the 10 %
significant level 12 industries59 are significant.

         Our investigation discover when we moved from the oil risk factor denominated in the
domestic currency from Faff and Brailsford (1999) equation, and into a mis-specified equation that
contained an exchange variable, was that the significant on an oil price in USD was more significant
than an oil price in domestic currency. It is especially Int. Oil and Gas, Oil and Gas, Oil and Gas
Production that becomes more significant. It is our belief that a lot of the cash flows from the
companies in these industries are dominated in USD, and not in NOK. The monthly return of the
market index and an oil price factor in NOK is on average able to explain 53 % of the variation in
the monthly stock return for the industries. The regression model was better to explain the
variation in Int. Oil and Gas, Oil and Gas, and Oil and Gas Production industries, while is was worst
explaining the variation in the Utility Industry. When using the mis-specified equation, we see that
the variation explained in the monthly stock return for the industries almost does not change, even
when the fact is the oil price risk becomes more significant60.

         By using a market proxy and an oil price factor either in NOK or USD, we are able to make a
model that are able to explain the variation in the monthly stock return in each industry with an
average of 0,79 percent.

7.1 - Banks
The bank industry that is used in our model consists of the two largest listed banks in Norway, DnB
Nor and Sparebank 1 SR. The bank industry is a subgroup of the financial industry that we have
conducted an investigation on.




WTI as the Oil price proxy




57
   Consumer Staples, Food and Beverage, Food Products, Food Producers, Industrials and Oil and Gas.
58
   The same industries at under significant level 1 %, plus Goods and Service, and Utilities.
59
   The same industries at under significant level 1 % and 5 %, plus Insurance, Int. Oil and Gas, and Oil and Gas
Production.
60
   The Construction and Materials industry was the one with the lowest explaining value of 23 %.


                                                                                                              35
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

        The WTI in NOK as the oil factor from equation 1, the bank industry becomes insignificant
when using an oil price factor expressed in NOK, and as one can see from the table 9. The DW-test
shows some sign of negative correlation in the residual, which may indicate we have omitted a
variable and that we may not have an optimal regression model.

        In the second equation we divided the first equation into two separate variables, one with
an oil factor in USD and the exchange rate. We can see that the exchange rate variables has a
significant impact on the stock returns in the bank industry at a significant level of 10 %, and that
the oil price factor in USD is significant at the 5 and 10 % level. From the first Wald-test we do not
have enough evidence to reject the H0. and therefore the model is not mis-specified because the
exchange rate variable does not have extra explanatory power over the oil price factor in affecting
the stock return of the bank industry. We do not have enough evidence to reject the second Wald-
test either at all of the significant levels, and therefore the oil price factor in USD and the exchange
rate variable jointly do not have a significant impact on the monthly return of the bank industry in
Norway.




London Brent Crude as the oil price proxy

        The bank industry shows no sign of significance to an oil price risk when using the LBC
instead of the WTI. It seems from both of the regression models that the bank industry is not
significant to an oil price risk return in NOK. But something changes when we are using the second
equation. Now, an oil price risk factor in USD becomes significant at both the 5 % and the 10 %
level, and is close to become significant at the 1 %. The coefficient is negative, which implies that
the stock return for the bank industry in Norway is negative affected by an increase in the monthly
return of the oil price when using LBC. The exchange rate is still significant at 10 % when using
either the WTI or the LBC.

        From the Wald test, we do not have enough evidence to reject the H0. which implies that
the exchange rate variables does not bring further explanatory power to the monthly stock return
of the bank industry. But, we can reject the H0 at the 10 % level in the second Wald-test, which
means that both the exchange rate and the oil price risk in USD jointly does have an impact on the
monthly stock return on the bank industry in Norway.




                                                                                                        36
                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

Conclusion

         It is difficult to give a good explanation that the bank industry in Norway is significant to an
oil price in USD using the LBC, and not the WTI. One conclusion that may be draw is that most of
the lenders that the Norwegian banks lend to are perhaps either only selling oil as LBC, or uses the
LBC oil in their production process. We do not believe that the bank is directly influenced by the oil
price, but that their customers are affected by it, and therefore the bank industry is indirect
affected by it. Our argument can be seen in the light that Norway is a net exporter of oil, and that
both DnB Nor and Sparebank 1 SR Bank is highly exposed to the Norwegian market.




7.2 - Chemicals
         The chemical industry in Norway, as mention in the basic material industry section, is a
sub-industry of the basic material industry. It consists of Yara International as the only listed
company in this industry61.




WTI and LBC as the oil price proxy

         The values that we get from the tables 8 and 9 are not statistically significant to the
monthly return of the Chemical industry. There also seems to be a negative correlation in the
error-term using the critical values form the DW-test in all of the outputs. The reason could be that
we are either lacking data, or we are omitting a variable in our model. This assumption can be
supported given the fact that in our first Wald-test we have enough evidence to reject the H0 and
state that the equation1 is mis-specified because the exchange rate has a stronger explanatory
variable then the oil price factor for the oil price in USD and an exchange rate between NOK/US are
not equal.

         The exchange rate in the second equation using LBC shows sign of significant in 10 % level,
but form the first Wald test, we do not have enough evidence to reject the H0. which means that
the exchange rate does not bring extra explanatory power to the monthly stock return of the


61
  The outputs of the regression models reflect how Yara is influenced by the oil price factor in either NOK or
USD, therefore not the whole industry. We only had 74 observations from Yara International, and according
to our assumptions form the methodology part, the output is not BLUE.


                                                                                                            37
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

chemical industry when using the LBC as market proxy. And for further notice, we cannot reject
the H0 in the second Wald test either, which implies that neither the oil price in USD using the LBC
and the exchange rate does not jointly affects the stock return in this industry.

        It seems from the results that the first equation is mis-specified using WTI as the oil price
proxy in NOK, and not the LBC. There we believe using LBC as an oil price factor for the chemical
industry suits better. But we must state that the model is not 100 % accurate, therefore our
assumption may be groundless.




7.3 - Consumer Staples
        The industry consists of Aker, Austevoll Seafood, Cermaq, Lerøy Seafood Group, Marine
Harvest, Orkla and Salmar.




WTI as oil price proxy

        Using the WTI in NOK returns from equation 1, we can see that the oil price factor
becomes significant at level 5 % and 10 %. Even though we have enough prove to state that an oil
price factor return in NOK has an impact on the stock return, the oil price factor is insignificant at
the strongest significant level 1 %. When using equation 2, results show that an oil price in USD is
also significant at both level 5 % and 10 %, but the exchange rate factor is insignificant. This results
is also confirmed by the second Wald test, which indicates that we cannot reject the HO and state
that the variables does jointly influence the stock return for the consumer staples industry.




LBC as oil price proxy

        The difference between the WTI and LBC from using equation 1 is that the LBC in NOK is
more significant then the WTI, which indicates that an increase in the monthly return of the LBC in
terms of NOK will decrease the monthly stock return of the Consumer staples. From equation 2,
we find that the monthly return of the LBC in USD is significant in all of the significant levels and
monthly stock return of the consumer staples industry reacts negative to an increase in the oil
price risk return, while it is insignificant to an exchange rate risk. We do not have enough evidence



                                                                                                         38
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

to conclude that the exchange rate brings more explanatory power over the oil price in LBC
according to the first Wald test, but we have enough evidence to reject the HO in the second Wald
test, and can therefore state that the both the exchange rate and the oil price risk in LBC are jointly
affecting the monthly stock return for the Consumer Staple industry.




Conclusion

        It seems that the Consumer Staple industry is both stronger and more affected by an oil
price factor using LBC instead of WTI. In none of the outputs is the model mis-specified according
to the H0s in the first Wald-test. There seems to be evidence suggesting that the LBC has a stronger
influence on the monthly stock return of the consumer staples industry than the WTI.




7.4 - Financials
        The financial industry used consist of the listed companies DnB Nor, Norwegian Property,
Olav Thon Eiendom, Sparebank 1 SR Bank, and Storebrand.




WTI as the oil price proxy

        The oil price factor in NOK is insignificant to the monthly return. When using equation 2,
we can see from our results that an oil price factor in USD is significant at all levels, and that the
exchange rate variable is only significant at 5 % and 10 % level. The first Wald-test shows us that
we can reject the H0 at a 10 % significant level, which may indicate that the exchange rate variable
offer additional explanatory information to the stock return above the oil price return in USD. From
our second Wald-test, there is also enough evidence to reject the H0. but at a stronger 1 %
significance level, which means that both of the oil price factor in USD and the exchange rate
jointly affects the stock return in this industry.




LBC as the oil price proxy




                                                                                                         39
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

         As with the WTI, the LBC in NOK is also insignificant to the monthly return of the stock
return of the financial industry in Norway. But as with WTI, the LBC return in USD is also significant
to all of the levels, and we can estimate that the LBC is 1 % point higher than the WTI, which means
that the LBC has a higher negative impact on the monthly stock return for the Financial Industry in
Norway than the WTI. The exchange rate is still significant at the 5 and 10 %. As with the Wald-test
under the LBC as in the WTI equation, we cannot reject the H0 in the first Wald test, which implies
that the explanatory power of the exchange rate is not stronger than oil price factor. Therefore we
may conclude that the first equation 1 is not mis-specified. Under the Wald test 2, for the LBC, we
cannot reject the H0. as with the WTI, that both the oil price risk in either WTI or LBC, and the
exchange rate does not jointly affect the stock return of the financial industry.




Conclusion

         As with the bank industry, we have the belief that the financial industry in Norway is
affected by both an oil price factor in USD because many of their customers may be affected by the
oil price factor.




7.5 - Food and Beverage


WTI as the oil price proxy

         This industry seems to be significant to an oil price factor return in NOK at both the 5 %
and 10 % significance level. And from our equation 2, estimates show that an oil price return in
USD is significant at the same levels, and that the exchange rate variable is insignificant at all of the
levels. From Wald test 2 there is enough evidence to reject H0 at significance levels 5 and 10.and
therefore we can say that that one of the variables have a significant impact on the stock return in
the Food and beverage returns, in this case, the oil price factor in USD.




LBC as the oil price proxy




                                                                                                      40
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

        It seems that the monthly stock return Food and Beverage industry in Norway is more
significant to an oil price risk in NOK using the LBC instead of the WTI. Even though the impact is
almost the same, the LBC in NOK is more significant to the changes in the monthly return of the
stock price. From our results it also seems as that the oil price changes in dollars by using LBC are
more significant than using WTI in the second equation. The exchange rate is still not significant to
the monthly return of the stock price for the Food and Beverage Industry. As with the LBC, we
cannot reject the H0 in that the exchange rate brings a higher explanatory power over the LBC risk
factor. But we have enough evidence to reject the fact that the H0 in the second Wald test, which
implies that the exchange rate and the oil price return for LBC jointly affects the stock returns for
the Food and Beverage industry.




Conclusion

        Food and beverages is an industry we did not expect to find a significant oil price factor in.
It is hard to exactly pinpoint where the connection is and it does not seem logical to believe that
the effect is directly. The most plausible explanation is that the industry is affected indirectly either
through its demand (i.e. through its customers) or supply-side (i.e. through increased expenses).
The results also show that the industry is more sensitive towards LBC than WTI.




7.6 - Food Products


WTI as the oil price proxy

        The food products industry is significant at all of our levels in equation 1, which indicates
that an oil price risk factor in NOK has a negative impact on the stock return, which can be
interpreted as a 1 % increase in the oil price in NOK, will decrease the stock return in Food products
industry with 0.11 %. When using the second equation we can see that an oil price in USD is also
significant at all levels, The main difference is that the changes in the oil price in USD has a stronger
negative impact on the stock return that under oil price changes in NOK. The exchange rate
variables are insignificant in our findings. From Wald test 1, we do not have enough evidence to
reject the H0, which mean that the exchange rate variable does not bring extra explanatory



                                                                                                        41
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

variation to the stock return for the food products industry. But from Wald test 2, results show that
there is enough evidence to reject the H0 and therefore state that the one of the variables does
jointly affect the stock return – oil price in USD.




LBC as the oil price proxy

        As with the WTI, the LBC is also significant at all the levels in equation 1, but still the LBC
has a 2 point higher percentage than the WTI, which means that the monthly oil price risk in LBC
has a stronger impact on the monthly stock return for the food products industry than the WTI in
NOK. The results for equation 2 show that the oil price factor using LBC as a proxy has the same
significance level as the WTI (1 %). And again the negative impact is larger using the LBC than WTI.
The exchange rate is insignificant in the regression, and therefore has no impact on the monthly
stock return for food products industry. It can further be concluded that H0 (Wald-test 1) cannot be
rejected and so does not bring any excess explanatory power into the model. However, the second
Wald-test shows a more significant p-value than for WTI and thereby passes at the 1 % level. This
again implies that the oil price factor and the exchange rate have a significant impact on the
monthly return for the stocks in Food products.




Conclusion

        The estimates generated for Food products confirm one of our main assumptions, that the
LBC is more significant than WTI. Both oils are significant, but with a slightly advantage to LBC. As
with food and beverages, it is reasonable to believe that the industry is indirectly. The negative
impact could indicate that the oil affects the companies’ cost-side. A higher oil price will then
increase cost, thereby lowering costs and through that profit.




7.7 - Food Producers


WTI as the oil price proxy




                                                                                                          42
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

        As with the food products industry, the food producer industry is also significant at both
the levels 5 and 10 %. The industry’s stock return reacts negatively on the monthly oil price return
in NOK. Again, as with the food product industry, the food producer industry’s monthly stock
return reacts negative to an oil price in USD at the 5 and 10 % significant level. With indicates that
a 1 % increase in the oil price in USD would decrease the monthly stock return in the food producer
industry with 0.11 %. From the Wald test 1, we cannot reject the HO, which implies that the
exchange rate does not bring extra explanatory explanation to the monthly stock return of the
food producers industry. While, we have enough evidence to reject the H0 in the second Wald test,
which again, implies that one of the variables affects the stock return – the oil price in USD.




