Contracts Outline Warren by wcd64786

VIEWS: 4 PAGES: 91

More Info
									   Latest Developments in
  Weather Risk Management

presentation to “Risk Finance” , 22-24 March, 2004

     The Finance and Treasury Association




               Dr Harvey Stern,

       Shoni Dawkins & Robin Hicks

    Bureau of Meteorology, Melbourne
          Important WEB Sites

• http://www.bom.gov.au


• http://www.artemis.bm/artemis.htm


• http://www.wrma.org
          Outline of Presentation
• Introduction
• The foundation of the weather market.
• The growing diversification of weather risk
  products and their interest.
• Sources of meteorological data, their quality
  control and application.
• Managing weather risk using daily weather
  forecasts and seasonal outlooks.
         Outline of Presentation
• Introduction…
         The Noah Rule

      “Predicting rain doesn’t count;
           Building arks does”.

              Warren Buffett,
Australian Financial Review,11 March 2002.
         Weather-linked Securities
• Weather-linked securities have prices which are linked to
  the historical weather in a region.
• They provide returns related to weather observed in the
  region subsequent to their purchase.
• They therefore may be used to help firms hedge against
  weather related risk.
• They also may be used to help speculators monetise their
  view of likely weather patterns.
             Some Recent News

• The next few slides illustrate some recent news.
         Outline of Presentation
• The foundation of the weather market…
  Foundation of the Weather Market
“The foundation of today’s financial weather contracts is in
  the US power market …

For the weather-sensitive end-user, not to hedge is to
  gamble on the weather.”

                                                  Robert S. Dischell
          Outline of Presentation
• The growing diversification of weather risk
  products and their interest…
       WRMA 2002 Survey Results.
         The Growing Interest.
• 3,937 contracts transacted in last 12 months (up 43%
  compared to previous year).
• Notional value of over $4.3 billion dollars (up 72%).
• Market dominated by US (2,712 contracts), but growth in
  the past year is especially so in Europe and Asia.
• Australian market accounts for 15 contracts worth over $25
  million (6 contracts worth over $2 million, previously).

              Source: Weather Risk Management Association Annual Survey (2002)
       WRMA 2002 Survey Results.
          The Diversification.
• Another significant development is the diversification of
  the types of contracts that were transacted.
• Temperature-related protection (for heat and cold)
  continues to be the most prevalent, making up over 82
  percent of all contracts (92% last year)
• Rain-related contracts account for 6.9% (1.6% last year),
  snow for 2.2% (0.6% last year) and wind for 0.4% (0.3% last
  year).
               Source: Weather Risk Management Association Annual Survey (2002)
     Views prior to the release of the
       WRMA 2003 Survey Results
“Most market participants … are predicting an increase in total notional
  volumes”
“The general malaise that has clouded the weather risk market in the past
  year may be on the wane”
“…we will see a sizeable decrease in volumes … as Enron, Aquila … have
  left the market”
“The effect of market departures was clearly felt …[but]… big players
  more than compensated for the loss, providing liquidity and execution
  of service”
“…weather forecasting improvements could pose a threat to market
  development”
                                                   Energy Power Risk Management
                                                                        May2003
     WRMA 2003 Survey Results (a)
A near tripling of contracts transacted (11,756 contracts
  compared with 3937 previously)

Notional value of contracts fell slightly ($US4.2b compared
  with $US4.3b previously)

Indicates a surge in smaller contracts, and a broader
  spectrum of users

Total business generated over the past 6 years: $US15.8b
    WRMA 2003 Survey Results (b)

North American market: 2217 contracts compared with 2712
  previously (20% decline)

European market: 1480 contracts compared with 765
  previously (90% increase)

Asian market: 815 contracts compared with 445 previously
  (85% increase)
     WRMA 2003 Survey Results (c)
Diversification Increasing:

Temperature related contracts 85% compared with 90%
  previously

Rain related contracts 8.6% compared with 6.9% previously

Wind-related contracts 1.6% compared with 0.3% previously

Snow related contracts 2.1% compared with 2.2% previously
           The Asia-Pacific Region

• Interest in weather risk management has grown in the
  Asia-Pacific Region (covering electricity, gas, &
  agriculture). Countries involved include:
- Japan;
- Korea; and,
- Australia/New Zealand.


