Weather Derivatives
necessity, methods and application
Reinhard Hagenbrock
Seminar of the
working group on Climate Dynamics
Bonn, 16. Mai 2003
Outline
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“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
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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)
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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:
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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:
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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:
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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
trader
(online-) broker
stock exchange
(re-) insurance
companies
(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
Germany
France
Great Britain
Netherlands
Norway
Sweden
Spain
USA
Availability
6
6
9
8
9
9
6
9,5
Cost (ongoing Cost (historic
data access)
data)
7
4
8
7
9
6
9
6
9
9
7
5
10
10
10
10
Quality
6.-7.
9
9
8
7
7
5
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: 42 %
Unexplained
variance: 61 %
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