LBC as the oil price proxy

        The oil price risk using the LBC in NOK is significant at all the levels, even at the 1 %
significant level, which the WTI in NOK is not. It is especially the oil price factor in USD that are
significant to the stock return for the Food Producers industry. Both the models shows that a
increase in the monthly return of the LBC, either in NOK or USD, has a negative impact on the
monthly stock return of the food producers. The exchange rate is insignificant to the monthly stock
return for the industry. Which can be confirmed by the first Wald test, because we do not have
enough evidence to reject the H0, which means that the exchange rate does not bring extra
explanatory power to the stock returns, and therefore equation 1 is not mis-specified. But we have
enough evidence to reject the H0 in the second Wald test, which implies that both the oil price in
LBC in USD and the exchange rate affects the stock return for the food producers industry.




Conclusion

        The conclusion for Food producers is much like the one given for Food products. The oil
factor is significant for both LBC and WTI and has a negative effect. Whether or not the industry is
directly affected is again hard to explain. Nonetheless, results show that there is a negative
relationship, which could very well be caused by the same reason as Food products – through a
higher cost level, when oil prices increase.




                                                                                                        43
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

7.8 - Good and Service
The goods industry: Aker, Austevoll Seafood, Cermaq, Ekornes, Lerøy Seafood Group, Marine
Harvest, Orkla and Salmar.

The service industry: Norwegian Air Shuttle and Schibsted.




WTI as the Oil price proxy

        When using equation 1 the oil price factor in NOK has a significant impact on the stock
returns of the industry62. The goods and service industry has a significant impact when using the
significant levels 5% and 10%, but are insignificant on the 1% level, which may indicate that the
stock returns in the goods and service industry is not that affected by changes in the oil price
expressed in Norwegian kroner.

        Equation 2 where, we use both an oil factor in USD and a exchange rate factor, the oil price
in USD instead of NOK becomes insignificant at all of the levels, and the exchange risk becomes
significant at all of our significant levels. This may indicate that the stock returns in the goods and
service industry are more affected by an exchange rate risk, than by an oil price risk in USD. From
the first Wald test from table 13, we have enough evidence to reject the H0 and state that the
exchange rate have a higher explanatory power over the oil price factor, and therefore the model
used in equation 1 is mis-specified. On the other hand, we believe that the model used does not
fully explain the risk involved in the goods and service industry. From the second Wald-test we
also have enough evidence to reject the H0 and therefore state that both the oil price factor in USD
and an exchange rate variable affects the monthly stock return of the industry.




LBC as the oil price proxy

        Using the London Brent crude as the oil price factor instead of the WTI, the Goods and
industry becomes less significant to the to an oil price risk. Even though both the oil price factor in
NOK has a negative impact, the impact from WTI seems to be stronger.


62
 The goods industry: Aker, Austevoll Seafood, Cermaq, Ekornes, Lerøy Seafood Group, Marine Harvest, Orkla
and Salmar.
The service industry: Norwegian Air Shuttle and Schibsted.



                                                                                                      44
                          The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

        The first equation seems to be mis-specified according to our first Wald-test, since the p-
value is low enough for us to conclude that we have enough evidence to reject the H0 and
therefore state that the exchange rate variable bring more explanatory power to the monthly
changes in the stock return. The exchange rate variable seems to be significant at all levels, while
the oil price risk in USD is insignificant. We also have enough evidence to reject the H0 under the
second Wald test, which implies that both the oil price risk in USD and the exchange rate have a
significant impact on the stock return for the Goods and service industry.




Conclusion

        It seems from our results that the good and service industry in Norway is insignificant to an
oil price factor in USD, and is more affected by an exchange rate. The result may not come as a big
surprise given the fact that not any of listed companies are largely affected by changes in the oil
price. These companies are large companies that export there goods to abroad market were the
exchange rate could change their earnings. The only listed company in this industry that may be
largely affected by the changes in the oil price is Norwegian Air Shuttle, which is a large commercial
airline company where the largest input in there service is fuel. According to their own financial
statement in 200863, they are hedging a large portion of their fuel consumption in terms of forward
contracts.

        Even though both variables in equation 2 have an affect on the stock return of the industry
according to both of the Wald-tests, they both conclude that the equation 1 is not a good way to
explain the oil price factor in the monthly stock return of the industry. The reason is found in the
first Wald-test that conclude that the exchange rate have a stronger explanatory power over the oil
price factor.




7.9 - Industry Transportation
The industry consists of Ocean Groupe, Stolt Nielsen and Wilhs. Wilhelmsen.


63
   The Norwegian Air Shuttle (2008), THIRD QUARTER REPORT 2008,
http://www.norwegian.com/Global/english/aboutnorwegian/IR/doc/interimreports/2008/Q3_08_Report.pd
f.



                                                                                                       45
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010



WTI as the oil price proxy

        The stock return of the industry transport industry seems to be insignificant to an oil price
factor in NOK in equation 1. The Durbin-Watson test indicates that there may be a negative
correlation in the error term that may be better of explaining the monthly stock return for industry
transport. For equation 2, the estimates are not significant at either the oil price in USD or an
exchange rate variable. Both the Wald tests are insignificant at all levels.




LBC as the oil price proxy

        The Industry Transport industry also seems to be insignificant to an oil price risk factor in
LBC as with the WTI. The oil price using the LBC is also insignificant in to the monthly returns of the
industry transport stocks. However, when using equation 2, the exchange rate suddenly becomes
significant at the 10 % level. Both the Wald tests insignificant are insignificant.




Conclusion

        It is very limited what can be concluded from the estimates for Industry transportation.
Almost all variables return insignificant estimates, which makes it impossible to state anything on
the given statistical material. The only tendency, even though it is limited, is that the exchange rate
is somehow correlated with industry.




7.10 - Industrials
        The industry consists of Golden Ocean Group, Kongsberg gruppen, Stolt-Nielsen, Tomra
systems, Veidekke and Wilhs.Wilhelmsen.




WTI as the oil price proxy




                                                                                                        46
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

        This industry is significant at all of the significant levels when it comes to an oil price risk in
NOK. The model is able to explain 62% of the variation in the monthly stock return. In our second
equation 2 an oil price factor in USD seems to be significant at the 10 % level, while the exchange
rate variable is significant at both the 5 % and 10 % levels. This may indicate that the variables does
not affect the stock returns much, but does have some explanatory power that affects the monthly
stock return of this industry. From the Wald tests, we have enough evidence to reject both of the
H0s. Furthermore it can be conluded that there is evidence that supports that the exchange rate
bring more explanatory variation to the stock return then the oil price, and therefore the first
equation is mis-specified. From the second Wald test, we have enough evidence to reject H0 and
state that both the oil price risk in USD and an exchange rate is not jointly equal to zero, which
indicates that both of the variables affects the monthly stock return of the industrials industry.




LBC as the oil price proxy

        Using the oil price factor in LBC instead of the WTI, we get the same conclusions: both are
significant at all of the levels. But is seems that this industry are more negatively affected by WTI
than by LBC. An estimated increase in the oil price of LBC would decrease the monthly return of the
industrials stock returns of 0.11 %, while it would decrease the monthly return of the industry
using the WTI with 0.13 %. It seems like the difference between the two oils lies in the fact that the
price factor LBC is significant at both the 5 and 10 % level, while WTI is only significant at the 10 %
level. On the other hand, in the equation 2 under the LBC, the exchange rate in insignificant at all
levels, while it is significant at both 5 and 10 % level for the WTI output.

The Wald-test show evidence that enable us to reject both null hypotheses.




Conclusion

        It is statistically proven that both WTI and LBC have an impact on the industry. However, it
is necessary to use the estimates with care since both Wald-tests for both models (equation 1 and
2) returned enough evidence to reject the null. That is, it can be concluded that the regression
model for OILR(NOK) WTI and OILR(NOK) LBC are mis-specified. The results corresponds with our
predictions that an oil intensive industry like industrials is negatively related with oil.




                                                                                                         47
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010



7.11 - Insurance
        The only company in this sector is Storebrand.




WTI as the oil price proxy

        From equation 1 we can see that an oil price factor in NOK is significant at the 10 % level.
From equation 2 the estimates confirm that an oil price risk in USD is significant at all levels, which
indicates that an oil price risk has a negative impact on the stock return for the Insurance industry.
A 1 % increase in the oil price return decreases the monthly return of the Insurance industry with
0.22 %. From the Wald test, the first test does not get enough evidence to reject the H0, which
indicates that the exchange rate does not bring extra explanatory information. From the second
Wald test, we reject the H0 because of sufficient evidence of significance within all levels. It means
that the oil price in USD and the exchange rate jointly affects the monthly stock return.




LBC as the oil price proxy

        The insurance industry is only significant at the 10 % level using the LBC as the oil price risk
factor, while it seems to be significant at all the levels under the second equation. It implies that a
higher oil price return would decrease the monthly stock return of the industry. We do not have
enough evidence to reject the first Wald test, which from the first indicates that the model is not
mis-specified. And from the second Wald test, we have enough evidence to reject the H0 and state
that both the oil price risk in USD from LBC and the exchange rate, have a jointly effect on the stock
price return of the insurance industry in Norway.




Conclusion

        All four models using WTI and LBC returns significant values confirming the correlation
between the insurance industry and the oil factor. It is in line with our predictions. It is normal
procedure that insurance companies invest a large part of their cash so they get exposed to oil
indirectly through investments in the equity markets. Since the industry is negatively correlated it



                                                                                                       48
                             The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

suggests that a primary part of their investments could be in sectors, which have this sort of
behaviour towards oil.




7.12 - Int. Oil and Gas
        Statoil is the only company listed in this industry.




WTI as the oil price proxy

        The international oil and gas industry is only significant to an oil price factor in NOK at the
10 % significant level. We are able to explain about 71 % of the variation in the monthly stock
return. An oil price factor in USD (equation2) is significant at all of the significant levels, which
indicates that a 1 % increase in the monthly return of oil price in USD will increase the monthly
return of the Int. Oil and Gas industry of 0.13 %. The exchange rate variable is only significant at the
10 % level, and from the Wald test the estimate show that we cannot reject the H0. Again, it can be
stated that the exchange rate does not bring extra explanatory measure to the monthly stock
return of the Int. Oil and Gas industry. We cannot reject the H0 at all of the significant levels, with
indicates that one of the variables affects the monthly stock return of the Int. Oil and Gas industry,
which is the case of the oil price return in USD.




LBC as the oil price proxy

        The use of LBC as an oil price factor in NOK is only significant at 10 % level, which indicates
a positive impact on the monthly return of the stock price in the Int. Oil and Gas industry. The same
as with WTI. The industry is significant to an oil price risk in USD using the LBC at all the levels,
wihth indicates that the oil price risk has a positive impact on the stock return for this industry. It is
the same as under the WTI – no changes in the impact. The exchange rate is only significant at the
10 % level, same as under WTI.

Under the first Wald test we do not have enough evidence to reject the H0. Therefore the model is
not mis-specified. Under the second Wald test, we have enough evidence at the all the significant




                                                                                                        49
                             The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

levels to reject the H0 and therefore state that that both the oil price in LBC in USD and the
exchange rate is jointly have an impact the industry’s stock returns.




Conclusion

          The results reveal that the oil price factor in NOK is not as sensitive as oil in USD in relation
to the industry. It does not come as a surprise, since the oil commodities are priced in USD. It is
further proven that LBC and WTI affect the industry positively, which also confirms our predictions.
A higher oil price will typically benefit the given companies’ earnings.




7.13 - Marine Transportation
          The industry is formed by Golden Ocean Groupe, Stolt Nielsen and Wilhs. Wilhelmsen.




WTI as the oil price proxy

          The marine transportation sector is insignificant to the oil risk factor in NOK at all of the
significant levels. There is some sign of negative correlation in the error term according to the DW-
test. From the second equation, we can see that neither the oil price risk factor in USD or the
exchange rate are significant at both the 1 and 5 % levels, but the exchange rate is significant at the
10 % level. From the Wald test 1, we can reject the H0 and therefore with enough evidence at the
10 % level conclude that the exchange rate explain more than the oil price risk factor in USD on the
monthly stock return for the marine transportation industry. The second Wald test is insignificant,
wish means that the either the oil price risk in USD or the exchange rate affects the monthly stock
return.




LBC as the oil price proxy

          Seems to be insignificant to an oil price risk in LBC in NOK. Further in equation 2 is seems to
be only significant to an exchange rate return at the 10 % level.




                                                                                                          50
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

According to the first Wald test, the industry we do have enough evidence to reject the H0 at the 10
% level, which means that the model is mis-specified. From the second Wald test we do not have
enough prove to reject the H0.




Conclusion

        The oil factor does not have an impact on the marine transportartion industry using WTI,
which is explained by the first Wald-test. It indicates that the model is mis-specified. For that
reason, it is difficult to draw any conclusions for WTI. For LBC there seems to be a small
relationship between the oil factor and the industry, but it is weak and only passes the 10 % level.
Furthermore, the first Wald-test rejects, as for WTI, the null hypothesis, and thereby suggest that
the model is mis-specified.

        It comes as a little surprise that the all the oil variables in our models return none or a very
weak sign of correlation. Marine transportation is typically an oil intensive industry with large costs
contributed to fuel, but we must assume that the three companies listed has somehow managed
to hedge themselves against the risk.




7.14 - Oil and Gas Production
        The industry consists of DNO International, Norwegian Energy and Statoil.




WTI as the oil price proxy

        The oil and Gas Production is insignificant to an oil price risk in NOK. From equation 2 we
can see that an oil price in USD are significant at all of the levels, and the industry reacts positive to
a increase in the monthly return of the oil price change. With a 1 % increase in the monthly oil
price changes, increase the monthly stock return of oil and gas production with 0.14 %. The
exchange rate is only significant at the 10 % level. Indicating that an exchange rate changes does
not have so much affect on the monthly return of the stock return for the oil and gas industry.
Because of the both the oil price and exchange rate are significant, we cannot reject the H0 under
our second Wald test, which means that both the oil price risk in USD and an exchange rate



                                                                                                       51
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

variable affects the monthly return of the stock return in the Oil and Gas Production. We do not
have enough evidence to reject the null in the first Wald, which mean we cannot conclude that the
exchange rate give more explanatory power to the monthly changes in stock price. The equation is
therefore not mis-specified.




LBC as the oil price proxy

        The oil and gas production industry is only significant to an oil price factor using the LBC as
proxy in NOK at the 10 % level, while the WTI is not significant at any of the levels. From the
second equation the industry is significant at all of the levels when using the LBC returns in USD. It
seems to have the same impact as the WTI in USD. The exchange rate is only significant at the 10
% level. We do not have enough evidence from the Wald 1 to conclude that the exchange rate has
a higher explanatory power above the oil price return in USD (LBC). From Wald test 2 we do have
enough evidence to reject H0 and to conclude that jointly the oil price factor using LBC and the
exchange rate have a effect on the stock return of the industry.