                            Source: Weather Risk Management Association.
          Australian Developments
• For many years, the power industry has received detailed
  weather forecasts from the Bureau.
• Now, Australia has joined the global trend towards an
  increased focus on the management of weather-related
  risk.
• The first instance of an (Australian) weather derivative
  trade occurred about three years ago.
• A number of businesses have now moved into the trading
  of weather risk products, almost all “over the counter”.
• Partnerships are emerging between merchant banks and
  weather forecasting companies.
                   Securitisation

• The reinsurance industry experienced several catastrophic
  events during the late 1980s & early 1990s.
• The ensuing industry restructuring saw the creation of new
  risk-management tools.
• These tools included securitisation of insurance risks
  (including weather-related risks).
• Weather securitisation may be defined as the conversion of
  the abstract concept of weather risk into packages of
  securities.
• These may be sold as income-yielding structured products.
               Catastrophe Bonds
• A catastrophe (cat) bond is an exchange of principal for
  periodic coupon payments wherein the payment of the
  coupon and/or the return of the principal of the bond is
  linked to the occurrence of a specified catastrophic event.
• The coupon is given to the investor upfront, who posts the
  notional amount of the bond in an account.
• If there is an event, investors may lose a portion of (or their
  entire) principal.
• If there is no event, investors preserve their principal and
  earn the coupon.
                                        Source: Canter & Cole at http://www.cnare.com
              Catastrophe Swaps
• A catastrophe (cat) swap is an alternative structure, but
  returns are still linked to the occurrence of an event.
• However, with swaps, there is no exchange of principal.
• The coupon is still given to the investor upfront, but the
  structure enables investors to invest the notional amount
  of the bond in a manner of his own choosing.
                                      Source: Canter & Cole at http://www.cnare.com
              Weather Derivatives

• Weather derivatives are similar to conventional financial
  derivatives.
• The basic difference lies in the underlying variables that
  determine the pay-offs.
• These underlying variables include temperature,
  precipitation, wind, and heating (& cooling) degree days.
           Derivative or Insurance?

• A Derivative:
  -has ongoing economic value,
  -is treated like any other commodity,
  -is accounted for daily, &
  -may therefore affect a company’s credit rating.
• An Insurance Contract:
  -is not regarded as having economic value, &
  -therefore does not affect a company’s credit rating.
          A Weather-linked Option
• An example of a weather linked option is the Cooling
  Degree Day (CDD) Call Option.
• Total CDDs is defined as the accumulated number of
  degrees the daily mean temperature is above a base figure.
• This is a measure of the requirement for cooling.
• If accumulated CDDs exceed “the strike”, the seller pays
  the buyer a certain amount for each CDD above “the
  strike”.
    Specifying the CDD Call Option
• Strike: 400 CDDs.
• Notional: $100 per CDD (> 400 CDDs).
• If, at expiry, the accumulated CDDs > 400, the seller of the
  option pays the buyer $100 for each CDD > 400.
Pay-off Chart for the CDD
       Call Option
               An Historical Note:
                      An Early Example

• In 1992, the present author explored a methodology to
  assess the risk of climate change.
• Option pricing theory was used to value instruments that
  might apply to temperature fluctuations and long-term
  trends.
• The methodology provided a tool to cost the risk faced
  (both risk on a global scale, and risk on a company specific
  scale).
• Such securities could be used to help firms hedge against
  risk related to climate change.
   Carbon Disclosure Project (2003)

• "Investors failing to take account of climate change and
  carbon finance issues in the asset allocation and equity
  valuations may be exposed to significant risks which, if left
  unattended, will have serious investment repercussions
  over the course of time."
   Cooling Degree Days (1855-2000)
                   (and climate change)

• Frequency distribution of annual Cooling Degree Days at
  Melbourne using all data:
   Cooling Degree Days (1971-2000)
                    (and climate change)

