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Weather Risk Management

David Molyneux, FCAS

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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 derivatives are

used to “hedge” risk

• The value of a Financial derivative depends on the

value of an underlying asset, index or commodity

• 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

Call Payout

-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 snow

leads to an additional $250,000 of snow 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 inches

Limit = 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 Snow

Rem

oval

Cos

t (M

illio

ns)

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 Call

Inches of With Without Probability Snow Call Call4.0% 6 3,500,000 3,000,0005.0% 7 3,500,000 3,000,0007.0% 8 3,500,000 3,000,0009.0% 9 3,500,000 3,000,00010.0% 10 3,500,000 3,000,00012.0% 11 3,500,000 3,000,00015.0% 12 3,500,000 3,000,00012.0% 13 3,500,000 3,500,00010.0% 14 3,500,000 4,000,0008.0% 15 3,500,000 4,500,0004.0% 16 3,500,000 5,000,0003.0% 17 3,500,000 5,500,0001.0% 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

19491952195519581961196419671970197319761979198219851988199119941997

Original Adjusted

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Phoenix CDD Call Graph

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 Call - Impact of Data Adjustments

CDD Call Structure

Period = Jun-Sept

Strike = 3,200

Tick = $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 in Insurance

or Other Types of Contracts