Weather Derivatives necessity, methods and application

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’


• 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)


• Price risks may generally be managed with options / long term contracts

• “weather risk” generally is a volume risk, price risk should by managed independently


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


• 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


• Other indices:– precipitation

• Indices are dealt like goods


• 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’


• ‘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


• 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


• 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

Weather Derivatives necessity, methods and application

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