• Frequency distribution of annual Cooling Degree Days at
  Melbourne using only recent data:
          Outline of Presentation
• Sources of meteorological data, their quality
  control and application…
 Types of Data Available

• Rainfall – daily, monthly,
  seasonal, analyses,
• Temperature – hourly, maximum
  and minimum, dew point, monthly
  averages and extremes
• Wind speed, hourly , maximum
  wind gust, wind run
      Sources of Observations

• Bureau Staffed
  Sites
  – Fully trained
     observers
  – Equipment
     maintenance
       Bureau Stations

• Some in
  remote
  locations
• Some
  located at
  major
  airports
              Automated Weather
                   Stations




Currently 513 sites
        Features of an Automatic
            Weather Station
• In general, compared to human observers:
   – AWS are more consistent in their measurement
   – AWS provide data at a significantly greater
     frequency
   – AWS provide data in all weather, day and night,
     365 days per year
   – AWS can be installed in sparsely populated
     areas
   – AWS are significantly cheaper than human
     observers
       Features of an Automatic
        Weather Station (cont.)
• However, AWS suffer a number of disadvantages.
  These are:
   – Some elements are difficult to automate (e.g.
     cloud cover)
   – AWS require a large capital investment
   – AWS are less flexible than human observers
 Automatic Weather Stations (cont.)

• Consistency between sites
  – Bureau Specification 2013, based on WMO
    guidelines
  – Different sensors because some sites are
    designed around specific users / programs
     • Aviation, agriculture, climate, marine
  – Inspection routine to ensure calibration,
    preventative maintenance, software upgrades
            Automated Weather
              Stations (cont.)
• Sites are fenced to
   – minimise
     obstructions,
   – reduce
      • vandalism,
        interference from
        animals
• Rural locations
  generally
  representative of local
  area
          Cooperative Observers
• Currently about 300 sites
• Historically main source of
  surface observations
   – Lighthouses
   – Post Offices
• Generally up to 7
  observations per day
• Replacement with AWS, or
  concurrent for cloud, visibility
  observations
       Rainfall only observations

• Some 20000 sites
  historically, about
  6000 sites currently
  open
• Majority send
  monthly returns –
  key sites daily
• Daily 9am
  observations
                 Pluviograph

• Sites often owned by
  water authorities
• Gives indication of
  timing of heavy rain
• Data generally not
  available for long
  period after an event
• 1000 sites with data,
  300 Bureau sites
  currently open
        Things that can go wrong


• Instrumentation problems
   – Unattended sites
      • equipment problems
      • Vandalism
   – Communication problems – remote areas
   – Power cuts, spikes
   – Calibration of instruments, time accuracy
Effects of changes in
   instrumentation
             Sensor characteristics

• Resolution - the smallest change the device can detect (this is
  not the same as the accuracy of the device).
• Repeatability - the ability of the sensor to measure a
  parameter more than once and produce the same result in
  identical circumstances.
• Response time - normally defined as the time the sensor takes
  to measure 63% of the change.
• Drift - the stability of the sensor's calibration with time.
• Hysteresis - the ability of the sensor to produce the same
  measurement whether the phenomenon is increasing or
  decreasing.
• Linearity - the deviation of the sensor from ideal straight line
  behaviour.
           Observing Practices

• Observers receive training in standard practices
• Scheduling of manual observations often affected
  by availability of observer, or access to site
• Change in use of Daylight Savings Time
   How representative is
         the site?
• Site might be located in valley or
  on hilltop
• Surrounding vegetation might not
  be typical of general area
• Many sites become surrounded
  by buildings over time -
  urbanisation
                                            Urbanisation
                                       Melbourne versus Victoria Minimum Temps
                          1.5
                                 Vic Min Temps          Melb Min Temps
Annual Temp Anom (degC)




                           1


                          0.5


                           0


                  -0.5


                           -1


                  -1.5
                      1950      1955    1960   1965   1970   1975   1980   1985   1990   1995   2000
                                                             Year
    Distance of site from
       area of interest
• Rainfall totals can vary
  significantly over short distances
  because of terrain or
  thunderstorms
• Minimum temperatures drop
  sharply as one travels inland from
  the coast, particularly in winter
• Frost hollows, funneling of winds
         Changes in site location