Conclusion

The results confirm our predictions. Even though WTI is not significant in NOK, it becomes strongly
significant in USD. At the same time, the statistical material suggests a positive correlation. LBC is
significant in both models indicating that the industry is more exposed towards LBC. This also
confirms our expectations. It is natural that a higher oil price will affect the industry positively since
it will boost earnings.




7.15 - Oil and Gas
        Companies: Acergy, Aker Solutions, Bonheur, BW Offshore, DNO International, DOF,
Farstad shipping, Fred Olsen Energy, Frontline, Ganger Rolf, Norwegian Energy Co, Petroleum Geo
Services, Prosafe, Prosafe Production Pub, Renewable energy, Seadrill, Sevan Marine, Solstad
Offshore, Statoil, Subsea 7 and TGS-Nopec Geophs.




                                                                                                         52
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

WTI as the oil price proxy

        The oil and gas industry are significant to all of the levels in the first equation, which means
that an oil price risk in NOK has a significant power to the monthly return of the stock in this
industry. The oil price risk in NOK has a positive impact on the monthly stock return, which means
that an increase of 1 % in the change of the monthly return of an oil price risk in NOK will increase
the monthly return of the stock return of 0.11 % for the Oil and gas industry. As with the previous
industry, the oil and gas industry are significant affected by the monthly return of the a change in
oil price in USD. The difference is that this industry is more affected by a change in oil price than
the oil and gas production industry. A 1 % increase in the oil price factor in USD is estimated to
increase the monthly return of 0.14 %. The exchange rate factor is only significant at the 10 %
level. From the Wald test, we do not have enough evidence to reject the H0 under the first test. It
implies that the exchange rate does not bring more explanatory power to the stock return for the
industry. Under the second Wald test, we have enough evidence to reject the H0 and therefore
conclude the both the exchange rate and the oil price in USD have a affect on the monthly stock
return for the Oil and Gas industry.




LBC as the oil price proxy

        The industry is significant to an oil price risk in NOK by using the LBC at all of the levels.
There is a positive relation between them. The industry is significant at all of the levels using the
LBC in USD, and is only significant at the 10 % level under the exchange rate. According to the first
Wald test the exchange rate does not bring extra explanatory power above the oil price return in
USD using the LBC, and therefore the model in equation 1 is not mis-specified. We have enough
evidence to reject the second Wald test, which implies that both the oil price in LBC in USD and the
exchange jointly have a significant impact on the stock return for this industry.




Conclusion

We expected that this industry would show a highly significant relationship towards both types of
crude and the estimates confirm this. The models also seem to be correctly specified. It is actually a




                                                                                                         53
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

bit surprising that the impact is not larger – percentwise, since the companies earnings must be
highly dependent on the price of oil.




7.16 – Utilities
        Hafslund is the only listed company.




WTI as the oil price proxy

        The utility industry is insignificant to an oil price risk factor in NOK. The regression model is
not more than able to explain 22 % of the changes in the monthly return of the Utility industry. The
Utility industry seems to be significant to an oil price change in USD at the 5 and 10 % levels, but
insignificant at the must significant 1 % level. The exchange rate variables is insignificant at all of
the levels, which means we do not have enough evidence to reject the H0 in the first Wald test and
therefore conclude that the exchange rate does not bring extra explanatory power to the monthly
stock return of the industry.




LBC as the oil price proxy

The industry is significance at the 5 and 10 % level when using LBC return in NOK. Estimates from
equation 2 show that Utilities is also significant for all significance levels using the oil price factor in
USD. The exchange rate variables, however, is insignificant. It is further found that we cannot reject
the null in the first Wald test, but there is evidence enough to reject the null in the second at both
the 5 and 10 % level.




Conclusion

Even though we cannot reject the null hypothesis in the frist Wald-test, the R-squared still indicate
that the model is not optimal. This is further confirmed by the fact that WTI is insignificant in NOK
and only significant at the 5 and 10 % level in USD. Again LBC shows sign of being more significant
in the model and thereby more correlated to the industry.



                                                                                                          54
                             The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

8. - Empirical results for Denmark
        Industries with a relatively high proportion of their costs devoted to oil-based inputs, such
as Transport, are expected to react negative in the sensitivity report. On the other hand, the in the
absence of offsetting effects, we could expect a positive return from the industries operating in oil
and oil-related industries, in which oil directly impacts the revenue side of the income statement.
(Faff & Brailsford, 1999)




8.1 - Banks
        The sector contains 9 different banks where Danske Bank, Jyske Bank and Sydbank are the
largest. Danske Bank is in fact Denmark’s largest bank. Even though the banking sector is not
directly involved in any oil activities, it is obvious that most of the banks will have some kind of
assets bound to oil.




WTI as the oil price proxy

When we use WTI as the proxy in equation 1, we find that the industry is statistically significant
within the 10 % level (very close to the 5 %). The coefficient is -0.06, which means that “banks” is
negatively correlated with oil. So a 1 % increase in the oil price makes the stock return of “banks”
decrease by approximately 0.06 %. The DW-stat is a bit below 2 showing a sign of positive serial
correlation. It could indicate an omitted variable or that the model is somehow mis-specified.

In the second equation, we see that “Banks” suddenly becomes insignificant for oil, but significant
within the 10 % level for the exchange rate. From the first Wald test, we cannot reject H0. even
though the p-value is very close of being significant at the 10 % (0.1096). This indicates that the
model is not mis-specified. Furthermore we do not have enough evidence to reject the null for the
second Wald-test. That is, the oil price price and the exchange rate do not have a jointly significant
impact on the return from “banks”.




London Brent Crude as the oil proxy




                                                                                                       55
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

The t-statistic for LBC is clearly insignificant in equation 1. The DW-stat is again a bit below 2, which
could indicate some sort of positive serial correlation in the residuals. From equation 2 using LBC as
the oil proxy, we see the same pattern as for WTI. The oil factor is insignificant while the exchange
rate is significant at the 10 % level.

From the first Wald-test the p-value is significant at the 10 % level, telling us that the model is
somewhat mis-specified and that the exchange rate has excess explanatory power over the oil
price factor. We cannot reject the null hypothesis in the second Wald-test.




Conclusion

        We predicted that banks and oil would have no correlation. Nonetheless, “banks” seems to
be another example of an industry with no obvious connection to oil, but with some kind of
sensitivity anyway. When using LBC as the proxy, it becomes insignificant in both equations, while
the exchange rate is significant in equation 2. On top of that the first Wald-test tells us to reject the
null, so we conclude that the model is at least mis-specified when it comes to LBC. Equation 1 is
significant for oil, but only at the 10 % level, and when we split the oil factor up into two variables
in equation 2, the exchange rate is the only variable significant. This makes us conclude that the
exchange rate has some kind of impact or disturbance, rather than oil. It is reasonable to believe if
we remember that the first Wald-test was also very close to rejecting the null.




8.2 - Construction and materials
The industry consists of FL Smidth & Company and Rockwool. We expect some kind of sensitivity
towards oil, since higher oil prices typically will boost economy and thereby construction.

WTI as the oil price proxy

In equation 1 using WTI as the oil price factor, we find that it is insignificant. The Durbin-Watson
statistic is close to 1.5, which indicates a very strong sign of positive serial correlation. So already
here it is doubtful that we can use the estimate for anything. The standard errors of the estimates
may underestimate the true value of the errors, which mean that the t-values may be incorrect.




                                                                                                           56
                             The oil price influence on the stock market

               - An industrial perspective for Norway and Denmark between 1990-2010

Equation 7 tells us that oil is highly significant and within the 1 % level. The coefficient is negative,
so there is a negative correlation between the oil price and the return in Construction and
materials. The exchange rate is insignificant.

In the first Wald-test we cannot reject H0. but we can reject it in the second at the 5 % level. So the
oil price factor in USD and the exchange rate variable has a jointly impact.




London Brent Crude as the oil proxy

Construction and materials is also significant for oil within the 1 % level in equation 1. The
coefficient is like predicted positive. At the same time the DW-stat is approximately around 2,
which indicates no autocorrelation.

Moving to equation 7, we find that oil is significant again at the 5 % level and that the exchange
rate factor is insignificant. However, the coefficient is still positive compared to WTI. The first
Wald-test we keep the null hypothesis, but we reject it at the second. Again the two variables has a
jointly significance onto the industry.




Conclusion

           As mentioned above, it is hard to draw conclusions when the DW-stat indicates so strong a
sign of autocorrelation when using WTI as the proxy. Nevertheless, there is a clear pattern when
using London Brent. The construction business is in general a very exposed industry when it comes
to fluctuations in the economic growth. In times of prosperity the businesses often thrive. A higher
oil price will stimulate higher demand and thereby create a “spill-over” effect onto the industry. 64
In periods of recession the businesses often struggles for survival, so the companies tend to be
highly cyclical.65

           The firms in this industry are FL Smidth and Rockwool. An explanation to the estimators
can be that both FLS and Rockwool’s customers are affected by fluctuations in the oil price. A


64
     Fionia Bank, 3. Quarterly announcement, November 20. 2008, page 5




                                                                                                        57
                              The oil price influence on the stock market

                - An industrial perspective for Norway and Denmark between 1990-2010

higher oil price will stimulate higher demand and thereby create a “spill-over” effect onto the
construction and materials industry. The reason why at least FLS is not affected more by the oil
price is that they have outsourced many activities to China and other Asian countries.66 This makes
FLS able to better adapt to shifts in the demand and can be thought of as a indirect way to hedge
itself against unnecessary risk without hurting the core business. Rockwool’s order intake is also
affected of the general condition in construction. Again a higher oil price will stimulate
construction and thereby give Rockwool more to do. However, their exposure to oil should be
quite small since their primary energy source is coke.67




8.3 - Goods and services
The industry consists of Bang & Olufsen, Carlsberg, Danisco, East Asiatic company, IC Companys,
Royal Unibrew and Utd. Intl. Ents.




WTI as the oil price proxy

WTI seems to be insignificant in both equation 1 and 2. The DW-stat sends a mixed signal because
it is a bit below 2 in equation 1 and a bit over 2 in equation 2. Besides that we cannot reject the
null hypothesis in either Wald-test.




London Brent Crude as the oil proxy

We get a completely different result when using London Brent as the proxy. In equation 1, we find
that the oil factor is significant within the 10 % level. The coefficient is positive (0.06), and thereby
positively correlated to oil. Equation 2 returns a similar result. However, oil is now significant at the
5 % level and the coefficient is slightly more positive. As with WTI we cannot reject either null
hypothesis in the Wald-tests.




66
     Fionia Bank, 3. Quarterly announcement, November 20. 2008, page 1
67
     Article: ”Topchef skaber tvivl om Rockwools indtjening”, Tuesday October 16. 2007, Borsen, section 1, page
9.


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                             The oil price influence on the stock market

               - An industrial perspective for Norway and Denmark between 1990-2010

Conclusion

           Since we cannot reject the null in both Wald-tests for both London Brent and WTI, we must
assume that the models used are not mis-specified. It further tells us that the exchange rate
variable in equation 2 with either Brent or WTI does not hold any excess explanatory power over
the oil factor. The industry consists of 7 companies (Bang & Olufsen, Carlsberg, Danisco, East
Asiatic Company, IC Companys, Royal Unibrew and UTD. INTL. ENTS). None of which we would
directly connect to have anything to do with oil except from Carlsberg. One could argue that the
transportation of goods will be affected by rising oil prices. However, since most of these
companies have outsourced the transportation activities, makes it a rather vague argument.
Carlsberg’s dependence of oil is increased substantial after the acquisition of Scottish & Newcastle
whose “gold egg” BBH earns its money from the Russian marked. It means that Carlsberg’s
earnings are dependent on Russia, and Russia is dependent on the price of oil.68 This is probably
the explanation to why goods are significant and positive. Royal Unibrew is not exposed to oil in
the same way as Carlsberg. First of all because they do not have the same activities on the Russian
marked as Carlsberg, and second of all that they have hedged themselves from both the risk of
rising aluminium and oil prices.69




8.4 - Hld & Dvlp
There is only one company in this industry: Jeudan.




WTI as the oil price proxy

The regression model (1) containing WTI as the proxy is significant on oil and with a t-statistic
above 2.576 (3.00) it passes the 1 % level. The coefficient is 0.06 and thereby reveals a positive
relationship. The Durbin-Watson stat is around 2 in equation 1 and a bit lower for equation 2. So
there is none or little evidence suggesting autocorrelation.

In equation 2, we also find that oil is significant even though it is only at the 10 %. The coefficient
also changes from being positive in the first model to being negative in the second. The exchange


68
     http://borsen.dk/investor/nyhed/168623/newsfeeds_rss/
69
     Royal Unibrew stock prospect, page 33.


                                                                                                          59
                            The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

rate is insignificant. We cannot reject the null hypothesis in the Wald-tests suggesting a correct
specified model and no jointly significance.

London Brent Crude as the oil proxy

London Brent is also highly significant in equation 1 within the 1 % level (2.74). The DW-stat is
below 2 indicating some positive correlation. The coefficient is also positive. Equation 2 also
returns a significant t-statistic for oil. However, the exchange rate factor is still insignificant. The
positive coefficient of 0.08 indicates a positive exposure towards oil.

The first Wald-test has a p-value of 0.1242, and even though it is quite close to the 10 % level, we
cannot reject the H0. We can, on the other hand, reject the null hypothesis in the second Wald-test
and thereby state that the oil price factor in USD and the exchange rate variable has a jointly
significance impact on the stock return of Hld & Dvlp.




Conclusion

We had no pre-expectations towards this sector since it is very hard to foresee if the oil has any
effect. However, higher oil prices tend to stimulate economy and thereby leading to higher buying
activity, which leads to higher prices. This assumption is confirmed when we look at the numbers.
Datastream has only listed one company in this industry, Jeudan, making it hard to say anything of
the industry in general. Furthermore, the adjusted R-squared is quite low with both oils which gives
reason to believe that there is some uncertainty of how much information the model can give. On
the other hand, both Wald-tests showed no sign of mis-specification.

