Weather Derivativesnecessity, methods and application

Reinhard HagenbrockSeminar of the

working group on Climate Dynamics

Bonn, 16. Mai 2003

Outline

• “History”

• What is a ‘weather derivative’?

• Idealised example

• Market: players, -places and requirements

• Use of meteorology

• Summary and outlook

“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’

“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)

“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

“History” of weather derivatives

• Starting of weather derivatives: dependency of energy sales on temperature

“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

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

What is a ‘weather derivative’?

• Other indices:– precipitation

• Indices are dealt like goods

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’

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

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

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

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 �

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

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

Market

• Market places:– Chicago Mercantile Exchange– London International Financial Futures and

Options Exchange (LIFFE)– Eurex (Frankfurt) ‘xelsius.com’

Market

• CME expects that the products are not dealt by end customers but by risk traders secondary market

(online-) brokerstock exchange

trader

(re-) insurancecompanies

(investment-)banks

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

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, ...)

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

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

Use of meteorology

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

AvailabilityCost (ongoing data access)

Cost (historic data) Quality

Germany 6 7 4 6.-7.France 6 8 7 9Great Britain 9 9 6 9Netherlands 8 9 6 8Norway 9 9 9 7Sweden 9 7 5 7Spain 6 10 10 5USA 9,5 10 10 9

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, ...

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: 61 %

Unexplained variance: 42 %

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”

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

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

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

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