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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).
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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.
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.
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 cont….
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.
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: Soft Drink Sectors Power Air Conditioning Theme Park Clothing Brewing Mining Ice Cream Gas Agricultural Weather Derivatives
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.
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). Source:EnronOnline
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. Source:EnronOnline
Applications: Power E.g. 2 (4) • A weather-indexed commodity swap can be structured to protect against such opportunity losses inherent in hedging programs. Source:EnronOnline
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. Source:EnronOnline
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 AU$0.10c/MW over the base price. Source:EnronOnline
Applications: Power E.g. 2 (7) • If the cumulative number of CDDs for February equals 125, the power company would receive AU$51.50/MW(AU$50 + AUD$0.10 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. Source:EnronOnline
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 tabulated Source: Weather Risk Management Association Annual Survey (2002)