          The fact that the data is rather limited in this industry makes it harder for us to explain the
differences. However, we find the significance levels quite different in equation 7. By using Brent,
the oil factor remains significant at the 5 % level, while the WTI is only significant at the 10 % level.
The difference can be a sign of how Jeudan is exposed more to Brent than WTI. It makes good
sense when we know that Jeudan’s main activities are within Scandinavia.70 The exchange rate is
insignificant in both cases and can therefore be ignored.




70
     www.jeudan.dk/om



                                                                                                           60
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

8.5 - Industrial transportation
The industry contains some major players in the transportation business - not only in Denmark, but
also globally. It includes A.P. Moller Maersk, DFDS, DS Norden, DSV and Torm. We expect this
industry to be highly negatively correlated with oil, since higher oil prices will increase the costs.




WTI as the oil price proxy

Equation 1 using WTI as the proxy is surprisingly insignificant. So we cannot interpret anything on
the statistical material. The Durbin-Watson is close to 2, so it indicates no obvious problems
towards serial correlation. Industrial transportation is more significant and closer to the 10 % level
than in the second model, but still insignificant. The p-values show that we cannot reject the null
hypothesis for any of the Wald-tests.




London Brent Crude as the oil proxy

Switching the oil to London Brent in equation 1 tells us that oil is significant at the 10 % level
(1.649). The coefficient is positive (0.06) and the DW-stat is around 2. The t-statistic is also
significant for equation 2 even at the 5 % level. So in the combined variable OILR(DKK) the
exchange rate somehow disturbs the model. This is further confirmed by the exchange rate being
insignificant and the fact that we cannot reject the null in both of the Wald-tests.




Conclusion

        We have predicted the industry to be strongly negative correlated with oil. Increases in the
oil price should influence the costs of fuel, which should account for a considerable part of the
expenses. Nevertheless, the estimates give us quite another picture. The companies included in
the industry are A.P. Moller Maersk, DFDS, DS Norden, DSV and Torm. Four of them transports
primarily by sea while DSV’s core business is transportation by land. A.P. Moller Maersk is often
referred to as the largest Danish company measured in quoted value (in strong competition with
Novo Nordic) and weighs about 30 % of the C20. This combined with their large engagement in the
oil and gas industry as well as their dependence on a favourable world economy (the stock is quite



                                                                                                         61
                          The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

cyclical) makes it a bit strange that the industry is not more significant. The other shipping
companies should also be highly sensitive towards fluctuations in oil.

        The only two explanations that makes sense to why this industry is apparently positively
correlated to our oil return factor is that they have either managed to hedge themselves, transfer
the extra cost to their customers or there is something wrong with our data. It would make sense if
the shipping industry used hedging to protect them by making long-term contracts for delivery and
the cost of fuel.

        The fact that A.P. Moller Maersk, DSV, DS Norden and Torm operates internationally makes
it hard to predict whether London Brent or WTI has the largest impact. When comparing the
results from equation 1 using Brent and equation 1 using WTI, we can see that only equation 1
using Brent is significant. Equation 1 using WTI has a t-statistic of 1.277. It is therefore an obvious
conclusion to think that they are mostly exposed towards London Brent. This result is in coherence
with what we have expected since A.P. Moller Maersk’s oil division drills in the North Sea and
thereby producing Brent. R-squared is quite high, 0.728, but it can be caused by strong correlation
between the market index variable and the industry rather than a strong explanation power from
caused by the oil price factor. The output shows a t-stat of 22.68 for the market index, which
supports the claim.

        Even though our first thought would be that industry transportation would be especially
significant due to A.P. Moller Maersk, there is reason to believe the opposite. A.P. Moller Maersk is
first of all a shipping company. The container business is dominant, and even though the oil
division has made more money the last couple of years, Maersk keeps seeing itself and keep acting
like a shipping company. It is ironic that more attention to the oil division helps stabilize and
further reduces the risk towards oil. The container division will still be exposed, but what the
container business loses due to volatility in oil can be gained in the oil division. A.P. Moller
Maersk’s involvement in many different businesses has made it a very diversified company and a
less cyclical stock. This is probably why we do not see a more significant estimator in this industry.




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                             The oil price influence on the stock market

               - An industrial perspective for Norway and Denmark between 1990-2010

8.6 - Oil and gas
Datastream has categorized Vestas Wind Systems as the only company within Oil and Gas. Our
prediction is that Vestas is not particular affected by oil, and if so there should be a positive
correlation.




WTI as the oil price proxy

We find that “Oil & Gas” is statistically insignificant towards the oil factor in equation 1. However,
the Durbin-Watson stat is very low, indicating a very strong sign of autocorrelation in the residuals,
making the estimates impossible to interpret. The risk of wrong estimates and t-statistics are to
great.

Equation 7is significant at the 5 % level for oil and insignificant for the exchange rate factor. The
Durbin-Watson stat is clearly improved only stating some kind of positive serial correlation, i.e. the
estimates and t-statistic are more reliable in the second equation. The exchange rate factor is also
insignificant here.

In the first Wald-test we cannot reject the null suggesting that there is no mis-specification in the
model. However, we can reject H0 in the second at almost the 5 % level (0.0555). So both the oil
factor in USD and the exchange rate has some influence on “Oil & Gas”.




London Brent Crude as the oil proxy

When we use LBC in equation 1, we get the exact opposite result than with WTI. We find that oil is
significant within the 10 % level with a positive coefficient. So if oil should increase in price, the
return on “Oil & Gas” should also increase. The DW stat shows some sign of serial correlation.

The second equation returns insignificant values for both variables (oil and XR), which is quite
strange. Oil is close to being significant at the 10 % level, and it seems like the exchange rate factor
has disturbed the combined variable. Both Wald-tests does not reject the null hypothesis.




Conclusion



                                                                                                         63
                             The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

        For some reason Datastream has chosen not to include A.P. Moller Maersk in this industry.
For that reason, only Vestas Wind Systems is included which seems a bit odd since their core
business is production of windmills. Nonetheless, there is a connection between oil and Vestas. The
connection is probably that when the oil price increases, customers will look for alternative energy
sources which benefits Vestas’ order intake.




8.7 - Marine Transportation
Marine Transportation contains A.P. Moller Maersk, DFDS, DS Norden and Torm. We believe that
there is a negative correlation between oil and the industry.




WTI as the oil price proxy

The industry is insignificant for the WTI proxy in both equation 1 and 2. It is the same for the
exchange rate factor, and the DW-stat remains around 2. We cannot reject any of the null
hypotheses for Wald-test 1 and 2.




London Brent Crude as the oil proxy

It is more interesting to look at the industry in connection with London Brent. Even though the
statistic for oil is not significant in equation 1, it comes very close to the 10 % level (1.61). If we
further investigate equation2, we find that oil becomes significant at the 10 % level and close to
the 5 % level when we split OILR(DKK) into an oil factor and an exchange rate factor. The DW-stat is
around 2 indicating no sign of serial correlation. The coefficient is 0.09 and thereby positive.

As with WTI, we cannot reject any of the null hypotheses suggesting no mis-specification in the
model and no jointly significance.




Conclusion

        It is surprising that the coefficient is positive in equation 2 (Brent) suggesting a positive
relation between the oil price and the industry. We would have expected the opposite since a



                                                                                                          64
                             The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

higher oil price will have a negative impact on the cost. The explanation is much like the one given
for Industry transportation.

        A.P. Moller Maersk, DFDS, DS Norden and Torm dominate the sector. All of these
companies are widely exposed to the price of oil. However, it is likely that the companies want to
reduce their risk by hedging (e.g. by making long contracts on oil). The exchange rate is insignificant
so we cannot state any effect on marine transportation. The adjusted R-squared is slightly higher,
indicating that equation 7 using Brent, explain a little more than equation 1 using Brent.




8.8 - Real estate
        Datastream has only ranked one company in this sector. The company is Jeudan, which is
exactly the same as in Hld & Dvlp.




8.9 - Telecommunication
        Simcorp is the only company is this sector. We expect no relation between oil and the
industry. Results can however be distressed by the lack of more companies.




WTI as the oil price proxy

For equation 1 using WTI as a proxy, we see that the oil factor is insignificant, nonetheless close to
the 10 % level. The DW-stat is a bit over 2 indicating some sign of serial correlation. Splitting the oil
price factor in DKK up into two variables in equation 2, we find no sign of significance in either the
oil factor or the exchange rate. We cannot either reject the null in the Wald-tests.




London Brent Crude as the oil proxy

The London Brent Crude proxy in equation 1 is also insignificant. However, it becomes significant at
the 10 % level in equation 2 with a negative correlation. So a 1 % increase in oil, will
correspondingly give a decrease of -0.09 % in the return for Telecommunication.


                                                                                                       65
                          The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

Again we cannot reject any of the null hypotheses in our Wald-tests.




Conclusion

        We expected no correlation between oil and the industry. Equation 2 using Brent shows
the opposite. Simcorp is the only company in this industry. It develops financial IT system to large
financial institutions around the world. It would make sense to make an analysis of the industry’s
demand-side dependence, i.e. even though the industry does not use oil in its production process,
its customers might be heavily dependent on oil. However, such an analysis is outside the scope of
this paper. For further analysis on this subject see Gogineni (2009) who investigated the correlation
between the oil and the stock market on an industry level based on a cost-side and a demand-side
dependence model.

        It is hard to find a straight forward explanation to why this industry is correlated with the
oil factor. We believe, without having investigated it ourselves, that the industry could be affected
by oil indirectly through its customers. Another explanation is that Simcorp is somewhat exposed
to oil through the general business cycle. That is the business cycle is affected by movements or
shifts in the macro economic variables like interest rate, GDP, movements in commodities
(including oil) etc. However, we note that we have no statistical evidence to support this and that it
is a rather vague argument.




9. - Hedging oil price and diversify risk
        Given the fact that a lot of funds are invested in shares in both Norway and Denmark, we
believe that the findings of our studies will be of interest for both international as well as domestic
investors and portfolio managers when they are to allocate there investment. According to the
Modern Portfolio Theory, investors want to maximize the returns and minimize the risk by
choosing different assets class, and spread there investment. Investors and portfolio managers can
reduce the oil price risk factor in the industry, which are largely affect by the oil price, by spreading
their investment in different industries so that the volatility decreases. The correlation between
the industries should be close to zero, so that the risk in the portfolio decreases.




                                                                                                        66
                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

        Although investors or portfolio managers can spread the unsystematic risk by spreading
out there investment in different industries or assets class in Norway or Denmark, the systematic
risk can not be diversify. This means that the oil price risk factor to the total market can not be
directly diversified, only by investing in our market that are not correlated with the either Norway
or Denmark. Systematic variance is caused by macroeconomic factors that affect all risky assets.
Both global as well as domestic investors and portfolio mangers in Norway and Denmark may use
different oil or exchange rate derivatives to hedge the risk involved

        Usually hedging the oil price risk and exchange rate risk is something the corporations are
doing by either using financial instruments.71 Of course hedging the exchange rate risk is of course
something investors can do, but usually investors are reducing risk in there portfolio by diversify
there portfolio to include investment in different assets.



10. - Further analysis
        In our bachelor thesis we have not been able to address the following issues we feel could
be able to give explanation to an oil price factor to the monthly stock returns for both the financial
markets in Norway and Denmark. First, it could also be interesting to user other macro variables
like inflation rate and interest rate to see if perhaps the oil price risk factor is better explain in real
terms, and perhaps if the interest rate is to closed connected to the value of the exchange rates.

        We would also like to analyse the how the Norwegian Government Pension Fund affects
the Norwegian stock markets. The reason for this is that the Norwegian government is only
allowed to use 4 % of the fund each year in Norway, while the rest is invested in foreign markets.
When the oil prices increase, the wealth of the fund increases, therefore we believe that the fund
can have both a positive effect and a negative effect on the financial markets in Norway in terms of
higher consumer spending, and a higher inflationary pressure.

        In the section of hedging, we believe there has been a growing activity in using financial
instruments to hedge against an oil price risk as a result of oil prices have became more volatile
then before. It could therefore be interesting to see if there has been an increase in hedging




71
  Financial hedging instruments include alternatives likes forward contracts, swaps, options, and future
contracts.


                                                                                                           67
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

activity, and in the terms of how this has affected the stock returns for the industries in Norway
and Denmark.

        We believe that these issues are subjects for future work, and as we mention in the
introduction, there is a growing interest in the field of how the oil market affects the financial
markets around the world.



11. - Conclusion
        Oil is a globally traded commodity and the price of oil is determined by global oil demand
and global oil supply conditions. Rapidly increasing demand for oil from emerging market
economics like China and India, coupled with oil supply shortages will lead to much higher oil prices
in the future and eventually a substitution away from oil to other alternative energy sources.

        We have now conducted our analysis onto the Danish and Norwegian markets by the use
of equation 1 and 2. With these models we have investigated how the oil price factor affected the
selected industries. In general, we found that it is easier to predict which industries that are
affected by oil in Norway than in Denmark.

        The objective of this paper was to investigate whether or not the same industries across
the two countries are affected in terms of return to an oil price factor. We note that there are
many differences from country to country (see table xx). By difference we mean where the industry
in one country is significant for oil, while the same industry in the second country is insignificant for
oil. Using equation 1 with WTI as the proxy, we find differences between the countries in Banks,
Consumer Staples, Food Producers, Food & Beverages, Food Products, Goods & Services, Hld &
Dvlp, Industrials, Insurance, Oil & Gas and Real Estate. For equation 1 with LBC, we find
Construction & Materials, Consumer Staples, Food Producers, Food & beverages, Food Products,
Hld & Dvlp, Industrials, Industrial Transportation, Insurance and Real Estate to be different
between Denmark and Norway. In comparison we find that 11 out of 18 comparable industries are
different using equation 2 (WTI) and that 14 out of 18 industries return a different result using
equation 2 (Brent). From this evidence we conclude that the oil price factor affects the countries
quite differently in spite of the fact that both countries extract oil from the same fields. However,
the result is not that surprising and actually corresponds to our expectation due to Norway’s larger
engagement in oil. In addition we can also conclude on the statistical evidence that the oil price




                                                                                                      68
                          The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

factor, regardless of oil price proxy used, has an effect on some of the industries stock return in
both countries.

