Uncertainty%20in%20forecasts

1
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.

2
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.

3
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.

4
Pay-off Chart 38 deg C Call Option
5
Determining the Price of the38 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.

6
Historical Distribution of Outcomes
7
Evaluating the 38 deg C Call Option (Part 1)

• 1 case of 44 deg C yields (44-38)x1x100600
• 2 cases of 43 deg C yields (43-38)x2x1001000
• 6 cases of 42 deg C yields (42-38)x6x1002400
• 13 cases of 41 deg C yields (41-38)x13x1003900
• 15 cases of 40 deg C yields (40-38)x15x1003000
• 16 cases of 39 deg C yields (39-38)x16x1001600
• Total 53 cases Total 12500
• cont.

8
Evaluating the 38 deg C Call Option (Part 2)

• The other 61 cases (1571451731220102
  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.

9
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 forecast.

Source http//www.guaranteedforecast.com
10
Example from Aquila (cont.)

• Solution
• 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 period.
• 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.

Source http//www.guaranteedforecast.com
11
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.

12
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.

13
Weather Derivative Applications

• Several Case Studies in the Australia Market will
  be analysed including

Soft Drink Sectors
Power
Air Conditioning
Theme Park
Clothing
Brewing
Mining
Gas
Ice Cream
Agricultural
Weather Derivatives
14
Applications Agricultural (1)

• From plantation to harvest, precipitation,
  temperature, sunshine hours and wind can affect
  the quality and quantity of a crop.

• SourceEnronOnline

15
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.

16
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.

17
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.)

18
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 AU70,000 per millimetre of rainfall in
  excess of 150mm between 1st March - 30th April,
  less the premium. A maximum payout of
  AU10,000,000 is set.

• SourceEnronOnline

19
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 confidence.

20
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)

21
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
  tabulated

• Source Weather Risk Management Association
  Annual Survey (2002)