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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