        Our prediction was that oil intensive or industries where a high proportion of their costs
are devoted to oil-based inputs, to be significant towards oil and negatively correlated. Chemicals,
Industrials, Industrial transportation and Marine transportation are expected by be especially
sensitive. From Denmark, equation 1 using WTI, we find that none of the above mentioned sectors
are significant. For equation 1 using Brent (Danish), we only find statistical evidence for exposure
towards oil for Industry transportation, which on top of that returns a positive coefficient. For the
Norwegian industries using equation 1, we discover that only Industrials are significant for both the
WTI and the London Brent proxy. We note that we do not have enough statistical evidence to
conclude a direct connection between oil intensive industries and a significant negative sensitivity
towards oil for neither Denmark nor Norway. The main explanation must be that these industries
are especially aware of the risk coming from oil price fluctuations and has therefore found multiple
ways to bring that risk down. The other prediction was that Oil & Gas will have a positive sensitivity
and actually benefit from a higher oil price. For Denmark (equation 1), we see that Oil & Gas is
insignificant for WTI, but significant within the 10 % level for Brent with a positive coefficient. For
Norway (equation 1) we find that Oil & Gas is highly significant with t-statistics above the 1 % level
for both WTI and LBC. These results speak in favour of a correct prediction for the industry in both
countries.

        Results also show that there is a considerable difference in the sensitivity towards oil
dependent on the type of crude oil used. For the Danish industries using London Brent as the proxy
we find that equation 1 gives us 6 out of 18 industries that are significant within the 10 % level. For
equation 2 still using LBC, we find 7 out of 18 industries to be significant to oil. The results for WTI
are 3 out of 18 and 5 out of 18 for the equation 1 and 2, respectively. So our prediction that
London Brent Crude would be more significant than WTI seems to be true. Furthermore we note
that the exchange rate is significant for 4 industries in Denmark for LBC and for 4 industries using
WTI. In most cases the exchange rate adds explanatory power above oil in the model indicating
mis-specified models (we reject the null hypothesis in the first Wald-test). For the Norwegian
industries using London Brent as the proxy, we find that equation 1 gives us 9 out of 21 industries
that are significant within the 10 % level. For equation 2 (Brent), we find that 12 out of 21
industries are significant. Using WTI as the proxy in equation 1, we find significance towards oil in
10 out of 21 industries, and in equation 2, we find 9 out of 21 to be statistically significant to our oil



                                                                                                       69
                          The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

price factor. That is, we conclude that London Brent also seems to be slightly more significant than
WTI in Norway. Additionally we note that there is some evidence that in the industries where the
exchange rate is significant, we also find reason to believe (significant p-values) that the exchange
rate variable adds explanatory power to the model suggesting mis-specified model.

        Furthermore estimates show that there are some similarities when the specific industries
are compared across the two countries. The tendency is that if an industry is insignificant in
Norway, it is also insignificant in Denmark. Norway is, nonetheless, on the basis of the statistical
evidence the most oil sensitive country among Denmark and Norway.




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            - An industrial perspective for Norway and Denmark between 1990-2010

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                              The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

Appendix




Figure 1: Production of crude oil, Source: DataStream and IEA




                                                                                     74
                                      The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

                                                                               Norway - Total Production and Consumption of Crude Oil

                                                                  3,500


                                                                  3,000




                                       Thousand Barrels Per Day
                                                                  2,500


                                                                  2,000


                                                                  1,500


                                                                  1,000


                                                                    500


                                                                      0
                                                                          90      92     94    96    98     00    02    04       06     08

                                                                                                       Year

                                                                                        Norway - Total Consumption of Crude Oil
                                                                                        Norway - Production of Crude Oil




Figure 2: Norway – Total production and consumption of crude oil, Source: IEA




                                                                                       Norway - Export and Import of Crude Oil

                                                                  3,200

                                                                  2,800

                                                                  2,400
                             Thousand Barrels Per Day




                                                                  2,000

                                                                  1,600

                                                                  1,200

                                                                   800

                                                                   400

                                                                     0
                                                                          90      92     94    96     98    00    02     04       06    08

                                                                                                        Year

                                                                                              Norway - Import of Crude Oil
                                                                                              Norway - Export of Crude Oil




Figure 3: Norway – Export and import of crude oil, Source: IEA




                                                                                                                                             75
                                   The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010


                                                      Crude oil production
                               1,800


                               1,600


                               1,400
                   1000 tons




                               1,200


                               1,000


                                800


                                600


                                400
                                       90   92   94   96   98    00    02    04     06    08

                                                                Year



Figure 4: Denmark - Total Yearly Production in 1000 tons from 1990 to 2010. Source: DataStream
______________________




Figure 5: Denmark - Total Yearly Production and Total Yearly Consumption in TJ from 1986 to 2008. Source: DST
__________________________




                                                                                                                76
                             The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010




Figure 6: Denmark – Total yearly employment/1000 hours from 1966 to 2006, Source: DST
___________________________




                     160

                     140

                     120

                     100

                       80

                       60

                       40

                       20

                        0
                            90    92    94     96    98      00   02     04    06     08

                                                          Year

                                             Oil - USD-BBL        Oil - Euro-BBL




Figure 7: Oil price development in USD and EUR per barrel, 1990-2010. Source: DataStream
___________________________




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              - An industrial perspective for Norway and Denmark between 1990-2010




Figure 8: Denmark - Total Yearly export and import of crude oil from 1993-2007, Source: DST
___________________________




Figure 9: Crude oil price development, Source: DataStream




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                             The oil price influence on the stock market

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Figure 10: Exchange rate development- Source: DataStream




                                                   OSEXBX NORWAY- TOTAL RETURN INDEX 1990-2010
                                           6,000


                                           5,000


                                           4,000
                             Index Value




                                           3,000


                                           2,000


                                           1,000


                                              0
                                                   90   92   94   96   98    00    02   04   06   08

                                                                            Year




Figure 11: Norway – Total return index 1990-2010. Source: DataStream




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                                                       DENMARK-DS Market - TOT RETURN IND
                                         12,000


                                         10,000


                                          8,000
                          Index Value



                                          6,000


                                          4,000


                                          2,000


                                             0
                                                  90   92   94   96   98    00    02   04   06   08

                                                                           Year




Figure 6: Denmark – Total return index 1990-2010. Source: DataStream




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                         The oil price influence on the stock market

           - An industrial perspective for Norway and Denmark between 1990-2010

Appendix 1 – Table 1: Descriptive data from the oil price form the time period 1990-2010.
and from 1991 - 2000


             Data analysed from 1990-2010         Data analysed from 1991-2000

             Mean            34.63440                           17.93009
             Median          23.74000                           18.14000
             Maximum         141.7100                           26.48000
             Minimum         10.51000                           10.51000
             Std. Dev.       24.88699                           3.068454
                                                              Source: DataStream



Appendix 2 – Table 2: The World Consumption and Production of Crude oil from 1990-
2008.


Data are collected from public data from IEA and Statistics Norway. Numbers from 2009 are
estimated figure form IEA.



                    Year                World Supply       World Demand
                    1990                66425,63769        66686,60007
                    1991                66399,10029        67290.69869
                    1992                66563,94143        67483,69276
                    1993                67091,31996        67602,1184
                    1994                68588,09296        68921,85943
                    1995                70271,86871        70132,42183
                    1996                71916,74666        71670.75845
                    1997                74157,59389        73430.88579
                    1998                75654,07128        74066,51506
                    1999                74841,66161        75758,35558
                    2000                77763,56151        76741,28105
                    2001                77679,92651        77468,01238
                    2002                76982,70428        78118,53599
                    2003                79595,27845        79681,1712
                    2004                83111,87949        82456,0272
                    2005                84576,50307        84038,36023
                    2006                84538,25624        85201,66788
                    2007                84407,24411        86138,45788
                    2008                85408,14539        85751,68475
                    2009                                   84900.00000




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   Table 3. Oil price series used
   Short name          Full name        Source             Starting Date     Starting Date
                                                           Series            Used
   West Texas         West Texas        NYMEX              January 2. 1986   January 1. 1990
                      Intermediate
                      crude oil,
                      NYMEX spot
                      U$/BBL
   Brent              London Brent      Intercontinental   November 21.      January 1. 1990
                      Crude Oil Index   exchange (ICE)     1988
                      U$/BBL
Source: DataStream

   Table 4. Basic characteristics of oil price changes
                                    West Texas                   Brent
   Mean (%)                         -0.005587                    0.005556
   Maximum (%)                      0.429359                     0.388087
   Minimum (%)                      -0.304438                    -0.435479
   Std. dev. (%)                    0.097704                     0.099165
   # of observations                243                          243
   Correlations                     West Texas                   Brent
   West Texas                       1                            0.898172
   Brent                            0.898172                     1
Summary results on oil price changes (measured as log returns). Monthly mean returns, maximum
and minimum as %age, monthly standard deviation as %age. Correlations are pair wise
correlations. Source: DataStream.




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Table 4 - Descriptive statistics - Denmark
Industry   Mean         Median       Maximum    Minimum     Std dev    Skewness    Kurtosis
Banks      0.007565 0.012385 0.274777           -0.370058   0.077265   -0.723901   7,379609
Basic      0.003623 0.00247          0.300984   -0.392976   0.091036   -0.221402   5,218677
materials
Chemical 0.003629 0.002468 0.300995             -0.392968   0.091035   -0.221347   5,218667
s
Construct 0.009389 0.00928           0.321512   -0.304663   0.075807   0.098125    6,130325
ion
Consume 0.004531 0.009988 0.179593              -0.370878   0.065476   -0.997449   7,088998
r staples
Financials 0.007275 0.012744 0.179561           -0.261347   0.063557   -0.660114   5,381471
Food       0.004248 0.009553 0.218704           -0.266274   0.065829   -0.521583   5,168051
producer
s
Food and 0.004255 0.008684 0.179588             -0.370884   0.064811   -1,028704   7,277459
beverage
s
Food       0.004321 0.009582 0.218699           -0.266268   0.068049   -0.468138   5,136385
products
Goods      0.007746 0.009569 0.250326           -0.27923    0.082835   -0.368641   4,267725
Hld &      0.003957 0.000000 0.234998           -0.29449    0.055679   -0.582830   9,873103
dvlp
Industrial 0.005218 0.00723          0.245905   -0.339125   0.086082   -0.528902   4,654096
s
Industry   0.007165 0.004911 0.247001           -0.297853   0.088672   -0.274670   4,288096
transport
ation
Insurance 0.00701       0.003621 0.2185         -0.192841   0.063415   0.142700    3,831214
Marine     0.007053 0.005768 0.255245           -0.312669   0.091693   -0.253131   4,155512
transport
ation
Oil and    0.016522 0.018134 0.366553           -0.547346   0.157282   -0.744158   4,269200
gas
Real       0.003953 0.000000 0.23498            -0.294494   0.055678   -0.583048   9,873799
estate
Telecom, 0.005267 0.008586 0.29334              -0.59171    0.097334   -1,260434   9,891476
media, It




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Table 5 - Descriptive statistics – Norway

Descriptive data for industries in Norway
                                            Mean       Median Maximum Minimum Std. Dev. Skewness Kurtosis
Goods and service                           0,004729   0,016121 0,169963 -0,355784 0,084524 -0,9547220 4,777372
Banks                                       0,011101   0,013694 0,431071 -0,420673 0,105326 -0,4336040 7,164873
Basic Materials                             0,008492   0,008873  0,27742 -0,490343 0,090841 -1,0229880 6,950094
Chemicals                                   0,023994   0,031071 0,370275 -0,551638 0,139394 -1,0951120 6,633892
Construction and materials                  0,012433   0,013226 0,270017 -0,379885 0,097104 -0,2910810 4,713461
Consumer Staple                             0,008306   0,016092 0,285111 -0,448245 0,095946 -0,9350470 6,059901
Financials                                  0,007236   0,015813 0,370315 -0,374591 0,094832 -1,0330460 6,904245
Food Producers                              0,008634   0,014881 0,285115 -0,448239 0,098457 -0,9347090 6,066395
Food and beverages                          0,007815   0,015825 0,285112 -0,44825 0,096309 -0,9281440 5,996689
Food Products                               0,007818   0,015827 0,285111 -0,448245 0,096309 -0,9281450 5,996654
Hld & Dvlp                                  0,005775   0,005628 0,388469 -0,367593 0,082495 -0,0286600 6,475078
Industry Transport                           0,00393   0,019007 0,289338 -0,332002 0,09377 -0,4119950 3,822708
Industrials                                 0,005715   0,006327 0,202746 -0,40723 0,092954 -0,9501350 5,315047
Insurance                                   0,000362   0,015023  0,40585 -0,59855 0,135153 -1,4168990 7,741840
Int, Oil and Gas                            0,008136   0,014074 0,190991 -0,273257 0,074914 -0,4218080 3,705870
Marine Transportation                       0,005421   0,005645 0,202745 -0,407227 0,09298 -0,9187780 5,176157
Oil and Gas                                 0,008298   0,015947 0,198163 -0,274414 0,076439 -0,6009230 4,133092
Oil and Gas production                        0,0082   0,013959 0,198166 -0,274425 0,076626 -0,6662810 4,339528
Real Estate                                 0,005771   0,005627  0,38847 -0,367598 0,082495 -0,0286550 6,475211
Telecommunication                           0,010906   0,012171 0,332872 -0,48398 0,102593 -0,3589930 5,105777
Utility                                     0,004367   0,010874 0,317796 -0,612644 0,100745 -1,0709000 8,779681




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Table 6 - Results - Equation 1 (WTI)
Danish industry               α             β (Market      γ (Oil price in   R-       Adjusted   Durbin-
                                            index)         DKK)              squared  R-         Watson
                                                                                      squared    stat
Banks                         -0.000353     1,020112      -0.060023          0.648123 0.642209   1,788647
                              (-1,07432)    (13,80741)*** (-1,962491)**
Basic materials               -0.00516      0.868199      -0.030582          0.347066 0.336092 1,788283
                              (-0.81986)    (8,287254)*** (-0.604327)