• Moves to less urban airport
  sites
• Reference Climate Stations
   – Min 30 years of continuous
     record with minimal
     inhomogenieties
   – Minimally affected by
     urban effects
• Site changes forced by
  change in observer
         Bureau sources of data

• SILO
• Climate Data
  Services
• SSU
• Regional
  Offices
     Useful tools in Silo

• Point patched data
  – To estimate missing historical
    data
  – Uses neighbouring sites
• Data Drill
  – Uses gridded data – no original
    observations
  – Resolution of 0.05 degrees
    (about 5km)
           Timeliness

• Data available on SILO and
  Bureau web site in close to real
  time
   – Subject to more errors, gaps
     etc
• Data available after quality
  control processes have been
  applied
        Future trends

• More automated observation sites
• Automated data quality control
  procedures to enable more
  checks to be performed
• More data and at higher
  frequencies
• Increased use of remotely sensed
  data for estimations in data
  sparse regions
          Future trends in data




Solar radiation data –
traditional network versus
satellite derived estimates
          Outline of Presentation
• Managing weather risk using daily weather
  forecasts and seasonal outlooks…
         Should Companies Worry?

•   In the good years, companies make big profits.
•   In the bad years, companies make losses.
-    Doesn’t it all balance out?
-    No. it doesn’t.
•   Companies whose earnings fluctuate wildly
    receive unsympathetic hearings from banks and
    potential investors.
    Weather-related Industry Risk
"Shares in Harvey Norman fell almost 4 per cent yesterday
as a cool summer and a warm start to winter cut into sales
growth at the furniture and electrical retailer's outlets…
Investors were expecting better and marked the shares
down 3.8 per cent to a low of $3.55…

Sales at Harvey Norman were hit on two fronts. Firstly, air
conditioning sales were weak because of the cool summer,
and a warmer than usual start to winter had dampened
demand for heating appliances”.
                                Source: The Australian of 18 April, 2002
Weather-related Agricultural Risk

“The Australian sugar industry is facing its fifth difficult
year in a row with a drought dashing hopes of an improved
crop in Queensland, where 95% of Australia's sugar is
grown...
Whilst dry weather during the May-December harvest
period is ideal for cane, wet weather during this time
causes the mature cane to produce more shoots and
leaves, reducing its overall sugar content”.

                              (Australian Financial Review of 8 May, 2002)
             The Road to
       Weather Risk Management.
• The era of (mostly) categorical forecasts.
• The rapid increase in the application of probability
  forecasts.
• The provision of forecast verification (i.e.
  accuracy) data.
• The era of the “guaranteed forecast”, with user
  communities being compensated for an inaccurate
  prediction.
• The purchase of “stakes” in the industry (by multi-
  national companies).
             Pricing Derivatives

There are three approaches that may be applied to
the pricing of derivatives.
These are:
•Historical simulation (applying "burn analysis");
•Direct modelling of the underlying variable’s
distribution (assuming, for example, that the
variable's distribution is normal); and,
•Indirect modelling of the underlying variable’s
distribution (via a Monte Carlo technique).
    Returning to the Cane Grower

• Suppose that our cane grower has experienced an
  extended period of drought.
• Suppose that if rain doesn't fall next month, a
  substantial financial loss will be suffered.
• How might our cane grower protect against
  exceptionally dry weather during the coming
  month?
               One Approach

• One approach could be to purchase a Monthly
  Rainfall Decile 4 Put Option.
• Assume that our cane grower decides only to take
  this action when there is already a risk of a dry
  month.
• That is, when the current month's Southern
  Oscillation Index (SOI) is substantially negative.
• So, the example is applied only to the cases when
  the current month's Southern Oscillation Index
  (SOI) is in the lowest 5% of possible values, that
  is, below -16.4.
  Specifying the Decile 4 Put Option
• Strike: Decile 4.
• Notional: $100 per Decile (< Decile 4).
• If, at expiry, the Decile is < Decile 4, the seller of
  the option pays the buyer $100 for each Decile <
  Decile 4.
Payoff Chart for Decile 4 Put Option
Outcomes for Decile 4 Put Option
   Evaluating the Decile 4 Put Option

