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 was born.
The world's first exchange traded weather derivative began trading 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 crop yields, & rain shuts-down entertainment & construction
Till now Energy Companies are major player
Importance
Companies whose earnings fluctuate wildly receive unsympathetic hearings from banks and potential investors.
As a tool to HEDGE; Not Correlated at all with trends in Financial Market.
Notional Value of $45 billion as of 2006 from $22 billion in 2005.
In Asia, the number of weather contracts traded rose to 6,837 for the current year, compared to 1,940 in 2007-2008.
A farmer's common complaint "Everybody talks about the weather, but nobody does anything about it" will soon become a thing of the past with weather derivatives
Tools available
Weather Insurance
Weather Derivatives (since 1997)
When to use what?
Weather Measure “HDD and CDD” They are the number of degree by which the average temperature is below or above a base temperature
Daily HDD = max(0, daily avg. temp – base temp)
Daily CDD = max(0, base temp - daily avg. temp)
Types of Weather Derivatives
Swaps: Payoff = [Min {P($/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 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.
Removal Costs With & Without the Call Probability 4.0% 5.0% 7.0% 9.0% 10.0% 12.0% 15.0% 12.0% 10.0% 8.0% 4.0% 3.0% 1.0%
Inches of Snow 6 7 8 9 10 11 12 13 14 15 16 17 18
With Call 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000
Without Call 3,000,000 3,000,000 3,000,000 3,000,000 3,000,000 3,000,000 3,000,000 3,500,000 4,000,000 4,500,000 5,000,000 5,500,000 6,000,000
Average
12
3,500,000
3,465,000
Snowfall Call Option 5.0
Period = Nov-Mar Strike = 12 inches Limit = 20 inches Tick= $250,000 Limit = $4,000,000 Price = $500,000
Removal Cost (Millions)
Call Option Features
4.5
Unhedged Costs
4.0 3.5 3.0
Hedged Costs
2.5 9
12
15
Inches of Snow
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
18
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 Adjusted Data
Data Adjustments
Station Changes
Trends
Instrumentation Location Global Climate Cycles Urban Heat Island Effect
ENSO Cycles Forecasting
Phoenix CDD Data Adjusted Phoenix CDD Data Adjusted for Trend 3800
3600 3400
3200 3000
2800 2600
2400 2200
Original
Adjusted
Phoenix CDD Call Graph 3600 3400 3200 3000 2800 2600 2400
19 49 19 54 19 59 19 64 19 69 19 74 19 79 19 84 19 89 19 94 19 99
2200
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:
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
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 was born.
The world's first exchange traded weather derivative began trading 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 crop yields, & rain shuts-down entertainment & construction
Till now Energy Companies are major player
Importance
Companies whose earnings fluctuate wildly receive unsympathetic hearings from banks and potential investors.
As a tool to HEDGE; Not Correlated at all with trends in Financial Market.
Notional Value of $45 billion as of 2006 from $22 billion in 2005.
In Asia, the number of weather contracts traded rose to 6,837 for the current year, compared to 1,940 in 2007-2008.
A farmer's common complaint "Everybody talks about the weather, but nobody does anything about it" will soon become a thing of the past with weather derivatives
Tools available
Weather Insurance
Weather Derivatives (since 1997)
When to use what?
Weather Measure “HDD and CDD” They are the number of degree by which the average temperature is below or above a base temperature
Daily HDD = max(0, daily avg. temp – base temp)
Daily CDD = max(0, base temp - daily avg. temp)
Types of Weather Derivatives
Swaps: Payoff = [Min {P($/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 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.
Removal Costs With & Without the Call Probability 4.0% 5.0% 7.0% 9.0% 10.0% 12.0% 15.0% 12.0% 10.0% 8.0% 4.0% 3.0% 1.0%
Inches of Snow 6 7 8 9 10 11 12 13 14 15 16 17 18
With Call 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000 3,500,000
Without Call 3,000,000 3,000,000 3,000,000 3,000,000 3,000,000 3,000,000 3,000,000 3,500,000 4,000,000 4,500,000 5,000,000 5,500,000 6,000,000
Average
12
3,500,000
3,465,000
Snowfall Call Option 5.0
Period = Nov-Mar Strike = 12 inches Limit = 20 inches Tick= $250,000 Limit = $4,000,000 Price = $500,000
Removal Cost (Millions)
Call Option Features
4.5
Unhedged Costs
4.0 3.5 3.0
Hedged Costs
2.5 9
12
15
Inches of Snow
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
18
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 Adjusted Data
Data Adjustments
Station Changes
Trends
Instrumentation Location Global Climate Cycles Urban Heat Island Effect
ENSO Cycles Forecasting
Phoenix CDD Data Adjusted Phoenix CDD Data Adjusted for Trend 3800
3600 3400
3200 3000
2800 2600
2400 2200
Original
Adjusted
Phoenix CDD Call Graph 3600 3400 3200 3000 2800 2600 2400
19 49 19 54 19 59 19 64 19 69 19 74 19 79 19 84 19 89 19 94 19 99
2200
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:
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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