Weather Derivatives necessity, methods and application

1. Weather Derivativesnecessity, methods and application Reinhard Hagenbrock Seminar of the working group on Climate Dynamics Bonn, 16. Mai 2003

2. Outline • “History” • What is a ‘weather derivative’? • Idealised example • Market: players, -places and requirements • Use of meteorology • Summary and outlook

3. “History” of weather derivatives • “risk management” of weather risks has always been part of insurance business • storms, crop failure, floods, ... • ‘accident’ caused by weather extremes is insured • starting point for weather derivatives: dependency of profit on ‘weather’ • approx. 20 % of business activities in western economies (partly) dependent on ‘weather’

4. “History” of weather derivatives • price risk • higher acquisition prices (e.g. for crop) • higher energy consumption • extra costs (e.g. for irrigation) • volume risk • in the production (e.g. agriculture) • in the sales (e.g. ice cream)

5. “History” of weather derivatives • Price risks may generally be managed with options / long term contracts • “weather risk” generally is a volume risk, price risk should by managed independently

6. “History” of weather derivatives • Starting of weather derivatives: dependency of energy sales on temperature

7. “History” of weather derivatives • First weather derivative: Sep 1997 between two energy suppliers • aim: to balance electricity sales caused by temperature fluctuations in winter 1997/98 • concept seemed simple, benefit obvious • new, exotic derivatives dealt at Chicago Mercantile Exchange since Sep. 1999

8. What is a ‘weather derivative’? • ... “derivative financial instrument in which meteorological data - e.g. temperature - is used as a basis product” • Degree-Day • Heating Degree Day HDD(t) = max(65°-T(t),0) • Cooling Degree Day CDD(t) = min(T(t)-65°,0) • usually summed up over a month/season • sometimes: DD with other reference temperatures, average temperature

9. What is a ‘weather derivative’? • Other indices: • precipitation • Indices are dealt like goods

10. What is a ‘weather derivative’? • 70-80% of the weather derivative deals are ‘options’ • ‘Put’: pay at end of contract if index is small: P = T  min((max(X-V),0),C) • T: ‘tick size’ or ‘notional’, e.g. 100 $/HDD • V: value of index at end of contract • X: ‘strike’ of the option • C: ‘cap-strike’: upper limit of pay • ‘Call’: counterpart to ‘Put’

11. What is a ‘weather derivative’? • ‘Swaps’: Interchange between Put and Call, no premium • more complex contracts: ‘Collars’, ‘spreads’ to chose appropriate chance/risk balance • other contracts • hybrid contracts • non-linear pay function • critical-day contracts

12. What is a ‘weather derivative’? • Differences between weather insurance and weather derivative: • proof of damage • no strict link between index value and damage • trade with contracts in a secondary market • standardised contracts • differences in accounting and fiscal aspects

13. What is a ‘weather derivative’? • Multitude of derivatives: • Location of measurement (USA: 10, Xelsius: 30) • Type of asset (HDD, CDD, precipitation, …) • Strike • Time period • Tick size

14. Idealised example • Risk analysis: • Electricity Enterprises finds out: electricity sales drop by 400 MWh/day if temperature rises by 1°C • monthly loss: (31 400  18) = 223.200 € • ave. 1969-1998: HDD(Frankfurt) = 686.4 • in 18/30 years: HDD(Frankfurt) < 500 • Contract: • Tick size: (400  18) = 7200 € • Strike: 500 HDD • Cap: 100 HDD  720.000 € • premium: 120.000 €

15. Idealised example • if winter is cold (HDD > 500)  no payment • if winter moderately warm: option “in the money” • break even: 483.3 HDD • if winter is extremely warm: cap limits payment

16. Market • Hedger: energy, agriculture, food and drink industry, building, tourism, ...  management of exogenous risks • Risk taker: (re-)insurance companies, (investment) banks, energy suppliers, ...  diversified portfolio, balance of risks

17. Market • Market places: • Chicago Mercantile Exchange • London International Financial Futures and Options Exchange (LIFFE) • Eurex (Frankfurt)  ‘xelsius.com’

18. Market • CME expects that the products are not dealt by end customers but by risk traders  secondary market trader (online-) broker stock exchange (re-) insurance companies (investment-) banks

19. Market • price model • Black/Scholes model, accepted for option prices, is not applicable • no other widely accepted price model  premiums not transparent, may vary by a factor 10! possible hedgers are discouraged from entering the market • possible ‘widely accepted price model’ must reflect reality, otherwise market prices and economic cost of ‘weather’ differ

20. Market • market needs to be ‘complete’ • “Any payoff vector [...] may be realised.” • number of traded derivatives matches at least the number of uncertainties (meteorological parameter, time period, place of measurement, ...)

21. Use of meteorology • Listed under “problem fields”! • methods require an estimate on the variability of the weather ‘variable’ • generally taken from ‘historic data’ • pricing may depend on length of ‘historic times series’ • 30 years seem to be generally accepted • station data from national weather services is strictly preferred

22. Use of meteorology • Problems like ‘heat islands’ or relocation of stations are known, data needs to be corrected • stationarity of the stochastic of the weather variable not generally given • higher confidence is given to more recent measurements

23. Use of meteorology • Meteorological data needs to be of high quality, cheap and quickly/easily available

24. Use of meteorology • Problem: connection between DD value and business performance is often only weak • profit dependent on economic factors • external: economic cycles, general social/economic changes, ... • internal: higher efficiency, new markets, ...

25. Use of meteorology • HDD is a “bad” predictor for the predictand business performance • use of additional meteorological information reduces the amount of unexplained variance Unexplained variance: 42 % Unexplained variance: 61 %

26. Use of meteorology • Disadvantage of using additional meteorological data (e.g. model output, objective analysis): number of control variables increases  number of different ‘markets’ increases  liquidity decreases • Generally no interest in more complex meteorological data than station values. • “Clash of Cultures”

27. Use of meteorology • Specific market traders (e.g. re-insurance companies) may have special interest in more complex meteorological methods • would reduce risk, increase profit (especially, if market prices are based on less appropriate methods) • Methods include seasonal prediction and Monte Carlo modelling • !!!TOP SECRET!!! • Reduces possibility for a generally accepted pricing method

28. Summary and Outlook • Weather derivatives: Measured weather is traded like goods • large market for business activities with a dependency on weather • Most common: HDD and CDD as integrals over period (month, season) • trade market established in Chicago in 1997, difficult start in London, stagnation in Frankfurt

29. Summary and Outlook • Success of trading weather derivatives relies on the simplicity of the products • needed for liquidity of market and accepted pricing method • simple statistical use of plain ‘weather’ measurements hardly appropriate to reflect dependency on weather

30. Summary and Outlook • Trade with weather derivatives in the USA connected with liberalisation of energy market and thus increased competition • need to manage risk of energy suppliers/traders • need to react to energy consumers needs • Energy market in Germany is only partly liberalised, competition is low • little need to compete for the consumers • relatively large regions make it possible to manage risk within the enterprise