• 14.2% cases of Decile 1 yields $(.142)x(4-1)x100=$42.60
• 13.2% cases of Decile 2 yields $(.132)x(4-2)x100=$26.40
• 8.4% cases of Decile 3 yields $(.084)x(4-3)x100=$8.40
• The other 25 cases (Decile 4 or above) yield nothing.
…leading to a total of $77.40, which is the price of our put
  option.
     Weather & Climate Forecasts
• Daily weather forecasts may be used to manage
  short-term risk (e.g. pouring concrete).
• Seasonal climate forecasts may be used to
  manage risk associated with long-term activities
  (e.g. sowing crops).
• Forecasts are based on a combination of solutions
  to the equations of physics, and some statistical
  techniques.
• With the focus upon managing risk, the forecasts
  are increasingly being couched in probabilistic
  terms.
           An Illustration of the
           Impact of Forecasts
• When very high temperatures are forecast, there
  may be a rise in electricity prices.
• The electricity retailer then needs to purchase
  electricity (albeit at a high price).
• This is because, if the forecast proves to be
  correct, prices may “spike” to extremely high
  (almost unaffordable) levels.
      Impact of Forecast Accuracy

• If the forecast proves to be an “over-estimate”,
  however, prices will fall back.
• For this reason, it is important to take into account
  forecast accuracy data in determining the risk.
          Forecast Accuracy Data

The Australian Bureau of Meteorology's Melbourne office
possesses data about the accuracy of its temperature
forecasts stretching back over 40 years.
Customers receiving weather forecasts have, recently,
become increasingly interested in the quality of the service
provided.
This reflects an overall trend in business towards
implementing risk management strategies. These strategies
include managing weather related risk.
Indeed, the US Company Aquila developed a web site that
presents several illustrations of the concept:
http://www.guaranteedweather.com
     Using Forecast Accuracy Data

• Suppose we define a 38 deg C call option (assuming a
  temperature of at least 38 deg C has been forecast).
• Location: Melbourne.
• Strike: 38 deg C.
• Notional: $100 per deg C (above 38 deg C).
• If, at expiry (tomorrow), the maximum temperature is
  greater than 38 deg C, the seller of the option pays the
  buyer $100 for each 1 deg C above 38 deg C.
Pay-off Chart: 38 deg C Call Option
       Determining the Price of the
          38 deg C Call Option


• Between 1960 and 2000, there were 114 forecasts of at
  least 38 deg C.
• The historical distribution of the outcomes are examined.
Historical Distribution of Outcomes
            Evaluating the 38 deg C
              Call Option (Part 1)
•   1 case of 44 deg C yields $(44-38)x1x100=$600
•   2 cases of 43 deg C yields $(43-38)x2x100=$1000
•   6 cases of 42 deg C yields $(42-38)x6x100=$2400
•   13 cases of 41 deg C yields $(41-38)x13x100=$3900
•   15 cases of 40 deg C yields $(40-38)x15x100=$3000
•   16 cases of 39 deg C yields $(39-38)x16x100=$1600
                                                        cont….
           Evaluating the 38 deg C
             Call Option (Part 2)
• The other 61 cases, associated with a temperature of 38
  deg C or below, yield nothing.
• So, the total is $12500.
• This represents an average contribution of $110 per case,
  which is the price of our option.
   Finally … Ensemble Forecasting
• Another approach to obtaining a measure of forecast
  uncertainty, is to use ensemble weather forecasts.
• The past decade has seen the implementation of these
  operational ensemble weather forecasts.
• Ensemble weather forecasts are derived by imposing a
  range of perturbations on the initial analysis.
• Uncertainty associated with the forecasts may be derived
  by analysing the probability distributions of the outcomes.
• A parallel approach is to “run” different models with the
  same initial analysis
• Spot the differences on the next slide …

								
To top