19082589 Weather Derivative

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Weather Derivatives:

Risk Management

Predicting rain doesn’t count; Building arks does

Warren Buffett, Australian Financial Review,11 March 2002.



AGENDA Introduction

When to use it ?

How to use it – Terminologies

Pricing Models

Indian Side



Introduction and Importance In 1997 the first over-the-counter (OTC) weather derivative

trade took place, and the field of weather risk management wasborn.

The world's first exchange traded weather derivative begantrading on September 22, 1999 at the CME

20% of the U.S. economy is directly affected by the weather

Weather risk is one of the biggest uncertainties facing any

business

A mild winter ruins a ski season, dry weather reduces cropyields, & rain shuts-down entertainment & construction

Till now Energy Companies are major player





Tools available Weather Insurance

Weather Derivatives (since 1997)

When to use what?



Weather Measure “HDD and CDD”

They are the number of degree by which the average temperature isbelow or above a base temperature

Daily HDD = max(0, daily avg. temp – base temp)

Daily CDD = max(0, base temp - daily avg. temp)



Types of Weather Derivatives Swaps: Payoff = [Min {P($/DD)*Max(ST-X,0), h}]-

[Min {P($/DD)*Max(X-ST,0), h}]

Collars: Payoff = [Min {P($/DD)*Max(ST-K1,0), h}]-[Min {P($/DD)*Max(K2-ST,0), h}]

Puts (floors): Payoff = P($/DD)*Max(X-ST,0)

Calls (caps): Payoff = P($/DD)*Max(ST-X,0)



Applications: ICE CREAM



Example Problem: The municipality of Fort Wayne,

IN has spent $3,000,000 to provide for snowremoval for the upcoming winter. This moneywill fund the equipment and labor to remove12 inches of snow. Because of overtime rules,

the municipal ity estimates that everyadditional1/2 inch of snow leads to anadditional $250,000 of snow removal costs.



Removal Costs With & Withoutthe Call

Inches of With Without

Probability Snow Call Call

4.0% 6 3,500,000 3,000,000

5.0% 7 3,500,000 3,000,000

7.0% 8 3,500,000 3,000,0009.0% 9 3,500,000 3,000,000

10.0% 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



Snowfall Call OptionCall Option Features

Period = Nov-Mar

Strike = 12 inches

Limit = 20 inches

Tick= $250,000

Limit = $4,000,000

Price = $500,0002.5

3.0

3.5

4.0

4.5

5.0

9 12 15 18

Inches of Snow

R e m o v a l C o s t ( M i l l i o n s )

Hedged Costs

Unhedged Costs

Solution: A Snowfall call option which pays $250,000 per 1/2 inchof snowfall above a strike of 12 inches to a maximum of 20 inches



Existing Pricing Models Arbitrage – Free Pricing

Actuarial pricing method

Consumption Based Pricing









Alternate Pricing model

Apply Structure to Empirical Data

NCDC Historical Database

Adjust the Historical Data

Apply Derivative Structure to AdjustedData



Data Adjustments

Station Changes

Instrumentation

Location Trends

Global Climate Cycles

Urban Heat Island Effect ENSO Cycles

Forecasting



Phoenix CDD Data - Adjusted



Phoenix CDD Call Graph

2200

2400

2600

2800

3000

3200

3400

3600

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9



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 UnadjustedData:

$826,000

Based on AdjustedData:

$1.3 mil



Calculating the Payoff

Fit a Probability Distribution to Adjusted Data after simulation

Apply the formula

Pr- expected payoff of CDD option;

Dpu- Dollars per unit;rd- rate of interest;t –time to expiration;Str-strike;CDDmax= Maximal payout/ Dpu+Str;P(CDD)- frequency function.



Thank You

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19082589 Weather Derivative