Chemicals                     -0.00516      0.868228      -0.030589          0.347115 0.336142 1,788251
                              (-0.81899)    (8,288895)*** (-0.60452)
Construction                  0.001339      0.477900      0.023791           0.258779 0.246322 1,532861
                              (0.282827)    (5,684345)*** (0.653297)
Consumer staples              -0.00521      0.697919      -0.004958          0.372869 0.362329 1,839470
                              (-1,20757)    (8,388366)*** (-0.081531)
Financials                    -0.00395      0.944307      -0.040713          0.701715 0.696702 1,727085
                              (-1,39186)    (14,23311)*** (-1,410735)
Food producers                -0.00248      0.565709      0.024841            0.21532 0.202132 1,930355
                              (-0.55227)    (4,762868)*** (0.337546)
Food and beverages            -0.00521      0.697925      -0.004961          0.372873 0.362333 1,839474
                              (-1,20851)    (8,388494)*** (-0.08158)
Food products                 -0.00248      0.565714      0.024843           0.215325 0.202137 1,930335
                              (-0.55184)    (4,762976)*** (0.337582)
Goods                         -0.00222      1,319167      0.076647           0.778095 0.774366 1,892207
                              (-0.73471)    (17,16238)*** (1,283059)

Hld & dvlp                    0.002246      0.080422       0.061268           0.10205 0.083536 1,918267
                              (0.919388)    (1,351297)     (3,002458)***
Industrials                   0.002491      0.871336      0.00546            0.515166 0.507017 1,648512
                              (0.579793)    (9,896925)*** (0.164489)
Industry transportation       -0.00344      1,405383       0.091821          0.734725 0.730266 1,932047
                              (-0.98487)    (14,8586)***   (1,27729)
Insurance                     -0.00582      0.687700      0.007758           0.278343 0.266214 2,167340
                              (-1,21842)    (6,374802)*** (0.131213)
Marine transportation         -0.00323      1,445353      0.098874           0.711927 0.707085 1,943100
                              (-0.85896)    (13,91313)*** (1,247692)

Oil and gas                   0.081438      1,060708      0.150614           0.379643 0.314342 1,189752
                              (3,505859)*** (4,044087)*** (0.996935)
Real estate                   0.002243      0.080412       0.061194      0.101922 0.083405 1,918400
                              (0.918281)    (1,351261)     (3,000639)***
Telecommucation               0.008438      0.970449      -0.101169          0.399257 0.389160 2,304466
                              (1,695359)*   (8,338392)*** (-1,55760)




                                                                                                  85
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             - An industrial perspective for Norway and Denmark between 1990-2010

Number of Significant
coefficients at 1 %                      1               16                2
Number of Significant
coefficients at 5 %                      1               16                3
Number of Significant
coeffivients at 10%                      2               16                3



This table reports the output from the following regression
model:
Rit = i + Rmt + iOILR(DKK)t + eit


where Rit is the return of the ith industry in the month t, Rmt is the return of the market index in the month , and
OIL(DKK) is the return of the oil price using WTI expressed in Danish kroner.

* Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
*** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)




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Table 7 - Results - equation 1 (Brent)
Danish industry      α                  β (Market             γ (Oil price in        R-         Adjusted R- Durbin-
                                        index)                DKK)                   squared    squared     Watson stat
                          (t-statistic)       (t-statistic)          (t-statistic)
Banks                0.000275           0.978104              0.041564               0.482766     0.478438     1,826728
                     (-0.065256)        (-8,304231)***        (-0.806633)
Basic materials      -0.002388          0.807923              0.032682               0.237019     0.230634     1,798637
                     (-0.389632)        (7,448684)***         (0.662012)
Chemicals            -0.002383          0.807945              0.032690               0.237037     0.230653     1,798653
                     (-0.388715)        (7,449233)***         (0.662203)
Construction         0.003151           0.795662              0.082418               0.347536     0.342076     1,947502
                     (-0.721047)        (-9,425781)***        (-2,67807)***
Consumer staples     -0.000994          0.732193              0.041754               0.379948     0.374759     1,796046
                     (-0.257644)        (7,468626)***         (0.752394)
Financials           0.000924           0.869479              0.016347               0.557928     0.554228     1,704177
                     (0.279793)         (10.75381)***         (0.500421)
Food producers       -0.000121          0.619140              -0.012611              0.261704     0.255525     2,262539
                     (-0.036886)        (8,190068)***         (-0.385559)
Food and beverages   -0.001235          0.725396              0.043929               0.381498     0.376322     1,795582
                     (-0.323099)        (7,405954)***         (0.794404)
Food products        -0.000189          0.645556              -0.020470              0.266269     0.260129     2,283868
                     (-0.05649)         (8,463838)***         (-0.611973)
Goods                -0.002266          1,340345              0.060373               0.789826     0.788067     2,171619
                     (-0.938547)        (25,28422)***         (1,80193)*
Hld & dvlp           0.001200           0.294314              0.088011               0.122005     0.113913     1,849324
                     (0.276049)         (3,343383)***         (2,744106)***
Industrials          -0.003060          1,152500              -0.000628              0.531538     0.527618     1,938677
                     (-0.733255)        (16,47822)***         (-0.017877)
Industry             -0.003151          1,376323              0.067474                0.72842     0.726147     2,061902
transportation
                     (-1,069622)        (-21,62602)***        (1,649054)*
Insurance            0.002754           0.625499              -0.037854              0.289232     0.283284     2,004994
                     (0.761043)         (8,964555)***         (0.861076)
Marine               -0.003467          1,402105              0.070540               0.707506     0.705058     2,033665
transportation
                     (-1,084932)        (20.51997)***         (1,613459)
Oil and gas          0.007169           1,504783              0.155870               0.352809     0.343497     1,784826
                     (0.571221)         (9,412342)***         (1,729594)*
Real estate          0.001197           0.294318              0.088011               0.122011     0.113919     1,849215
                     (0.27525)          (3,343526)***         (2,743711)***
Telecommunication    -0.001847          1,041925              -0.059079              0.340308     0.334788     2,142034

                     (-0.364473)        (10.50672)***         (-1,329682)




                                                                                                               87
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             - An industrial perspective for Norway and Denmark between 1990-2010



Number of                     0                 18                  3
Significant
coefficients at 1 %
Number of                     0                 18                  3
Significant
coefficients at 5 %
Number of                     0                 18                  6
Significant
coeffivients at 10%

This table reports the output from the following regression model:
Rit = i + Rmt + iOILR(DKK)t + eit

where Rit is the return of the ith industry in the month t, Rmt is the return of the market index in the month , and
OIL(DKK) is the return of the oil price using London Brent Crude expressed in Danish kroner.

* Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
*** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)




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Table 8 - Results - Equation 1 (Brent)
Norwegian Industry    α             β - Market       ϒ - Oil price in   R-       Adjusted R-   Durbin-
                                    index            NOK                squared squared        Watson stat
Goods and service     -0.003008     0.997525         -0.067417          0.686177 0.683551      2,147275
                      (-1,028004)   (-17,68766)***   (-1,965876)**
Banks                 0.004021      0.921013         -0.019419          0.384691 0.379521      1,695633
                      (-0.691813)   (-8,619743)***   (-0.390719)
Basic Materials       0.000334      1,009055         -0.012695          0.625036 0.621898      1,701579
                      (-0.073695)   (-9,520768)***   (-0.293805)
Chemicals             0.010613      1,31093          0.049482           0.552504 0.539342      1,535215
                      (-0.726829)   (-4,391758)***   (-0.378353)
Construction and      0.006892      0.647083         0.04343            0.239914 0.233554      2,066142
Materials
                      (-1,29129)    (-7,342046)***   (-0.788443)
Consumer Staples      0.000303      1,056995         -0.10388       0.592527 0.589117          2,120148
                      (-0.084304)   (-15,03592)***   (-2,831977)***
Financials            -0.000625     1,011238         -0.065261          0.560853 0.557178      1,649819
                      (-0.128298)   (-12,60688)***   (-1,630598)
Food and Beverage     -0.000221     1,061089         -0.104193      0.59265      0.589241      2,124793
                      (-0.061167)   (-15,29851)***   (-2,851973)***
Food Products         0.000664      1,078182         -0.13848       0.583024 0.579534          2,131624
                      (-0.180887)   (-14,68115)***   (-3,715767)***
Food Producers        -0.000217     1,06109          -0.104199      0.592649 0.589241          2,124787
                      (-0.060282)   (-15,29867)***   (-2,852133)***
Hld Dvlp              0.001008      0.595503         -0.01544           0.261842 0.255665      2,246518
                      (-0.232042)   (-8,34327)***    (-0.311938)
Industry Transport    -0.00252      1,011287         -0.002949          0.603407 0.600088      1,776401
                      (-0.622788)   (-14,47954)***   (-0.070681)
Industrials           -0.003967     1,053695         -0.11733           0.615011 0.61179       2,215555
                      (-1,123405)   (-17,923)***     (-2,84862)***
Insurance             -0.009446     1,317021         -0.156959          0.462035 0.457533      2,100490
                      (-1,436994)   (-8,419583)***   (-1,861834)*
Int, Oil and Gas      0.000704      0.850047         0.082671           0.717407 0.715043      2,106349
                      (-0.279703)   (-16,69538)***   (-1,882224)*
Marine                -0.002697     0.997638         -0.004025          0.586463 0.583003      1,770763
Transportation
                      (-0.648837)   (-13,98514)***   (-0.092595)
Oil and Gas           0.000388      0.901487         0.092376           0.779381 0.777535      1,870709
Production
                      (-0.151843)   (-17,60162)***   (-1,943044)*
Oil and Gas           -0.0000332    0.930204         0.107218       0.837638 0.836279          1,900087
                      (-0.016344)   (-23,84384)***   (-3,219633)***
Real Estate           0.001004      0.595501         -0.015439          0.261841 0.255664      2,246512
                      (0.231096)    (8,343284)       (-0.311934)
Telecommunication     0.004154      0.817195         0.013834           0.328136 0.322513      1,987650



                                                                                                   89
                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

                        (-0.681526)       (-7,674706)***    (-0.199197)
Utilities               -0.000801         0.705993          -0.099013          0.238666 0.232295       1,985260
                        (-0.132374)       (-5,272403)***    (-2,1013)**


Number of Significant          0                 21                 5
coefficients at 1 %
Number of Significant          0                 21                 8
coefficients at 5 %
Number of Significant          0                 21                12
coeffivients at 10%

This table reports the output from the following regression model:
Rit = i + Rmt + iOILR(NOK)t + eit


where Rit is the return of the ith industry in the month t, Rmt is the return of the market index in the month , and
OIL(NOK) is the return of the oil price using London Brent Crude expressed in Norwegian kroner.

* Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
*** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)




                                                                                                            90
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              - An industrial perspective for Norway and Denmark between 1990-2010


Table 9 - Results – Equation 1 (WTI)
Norwegian Industry α               β - Market index ϒ - Oil price in   R-        Adjusted   Durbin-
                                                    NOK                squared   R-         Watson
                                                                                 squared    stat
Goods and service    -0.003014     1,015613          -0.095109         0.69313   0.69056    2,13333
                                                                       5         7
                     (-1,038986)   (18,39661)***     (-0.034787)***
Banks                0.003944      0.911439          0.006446          0.38432   0.37915    1,70874
                                                                       5         1
                     (0.685508)    (8,623992)***     (0.100387)
Basic Materials      0.000273      1,000472          0.009601          0.62493   0.62179    1,71621
                                                                                 1
                     (0.061039)    (9,709036)***     (0.21354)
Chemicals            0.010572      1,256993          0.092885          0.55570   0.54263    1,53013
                                                                       2         4
                     (0.73159)     (3,961714)***     (0.69737)
Construction and     0.006965      0.649284          0.029479          0.23857   0.23220    2,06993
Materials                                                              4         3
                     (1,304068)    (7,04118)***      (0.533546)
Consumer Staples     0.000176      1,0614430         -0.092799         0.58923   0.58579    2,14066
                                                                       4         6
                     (0.049183)    (14,72862)***     (-2,317671)**
Financials           -0.000716     1,0118530         -0.05330          0.55880   0.55511    1,66476
                                                                       7         5
                     (-0.147941)   (12,84442)***     (-1,114344)
Food and Beverage -0.000346        1,0657820         -0.093614         0.58948   0.58605    2,14439
                                                                       6
                     (-0.096319)   (15,01717)***     (-2,352927)**
Food Products        0.000484      1,0818470         -0.118517         0.57597   0.57242    2,16761
                                                                       1         2
                     (0.131792)    (14,4132)***      (-2,891617)***
Food Producers       -0.000343     1,065784          -0.093619         0.58948   0.58605    2,14439
                                                                       5
                     (-0.095432)   (15,01733)***     (-2,353071)**
Hld Dvlp             0.001097      0.617776          -0.063385         0.26822   0.26210    2,22733
                                                                       8         4
                     (0.250876)    (8,673892)***     (-1,248072)
Industry Transport   -0.00247      1,022156          -0.027286         0.60438   0.60107    1,77339
                                                                       8         8
                     (-0.608193)   (14,31765)***     (-0.640562)
Industrials          -0.004047     1,0712020         -0.13346          0.61956   0.61638    2,20918
                                                                       3
                     (-1,153226)   (18,44676)***     (-3,642424)***
Insurance            -0.009589     1,333408          -0.162399         0.46248   0.45798    2,12236
                                                                       3         5
                     (-1,467143)   (8,596159)***     (-1,762613)*
Int, Oil and Gas     0.000796      0.844749          0.077888          0.71524   0.71286    2,11415
                                                                       5         2
                     (0.32203)     (16,764)***       (1,738219)*




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Marine                -0.002642      1,009694             -0.030865          0.58771     0.58426   1,76770
Transportation                                                               1           1
                      (-0.633898)    (13,92521)***        (-0.699421)
Oil and Gas           0.000503       0.897892             0.081697           0.77501     0.77312   1,88125
Production                                                                   1           9
                      (0.200919)     (18,00784)***        (1,692752)*
Oil and Gas           8,61E-05       0.923363             0.100947           0.83413     0.83274   1,90421
                                                                             3           5
                      (0.04294)      (23,11971)***        (2,937251)***
Real Estate           0.001093       0.617773             -0.063383          0.26822     0.26210   2,22732
                                                                             7           3
                      (0.249935)     (8,673877)***        (-1,248041)
Telecommunication 0.004086           0.799654             0.051249           0.33079     0.32519   1,97950
                                                                             4           4
                      (0.672338)     (7,639765)***        (0.709867)
Utilities             -0.00096       0.702645             -0.071043          0.23279     0.22637   1,98117
                                                                             8           8
                      (-0.158381)    (5,175254)***        (-1,400318)


Number of
Significant
coefficients at 1 %         0                21                   4
Number of
Significant
coefficients at 5 %         0                21                   7
Number of
Significant
coeffivients at 10%         0                21                  10



This table reports the output from the following regression model:
Rit = i + Rmt + iOILR(NOK)t + eit


where Rit is the return of the ith industry in the month t, Rmt is the return of the market
index in the month , and OIL(NOK) is the return of the oil price using WTI expressed in
Norwegian kroner.

* Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
*** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)




                                                                                                             92
                          The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010


Table 10 - Results - equation 2 (WTI)
Danish industry     α                β (Market            γ (Oil price in       δ (XR)             R-        Adjusted Durbin-
                                     index)               USD)                                     squared   R-       Watson
                                                                                                             squared stat

                     (t-statistic)        (t-statistic)         (t-statistic)      (t-statistic)
Banks               0.000132         1,004596             -0.007162             -0.233254          0.488264 0.481813 1,838703
                    (0.032368)       (8,816748)***        (-0.131542)           (-1,774455)*
Basic materials     -0.002082        0.776992             -0.049715             0.186375           0.240278 0.230701 1,782282
                    (-0.335202)      (6,49172)***         (-0.959859)           (1,070046)
Chemicals           -0.002077        0.777019             -0.049713             0.186337           0.240294 0.230718 1,782298
                    (-0.334298)      (6,492212)***        (-0.959867)           (1,069793)
Construction        0.003211         0.781883             -0.096739             -0.062526          0.351096 0.342917 1,951540
                    (0.731170)       (8,75431)***         (-2,619828)***        (-0.459993)
Consumer staples    -0.001135        0.750732             -0.022512             -0.200511          0.386600 0.378868 1,799201
                    (-0.302078)      (7,937983)***        (-0.382327)           (-1,778323)*
Financials          -0.000805        -0.892385            -0.012809             -0.166830          0.562982 0.557473 1,723924
                    (0.248479)       (1,117696)           (0.374973)            (-1,900522)*
Food producers      -0.000205        -0.636026            -0.033344             -0.079156          0.263981 0.254703 2,261462
                    (-0.063523)      (7,734894)***        (0.882386)            (-0.582514)
Food and            -0.001373        -0.743311            -0.025513             -0.200941          0.388361 0.380651 1,798704
beverages
                    (-0.369616)      (7,863063)***        (-0.436268)           (-1,802081)*
Food products       -0.000272        -0.664449            -0.044765             -0.073605          0.269128 0.259915 2,281959
                    (-0.083051)      (8,036267)***        (1,138846)            (-0.511604)
Goods               -0.002040        1,320599             -0.064936             0.063171           0.788820 0.786158 2,213874
                    (-0.846719)      (25,94816)***        (-1,711889)*          (0.654709)
Hld & dvlp          0.001334         0.302800             -0.053557             -0.108974          0.109864 0.097501 1,870530
                    (-0.302494)      (-3,293536)***       (-1,689445)*          (-0.895582)
Industrials         -0.002959        1,124501             -0.040119             0.153390           0.535105 0.529245 1,967128
                    (-0.716697)      (-1,529408)          (-1,075053)           (-1,17254)
Industry            -0.002890        1,355268             -0.069569             0.069417           0.726861 0.723418 2,104175
transportation
                    (-0.988243)      (-22,67739)***       (-1,508933)           (-0.591478)
Insurance           0.002759         0.626367             0.038218              0.063313           0.290313 0.281368 2,006112
                    (-0.767522)      (-8,716867)***       (-0.823604)           (-0.639508)
Marine              -0.003180        1,378936             -0.073003             0.082856           0.705979 0.702273 2,077957
transportation
                  (-1,005033)        (-21,53163)***       (-1,44951)            (-0.65536)
Oil and gas       0.006931           1,457518             -0.217689             -0.082929          0.361363 0.347480 1,778260
                  (-0.557631)        (-9,058116)***       (-2,15705)**          (-0.151471)        0.347480
Real estate       0.001330           0.302823             -0.053531             -0.109063          0.109869 0.097506 1,870430
                  (0.301692)         (3,293877)***        (-1,688594)*          (-0.896303)
Telecommunication -0.002249          1,063982             0.044590              -0.141552          0.338755 0.330420 2,152440
                  (-0.446302)        (-11,50212)***       (-0.767533)           (-0.731098)




                                                                                                                    93
                           The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010



Number of                  0              16                  1               0
Significant
coefficients at 1 %
Number of                  0              16                  2               0
Significant
coefficients at 5 %
Number of                  0              16                  5               4
Significant
coefficients at 10 %

This table reports the output from the following regression model:
Rit = i + Rmt + iOILR(USD)t + XR + eit

where Rit is the return of the ith industry in the month t, Rmt is the return of the market index in the month , and
OIL(USD) is the return of the oil price using WTI expressed in US dollar, and the XR is the exchange rate between
USD/DKK
For the regression model:
* Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
*** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)


Table 11
                                                                          Wald-test 1     Wald-test 2
                                                                          H0: γ = δ       H0: γ = δ = 0
                                                                          (p-value)       (p-value)
                                           Banks                          2,559489        3,158256
                                                                          (0.1096)        (0.2062)
                                           Basic materials                1,653783        1,991308
                                                                          (0.1984)        (0.3695)
                                           Chemicals                      1,653096        1,990690
                                                                          (0.1985)        (0.3696)
                                           Construction                   0.055071        7,564496
                                                                          (0.8145)        (0.0228)**
                                           Consumer staples               1,920094        3,343408
                                                                          (0.1658)        (0.1879)
                                           Financials                     3,689810        3,784382
                                                                          (0.0547)***     (0.1507)
                                           Food producers                 0.562791        0.916697
                                                                          (0.4531)        (0.6323)
                                           Food and beverages             1,910353        3,470042
                                                                          (0.1669)        (0.1764)
                                           Food products                  0.557405        1,348900
                                                                          (0.4553)        (0.5094)
                                           Goods ands services            1,266105        2,951238
                                                                          (0.2605)        (0.2286)



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                               Hld & Dvlp                  0.187565   3,897578
                                                           (0.6650)   (0.1424)
                               Industrials                 1,770974   1,995068
                                                           (0.1833)   (0.3688)
                               Industry transportation     1,023835   2,311002
                                                           (0.3116)   (0.3149)
                               Insurance                   0.051191   1,128681
                                                           (0.8210)   (0.5687)
                               Marine transportation       1,089575   2,156948
                                                           (0.2966)   (0.3401)
                               Oil and gas                 0.051531   5,781729
                                                           (0.8204)   (0.0555)***
                               Real estate                 0.188356   3,895464
                                                           (0.6643)   (0.1426)
                               Telecommunication           0.707644   0.826656
                                                           (0.4002)   (0.6614)
* Coefficient estimate is significantly from zero at the 10
% ( P-value in parentheses)                                 0         0
** Coefficient estimate is significantly from zero at the 5
% ( P-value in parentheses)                                 0         1
*** Coefficient estimate is significantly from zero at the
1 % ( P-value in parentheses)                               1         1




                                                                                    95
                          The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010


Table 12 - Results - Equation 2 (WTI)
Norwegian           α            β - Market    ϒ - Oil price in δ = Exchange   R-       Adjusted Durbin-
Industry                         index         USD              rate USD/NOK   squared R-squared Watson
                                                                                                 stat
Goods and service   -0.0030710 1,005125       -0.048446       0.337651         0.700538 0.696763 2,141693
                    (-1,079536) (18,27592)*** (-1,074336)     (3,056901)***
Banks               0.0041060    0.935863      -0.099456      -0.557243        0.408977 0.401495   1,757647
                    (0.733704)   (9,745562)*** (-1,487093)    (-1,74996)*
Basic Materials     0.0003160    1,008507     -0.026149       -0.195416        0.628692 0.624011   1,738362
                    (0.072235)   (9,53767)*** (-0.648523)     (-1,179143)
Chemicals           0.0110950    1,252904      -0.065697      -0.764856        0.571723 0.552547   1,528939
                    (0.775926)   (4,240428)*** (-0.425098)    (-1,736453)*
Construction and    0.0070040    0.656414      -0.002245      -0.194367        0.241167 0.231602   2,074058
Materials
                    (1,313348)   (7,186339)*** (-0.03338)     (-0.885417)
Consumer Staples    0.0002000    1,065900      -0.112634     -0.010294         0.590272 0.585107   2,133831
                    (0.055669)   (14,85619)*** (-2,124992)** (-0.05285)
Financials          -0.0006010 1,033131       -0.147977      -0.438799         0.583017 0.57776    1,680288
                    (-0.126483) (13,14734)*** (-3,309355)*** (-2,522536)**
Food and Beverage -0.0003240 1,069964       -0.112222     -0.003108            0.590393 0.585229   2,137386
                  (-0.089511) (15,12989)*** (-2,115525)** (-0.015985)
Food Products       0.0005110    1,086859      -0.140815     0.002618          0.577216 0.571887   2,158093
                    (0.138307)   (14,61431)*** (-2,57871)*** 0.013494
Food Producers      -0.0003200   1,069966      -0.112227     -0.003094         0.590392 0.585229   2,137382
                    -0.088631    (15,13001)*** (-2,115601)** -0.015912
Hld Dvlp            0.0010600    0.610903      -0.032808      0.222313         0.271564 0.262382   2,204693
                    0.238284     (8,279492)*** (-0.628119)    1,054121
Industry Transport -0.0025140 1,014117       0.008487         0.213218         0.607986 0.603044   1,789433
                   (-0.618946) (14,02201)*** (0.152164)       (1,518435)
Industrials         -0.0040920 1,062989       -0.096918       0.323392         0.623252 0.618503   2,199948
                    (-1,172172) (18,46181)*** (-1,778544)*    (2,263447)**
Insurance           -0.0095180   1,346559      -0.220914      -0.141743        0.467036 0.460318   2,127213
                    (-1,458232) (8,500602)*** (-2,779074)*** (-0.524253)
Int, Oil and Gas    0.0007300    0.832530      0.132259      0.204712          0.728039 0.724611   2,195541
                    (0.312232)   (18,03149)*** (3,302508)*** (1,844618)*
Marine              -0.0026920   1,00049       0.010089       0.243734         0.592424 0.587286   1,787735
Transportation
                    (-0.646781) (13,64241)*** (0.177743)      (1,687606)*
Oil and Gas         0.0004350    0.885423      0.137174       0.206651         0.787805 0.78513    1,984531
Production
                    (0.187007)   (18,94876)*** (3,336424)*** (1,824313)*
Oil and Gas         0.0000308    0.913126      0.146494      0.135793          0.842715 0.840733   2,000854
                    (0.016282)   (24,46523)*** (4,302638)*** (1,813128)*
Real Estate         0.0010560    0.610900     -0.032803       0.222324         0.271564 0.262382   2,204685
                    (0.237358)   (8,27947)*** (-0.628029)     (1,054175)




                                                                                                   96
                             The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010

Telecommunication 0.0040980         0.801975      0.040924            -0.104913         0.33104 0.322608   1,980244
                  (0.673698)        (7,647961)*** (0.55594)           (-0.473695)
Utilities             -0.0008830 0.716889       -0.134420     -0.258362                 0.24241 0.232861   2,016585
                      (-0.146841) (5,048204)*** (-2,246846)** (-1,419382)


Number of
Significant
coefficients at 1 %         0              21               6                 1
Number of
Significant
coefficients at 5 %         0              21              10                 3
Number of
Significant
coeffivients at 10%         0              21              11                 9

This table reports the output from the following
regression model:
Rit = i + Rmt + iOILR(USD)t + XR + eit


where Rit is the return of the ith industry in the month t, Rmt is the return of the
market index in the month , OIL(USD)
is the return of the oil price using WTI expressed in US dollar, and
the XR is exchange rate between USD/NOK
For the regression model:
 * Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
 ** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
 *** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)



Table 13
                                                                Wald Test 1       Wald Test 2
                                                                H0 = ϒ = δ        H0 = ϒ = δ = 0
                                 Goods and services             16,05036          18,2623
                                                                (0.0001)***       (0.0001)***
                                 Banks                          2,350751          3,819108
                                                                (0.1252)          (0.1481)
                                 Basic materials                0.99185           1,787998
                                                                (0.3193)          (0.4090)
                                 Chemicals                      27,05182          3,017901
                                                                (0.0001)***       (0.2211)
                                 Construction and materials 0.890335              0.892172
                                                                (0.3454)          (0.6401)
                                 Consumer staples               0.341447          5,785993
                                                                (0.5590)          (0.0554)*
                                 Financials                     2828577           1,513232




                                                                                                           97
             The oil price influence on the stock market

- An industrial perspective for Norway and Denmark between 1990-2010

                                            (0.0926)*     (0.0005)***
                Food and beverages          0.391484      5,882551
                                            (0.5315)      (0.0528)*
                Food products               0.670979      8,582183
                                            (0.4127)      (0.0137)**
                Food producers              0.391621      5,883157
                                            (0.5314)      (0.0528)*
                Hld & Dvlp                  1,479061      1,734739
                                            (0.2239)      (0.4201)
                Industry transportation     2438980       2,484821
                                            (0.1184)      (0.2887)
                Industrials                 12,12224      18,88692
                                            (0.0005)***   (0.0001)***
                Insurance                   0.067421      9,900952
                                            (0.7951)      (0.0071)***
                Int. Oil and gas            0.334037      17,48232
                                            (0.5633)      (0.0002)***
                Marine transportation       2,895878      2,992750
                                            (0.0888)*     (0.2239)
                Oil and gas production      0.272848      21,03780
                                            (0.6014)      (0.0000)***
                Oil and gas                 0.016896      21,82898
                                            (0.8966)      (0.0000)***
                Real estate                 1,479154      1,734755
                                            (0.2239)      (0.4201)
                Telecommunication           0.386798      0.525273
                                            (0.5340)      (0.769)
                Utilities                   0.530091      5,490541
                                            (0.4666)      (0.0642)*
 * Coefficient estimate is significantly
 from zero at the 10 % ( P-value in
 parentheses)                               3             7
 ** Coefficient estimate is significantly
 from zero at the 5 % ( P-value in
 parentheses)                               0             8
 *** Coefficient estimate is significantly
 from zero at the 1 % ( P-value in
 parentheses)                              5              12




