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Weather Derivatives
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Introduction
Weather Risk - Revenue or profits that are
sensitive to weather conditions
Weather Derivatives - Financial Products that
allow companies to manage or hedge their
weather related risk exposures
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Weather Derivative Basics
Like Financial derivatives, Weather derivativesare used to hedge risk
The value of a Financial derivative depends onthe value of an underlying asset, index orcommodity
The value of a Weather option depends on the
value of an underlying weather statistic Weather Derivatives protect against abnormal
weather outcomes
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Weather Derivative Customers
Utilities and energy companies
Agricultural companies
Municipalities Seasonal Clothing Manufacturers
Ski/Beach Resort Operators
Golf Course Management Companies Beverage Companies & Distributors
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Weather Derivative Risks
Average Temperature - HDDs/CDDs
Abnormal Temperature - # of Days above 100F
Precipitation or snowfall Humidity
Wind speed
Riverflow Combinations of the above
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Heating and Cooling Degree Days
Most temperature contracts in current practice are based on
Heating Degree Days (HDD) for winter protection, and Cooling
Degree Days (CDD) for summer protection.
HDD = Max (0, 65 F - average temperature in a day)
CDD = Max (0, average temperature in day - 65)
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How Weather Derivatives Work
Pay off is based on a measurable index (CDD,
HDD, etc)
Pay off is based on how the index performs
relative to a trigger or strike value - not on actual
loss
Coverage usually has a defined maximum limit
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Basic Option Terminology
Weather Options pay off when the underlying
weather statistic is above or below a certain
strike value
Put Options - pay if the weather statistic is
below the predetermined strike value
Call Options - pay if the weather statistic is
above the predetermined strike value
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Option Payoffs
Put Payout
-5
0
5
10
Strike
C ll t
-5
0
5
10
Strike
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Simple Example - Snow Removal
Problem: The municipality of Fort Wayne, IN has spent $3,000,000 to
provide for snow removal for the upcoming winter. This money will fund
the equipment and labor to remove 12 inches of snow. Because of
overtime rules, the municipality estimates that every additional1/2 inch of
sn
ow leads to an
addition
al $250,000 of sn
ow removal costs.
Solution: A Snowfall call option which pays $250,000 per 1/2 inch of
snowfall above a strike of 12 inches to a maximum of 20 inches.
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Snowfall Call Option
Call Option Features
Period = Nov-Mar
Strike = 12 inchesLimit = 20 inches
Tick= $250,000
Limit = $4,000,000
Price = $500,000 2.5
3.0
3.5
4.0
4.5
5.0
9 12 15 18
Inches of now
RemovalCost(Millions)
Hedged Costs
Unhedged Costs
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Snowfall Distribution
Snowfall Probability Distribution
0%2%
4%
6%
8%
10%
12%
14%
16%
6 7 8 9 10 11 12 13 14 15 16 17 18
Inches of Snow
Below the Strike Above the Strike
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Removal Costs With & Without the
CallInches o W h W hou
robab y now Ca Ca
4.0 6 3,500,000 3,000,000
5.0 7 3,500,000 3,000,000
7.0 8 3,500,000 3,000,000
9.0 9 3,500,000 3,000,00010.0 10 3,500,000 3,000,000
12.0 11 3,500,000 3,000,000
15.0 12 3,500,000 3,000,000
12.0 13 3,500,000 3,500,000
10.0 14 3,500,000 4,000,000
8.0 15 3,500,000 4,500,0004.0 16 3,500,000 5,000,000
3.0 17 3,500,000 5,500,000
1.0 18 3,500,000 6,000,000
Average 12 3,500,000 3,465,000
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Effect of the Call Purchase
If the total snowfall exceeds 12 inches - the
payoff from the call exactly offsets the
increased cost of snow removal
Fort Wayne guarantees snow removal costs of
$3.5 mil
Variability is reduced - although Expected
Cost is actually higher
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Pricing Weather Derivatives
Method 1 - Apply Structure to Empirical Data
NCDC Historical Database
Adjust the Historical Data
Apply Derivative Structure to Adjusted Data
Method 2 - Simulation
Fit a Probability Distribution to Adjusted Data
Model Stochastically
Black Scholes does not work!!!
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Data Adjustments
Station Changes
Instrumentation
Location
Trends
Global Climate Cycles
Urban Heat Island Effect
ENSO Cycles
Forecasting
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Phoenix CDD Data
Phoenix CDD Data Jun-Sept
2200
2400
2600
2800
3000
3200
3400
3600
1949
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
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Phoenix CDD Data - Adjusted
Phoenix CDD Data Adjusted for Trend
2200
2400
2600
2800
3000
3200
3400
3600
3800
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
Ori in l djust d
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Phoenix CDD Call Graph
2200
2400
2600
2800
3000
3200
3400
3600
1949
1954
1959
1964
1969
1974
1979
1984
1989
1994
1999
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Phoenix CDD Call - Impact of Data Adjustments
CDD Call Structure
Period = Jun-Sept
Strike = 3,200Tick = $10,000
Limit = $2 mil
All Year Expected Loss
Based on Unadjusted Data:
$826,000
Based on Adjusted Data:
$1.3 mil
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Simulation Analysis
Fit a Distribution to Adjusted Data Normal & Lognormal often work for HDD/CDD
Other Statistical Models can be used for Percip, etc.
Fit can be focused on area between strike and
limit
Run simulation analysis
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Portfolio Management
Diversify Geographically & Directionally
Track Correlations Between Cities
Manage Transactional & Aggregate Limits Hedging & Trading Strategies
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Future of the Weather Market
Growth in the Overall Size of the Market
Larger/Multi-Year/More Complex Deals
International Expansion Expanded End User Market
Imbedding Weather Derivatives inInsurance
or Other Types of Contracts