2nd International Conference on Fog and Fog Collection

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					           Uncertainty in 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
  verification data in determining the risk.
    Using Forecast Verification 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

Total 53 cases                              Total $12500
         Evaluating the 38 deg C
           Call Option (Part 2)
The other 61 cases (15+7+14+5+1+7+3+1+2+2+0+1+0+2+1),
  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
  ($12500/[61 cases (38 deg C or below)+53 cases (above
              38 deg C) ]), which is the
                    price of our option.
               Example from Aquila
  Business Situation

• A wheat farmer has specific times during the year when his crops must
  sprayed with pesticide in order to ensure a healthy yield.
• If there is substantial rain within a few days of application, the
  pesticide will wash away and will have to be reapplied.
• Not only is the pesticide application a substantial part of his operation
  costs, but he also could miss his window and hit a season or growth
  cycle that is especially susceptible to pests if he is unable to
  reschedule the application.
• In order to keep his costs down and guarantee the best growing
  conditions for his crop, he needs to access a short-term precipitation

         Example from Aquila (cont.)

• The Guaranteed Forecast product along with a partnership with
  agrochemical companies - allows him to schedule the pesticide
  application, and ensures that his crops will be protected against pests,
  even in rainy conditions.
• Based on a 72-hour forecast, he can have the pesticides applied if the
  precipitation will be less than 5 mm.
• If the forecast predicts 5 mm or more, he should wait for the next dry
• If he chooses to apply based on the forecast the chemical company
  will treat the farmer's fields.
• If the forecast was incorrect, and too much rain falls, the chemical
  company can reapply the farmer's field free of charge.

            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.
             Some Important Issues

•   Quality of weather and climate data.
•   Changes in the characteristics of observation sites.
•   Security of data collection processes.
•   Privatisation of weather forecasting services.
•   Value of data.
•   Climate change.
  Weather Derivative Applications
• Several Case Studies in the Australia Market will be
  analysed including:

                            Power        Soft Drink Sectors
     Air Conditioning

                           Theme Park
        Clothing                            Brewing


      Ice Cream                           Gas

                                        Weather Derivatives
     Applications: Agricultural (1)
• From plantation to harvest, precipitation, temperature,
  sunshine hours and wind can affect the quality and
  quantity of a crop.

     Applications: Agricultural (2)
• Rising production costs and stricter rules regulating the
  use of agrochemicals mean farmers must be
  increasingly efficient in their management practices.

• Although more intensive technology programs have
  been developed in recent years to integrate the use of
  high-yield seed varieties with planned applications of
  fertilizers, herbicides, and fungicides,weather remains a
  major risk.
     Applications: Agricultural (3)
• While there is a strong correlation between fluctuations
  in crop production volumes and the weather, risk
  management tools are now available which can
  minimize the financial impact of the weather on a
  grower's profitability.

• For example, weather derivatives can be tailored to
  protect growers against losses to heat-loving crops,
  such as cotton, due to frosts or prolonged cloudiness in
  the early stages of development.
     Applications: Agricultural (4)
• Similarly, weather derivatives can be structured to
  provide financial compensation for the ill effects of
  excess precipitation. (Too much rainfall can cause
  flooding and ponding of the soil, which can restrict the
  amount of oxygen available to root systems. This, in
  turn, can reduce nutrient uptake, leading to nitrate
  leaching and an increase in the incidence of disease.)
     Applications: Agricultural (5)
• The diagram illustrates the payout structure of an
  option designed for a cotton grower in New South
  Wales that needs protection against excessive rainfall.
  This derivative will pay the farmer AU$70,000 per
  millimetre of rainfall in excess of 150mm between 1st
  March - 30th April, less the premium. A maximum
  payout of AU$10,000,000 is set.

     Applications: Agricultural (6)
• With weather exposure covered by a derivative, yield-
  related financial volatility can be reduced significantly.
  The earnings of the grower are thus stabilized, and
  minimum levels of financial income guaranteed, before
  the crop is sold, making profit forecasting more
  predictable and accurate.

• The grower's strengthened risk management program,
  combined with more transparent accounts, may result
  in a lower cost of debt from financial institutions. In
  general, profit levels stabilize, and business
  management decisions can be made with greater
The increasing focus on weather risk

• 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)
        Survey Design and
        Implementation (1)
• Presurvey (sent in February)
   – Sent to All WRMA members
   – Will you participate? 20 companies responded
     in 2002 (19 in 2001)
• Survey (sent in April)
• Establish size of market between April 2001 and
  March 2002 (Latest statistics)
• 5 Pages in total (2 pages returned to PwC)
      • General information about company
      • Information on Contracts
   – Responses confidential and destroyed once
             •Source: Weather Risk Management Association Annual Survey (2002)

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