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
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.

10
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.

11
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
12
Applications Power (1)

• "Earnings from Australian operations were lower
  primarily because of abnormally warm winter
  temperatures in Victoria that affected both
  electric and gas operations. A utilities company
  in Texas, November 1999
• Demand for electric power is volatile, dependent
  upon numerous unpredictable factors, including
  the weather. New risk management tools can help
  power generators mitigate the impact of extreme
  weather conditions.

13
Applications Power E.g. 1 (2)

• A power generator can hedge its power price risk
  with a financial swap. However, it will incur an
  opportunity loss against the RRP (pool) price if
  temperatures in South Australia rise above normal
  during the peak cooling season (December -
  March).

• SourceEnronOnline

14
Applications Power E.g. 1 (3)

• Under such conditions, a generator would like to
  receive a higher price for its power, which it
  will already have hedged through an electricity
  swap.

• SourceEnronOnline

15
Applications Power E.g. 2 (4)

• A weather-indexed commodity swap can be
  structured to protect against such opportunity
  losses inherent in hedging programs.

• SourceEnronOnline

16
Applications Power E.g. 2 (5)

• The South Australian generator agrees to sell
  60MW of flat power at a price of 50/MW for the
  month of February 2001. Having analyzed
  historical weather conditions, both parties agree
  on a trigger number of 110 cooling degree days
  for February. CDDs are calculated as the
  cumulative number of CDDs for the month of
  February.

• SourceEnronOnline

17
Applications Power E.g. 2 (6)

• Should the underlying weather conditions be
  warmer than the trigger, the power producer will
  be assured of receiving a higher price for its
  power. For every CDD per day above 110, to a
  limit of 200, the power company will be paid
  AU0.10c/MW over the base price.

• SourceEnronOnline

18
Applications Power E.g. 2 (7)

• If the cumulative number of CDDs for February
  equals 125, the power company would receive
  AU51.50/MW(AU50 AUD0.10 x (125 - 110)). If
  the weather proves to be cooler than the strike
  of 400 CDDs, the generator will still be assured
  of a price of 50 per MW from the weather-indexed
  commodity swap.

• SourceEnronOnline

19
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)

20
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)