                                                                        98
                          The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010


Table 14 - Results - equation 2 (Brent)
Danish industry     α                 β (Market             γ (Oil price in      δ (XR)            R-         Adjusted Durbin-
                                      index)                USD)                                   squared    R-squared Watson
                                                                                                                        stat

                      (t-statistic)         (t-statistic)        (t-statistic)     (t-statistic)
Banks               0.000102          1,003614              0.01384              -0.230798         0.488489    0.482042   1,838796
                    (0.024899)        (8,520414)***         (0.255402)           (-1,686035)*
Basic materials     -0.002192         0.778846              0.064284             0.183013          0.242375    0.232826   1,792903
                    (-0.355589)       (6,583872)***         (1,240905)           (1,048771)
Chemicals           -0.002186         0.778871              0.064288             0.182978          0.242393    0.232843   1,792920
                    (-0.354681)       (6,584372)***         (1,241004)           (1,048532)
Construction        0.003137          0.797754              0.080144             -0.097939         0.347576    0.339353   1,949790
                    (0.715123)        (8,70571)***          (2,336862)**         (-0.67403)
Consumer staples    -0.001147         0.754836              0.017145             -0.209719         0.386227    0.378491   1,800411
                    (-0.302421)       (7,787900)***         (0.302968)           (-1,845867)*
Financials          0.000795          0.888597              -0.004432            -0.158172         0.562679    0.557167   1,723875
                    (0.244989)        (10.909222)***        (-0.131016)          (-1,765820)*
Food producers      -0.000192         0.629545              -0.023919            -0.064571         0.263015    0.253726   2,263458
                    (-0.058768)       (7,658783)***         (-0.598065)          (-0.456363)
Food and            -0.001387         0.747973              0.019392             -0.211401         0.387869    0.380153   1,800438
beverages
                    (-0.369655)       (7,714734)***         (0.344287)           (-1,878779)*
Food products       -0.000256         0.655528              -0.031308            -0.053506         0.267396    0.258162   2,285349
                    (-0.077423)       (7,960823)***         (-0.736987)          (-0.351858)
Goods               -0.002162         1,324869              0.077193             0.054428          0.791659    0.789033   2,193594
                    (-0.895445)       (26,12116)***         (2,041364)**         (0.580046)
Hld & dvlp          0.001187          0.29657               0.085502             -0.10337          0.122076    0.109883   1,850996
                    (0.271255)        (3,250428)***         (2,866481)***        (-0.89529)
Industrials         -0.002943         1,13519               0.018185             0.129035          0.533661    0.527783   1,955267
                    (-0.706334)       (15,19020)***         (0.460616)           (1,001004)
Industry            -0.003033         1,358802              0.086517             0.062501           0.73047    0.727073   2,074290
transportation
                    (-1,028285)       (22,68251)***         (1,928199)*          (0.552240)
Insurance           0.00279           0.620221              -0.032117            0.077012          0.289596    0.280641   1,996106
                    (0.768361)        (8,598562)***         (-0.688240)          (0.761400)
Marine              -0.003333         1,382293              0.092072             0.076424          0.709957    0.706301   2,048347
transportation
                  (-1,044623)         (21,53698)***         (1,913512)*          (0.630387)
Oil and gas       0.007157            1,507779              0.150812             -0.193769         0.352865    0.338797   1,781768
                  (0.566920)          (9,328198)***         (1,344359)           (-0.334737)
Real estate       0.001184            0.296585              0.08549              -0.103443         0.122083     0.10989   1,850593
                  (0.270447)          (3,250658)***         (2,865845)***        (-0.895932)
Telecommunication -0.002047           1,071468              -0.091188            -0.160074         0.345145    0.336891   2,156326
                  (-0.408314)         (11,54886)***         (-1,6902)*           (-0.850891)




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                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010



Number of
Significant
coefficients at 1 %        0                21                 2                 0
Number of
Significant
coefficients at 5 %        0                21                 4                 0
Number of
Significant
coeffivients at 10%        0                21                 7                 4

This table reports the output from the following regression model:
Rit = i + Rmt + iOILR(USD)t + XR + eit

   where Rit is the return of the ith industry in the month t, Rmt is the return of the market index in the month , OIL(USD)
is the return of the oil price using London Brent Crude expressed in US dollar, and the XR is exchange rate between
USD/DKK.
For the regression model:
 * Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
 ** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
 *** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)


Table 15
                                                                   Wald-test 1 Wald-test 2
                                                                   H0: γ = δ     H0: γ = δ = 0
                                                                     (p-value)       (p-value)
                                         Banks                     2,778910      2,916223
                                                                   (0.0955)*     (0.2327)
                                         Basic materials           0.412849      2,793826
                                                                   (0.5205)      (0.2474)
                                         Chemicals                 0.412580      2,793206
                                                                   (0.5207)      (0.2474)
                                         Construction              1,613624      7,255810
                                                                   (0.2040)      (0.0266)**
                                         Consumer staples          2,849981      3,407784
                                                                   (0.0914)*     (0.1820)
                                         Financials                2,510630      3,159344
                                                                   (0.1131)      (0.2060)
                                         Food producers            0.096588      0.414201
                                                                   (0.756)       (0.8129)
                                         Food and beverages        2,987507      3,532217
                                                                   (0.0839)*     (0.1710)
                                         Food products             0.025769      0.543683
                                                                   (0.8725)      (0.7620)
                                         Goods and services        0.057740      4,216167
                                                                   (0.8101)      (0.1215)



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- An industrial perspective for Norway and Denmark between 1990-2010

                        Hld & Dvlp              2,364987   8,508781
                                                (0.1241)   (0.0142)**
                        Industrials             0.730966   1,110466
                                                (0.3926)   (0.5739)
                        Industry
                        transportation          0.042238   3,828658
                                                (0.8372)   (0.1474)
                        Insurance               0.912364   0.986399
                                                (0.3395)   (0.6107)
                        Marine
                        transportation          0.015809   3,808783
                                                (0.8999)   (0.1489)
                        Oil and gas             0.413878   2,997147
                                                (0.5200)   (0.2234)
                        Real estate             2,366339   8,505395
                                                (0.1240)   (0.0142)**
                        Telecommunication       0.153316   2,922436
                                                (0.6954)   (0.2320)
    * Coefficient estimate is significantly
    from zero at the 10 % ( P-value in
    parentheses)                                     0          0
    ** Coefficient estimate is significantly
    from zero at the 5 % ( P-value in
    parentheses)                                     0          3
    *** Coefficient estimate is significantly
    from zero at the 1 % ( P-value in
    parentheses)                                     3          3




                                                                        101
                          The oil price influence on the stock market

              - An industrial perspective for Norway and Denmark between 1990-2010


Table 16 - Results - Equation 2 (Brent)
Norwegian            α             β - Market      ϒ - Oil price in   δ = Exchange R-          Adjusted   Durbin-
Industry                           index           USD                rate          squared    R-         Watson
                                                                      USD/NOK                  squared    stat
Goods and service    -0.003123     0.991049        -0.013346          0.373382      0.698182   0.694377   2,144044
                     (-1,100371)   (18,22839)***   (-0.276357)        (3,456332)***
Banks                0.004301      0.934711        -0.124728          -0.575313      0.413952 0.406534 1,749095
                     (0.765238)    (9,66901)***    (-2,248364)**      (-1,777158)*
Basic Materials      0.000421      1,013972        -0.05375           -0.219615      0.631027 0.626376 1,719491
                     (0.095658)    (9,61835)***    (-1,399263)        (-1,288045)
Chemicals            0.011474      1,252733        -0.102664          -0.821031      0.574518 0.555467 1,487687
                     (0.794434)    (4,37694)***    (-0.787291)        (-1,820628)*
Construction and     0.006941      0.649824        0.020542           -0.172943      0.241544 0.231984 2,069000
Materials
                     (1,30094)     (7,422115)***   (0.33592)          (-0.829715)
Consumer Staples     0.000347      1,059509        -0.124869          -0.014888      0.59393   0.588812 2,108311
                     (0.096385)    (15,31444)***   (-2,72415)**       (-0.078059)
Financials           -0.000437     1,021828        -0.153698      -0.435162     0.586363 0.581149 1,661479
                     (-0.091502)   (12,99349)***   (-4,142929)*** (-2,485319)**
Food and Beverage -0.000179        1,0634460       -0.123876          -0.007185      0.593875 0.588756 2,113199
                  (-0.049276)      (15,55845)***   (-2,69669)**       (-0.037773)
Food Products        0.000721      1,081354        -0.164967      -0.011399          0.585146 0.579917 2,111651
                     (0.194985)    (15,08997)***   (-3,421489)*** (-0.06004)
Food Producers       -0.000175     1,063447        -0.123881      -0.00717           0.593874 0.588755 2,113194
                     (-0.048396)   (15,55857)***   (-2,696786)*** (-0.037696)
Hld Dvlp             0.000906      0.589797        0.032205           0.28504        0.271627 0.262445 2,209217
                     (0.203941)    (8,088142)***   (0.583326)         (1,370795)
Industry Transport   -0.00261      1,0062410       0.039188           0.241385       0.609435 0.604512 1,781353
                     (-0.646582)   (14,33847)***   (0.708457)         (1,752963)*
Industrials          1,0488060     -0.076504       0.348345           -0.004054      0.620572 0.615789 2,2021640
                     (-1,338033)   (18,2145)***    (2,488187)**       (-1,156306)
Insurance            -0.009344     1,3227380       -0.204701          -0.113188      0.465695 0.45896     2,098268
                     (-1,42124)    (8,322365)***   (-2,752654)*** (-0.393646)
Int, Oil and Gas     0.000599      0.844141        0.131996           0.196439       0.730125 0.726723 2,183008
                     (0.251575)    (18,68053)***   (3,508154)***      (1,817925)*
Marine               -0.002799     0.991911        0.043793           0.274608       0.594222 0.589107 1,777986
Transportation
                     (-0.676064)   (13,91314)***   (0.77335)          (1,941827)*
Oil and Gas          0.000276      0.895179        0.14505            0.205684       0.793311 0.790706 1,977124
Production
                     (0.116984)    (19,60066)***   (3,642731)***      (1,825577)*
Oil and Gas          -0.000122     0.9252          0.149007           0.12925        0.846363 0.844426 1,996977
                     (-0.064077)   (26,21052)***   (4,640898)***      (1,784453)*
Real Estate          0.000902      0.589795        0.032207           0.285048       0.271627 0.262446 2,209208
                     (0.203016)    (8,088155)***   (0.583372)         (1,37084)




                                                                                                   102
                            The oil price influence on the stock market

             - An industrial perspective for Norway and Denmark between 1990-2010

Telecommunication 0.004207           0.820208          -0.011327         -0.156212       0.3299     0.321453 1,990258
                  (0.692137)         (7,79338)***      (-0.165912)       (-0.682187)
Utilities              -0.000659     0.714007          -0.16593       -0.279643          0.251607 0.242174 2,026975
                       (-0.110356)   (5,13516)***      (-2,946209)*** (-1,552902)


Number of                   0              21                    8               1
Significant
coefficients at 1 %
Number of                   0              21                    12              2
Significant
coefficients at 5 %
Number of                   0              21                    12              9
Significant
coefficients at 10 %

This table reports the output from the following
regression model:
Rit = i + Rmt + iOILR(USD)t + XR + eit


where Rit is the return of the ith industry in the month t, Rmt is the return of the market index in the month , and
OIL(NOK)
is the return of the oil price using London Brent Crude expressed in US dollar, and the XR is the exchange rate between
USD/NOK
For the regression model:
 * Coefficient estimate is significantly from zero at the 10 % ( t-statistics in parentheses)
 ** Coefficient estimate is significantly from zero at the 5 % ( t-statistics in parentheses)
 *** Coefficient estimate is significantly from zero at the 1 % ( t-statistics in parentheses)



Table 17
                                                                                     Wald Test 1 Wald Test 2
                                                                                     H0 = ϒ = δ  H0 = ϒ = δ
                                                                                                 =0
                                                    Goods and services               15,9025     15,96398
                                                                                     (0.0001)*** (0.0003)***
                                                    Banks                            2,311256     5,452338
                                                                                     (0.1284)     (0.0655)*
                                                    Basic materials                  0.909852     3,533776
                                                                                     (0.3402)     (0.1709)
                                                    Chemicals                        2,672381     3,455663
                                                                                     (0.1021)     (0.1777)
                                                    Construction and materials       0.913418     0.995761
                                                                                     (0.3392)     (0.6078)
                                                    Consumer staples                 0.396192     9,118541
                                                                                     (0.5291)     (0.0105)**
                                                    Financials                       27,07005     19,85417
                                                                                     (0.0999)*    (0.000)



                                                                                                         103
                         The oil price influence on the stock market

            - An industrial perspective for Norway and Denmark between 1990-2010

                                               Food and beverages             0.44999     9,101152
                                                                              (0.5023)    (0.0106)
                                               Food products                  0.784924    14,52946
                                                                              (0.3756)    (0.0007)***
                                               Food producers                 0.45014     9,101994
                                                                              (0.5023)    (0.0106)
                                               Hld & Dvlp                     1,523867    1,988222
                                                                              (0.217)     (0.3701)
                                               Industry transportation        2,46488     3,080785
                                                                              (0.1164)    (0.2143)
                                               Industrials                    12,08957    14,96305
                                                                              (0.0005)*** (0.0006)***
                                               Insurance                      0.085947    5,487937
                                                                              (0.7694)    (0.0643)
                                               Int. Oil and gas               0.257066    23,79279
                                                                              (0.6121)    (0.000)***
                                               Marine transportation          2,926546    3,801587
                                                                              (0.0871)*   (0.1495)
                                               Oil and gas production         0.199348    29,018
                                                                              (0.6552)    (0.000)***
                                               Oil and gas                    0.056492    27,48424
                                                                              (0.8121)    (0.000)***
                                               Real estate                    1,52396     1,988367
                                                                              (0.217)     (0.37)
                                               Telecommunication              0.359367    0.503328
                                                                              (0.5489)    (0.7775)
                                               Utilities                      0.46784     8,866041
                                                                              (0.494)     (0.0119)**
* Coefficient estimate is significantly from zero at the 10 % ( P-value in         2           5
parentheses)
** Coefficient estimate is significantly from zero at the 5 % ( P-value in         0           7
parentheses)
*** Coefficient estimate is significantly from zero at the 1 % ( P-value in        4           8
parentheses)




                                                                                                   104