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© Zurich Re Weather Risk Management David Molyneux, FCAS.

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Presentation on theme: "© Zurich Re Weather Risk Management David Molyneux, FCAS."— Presentation transcript:

1 © Zurich Re Weather Risk Management David Molyneux, FCAS

2 © Zurich Re 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

3 © Zurich Re 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

4 © Zurich Re Weather Derivative Customers Utilities and energy companies Agricultural companies Municipalities Seasonal Clothing Manufacturers Ski/Beach Resort Operators Golf Course Management Companies Beverage Companies & Distributors

5 © Zurich Re Weather Derivative Risks Average Temperature - HDDs/CDDs Abnormal Temperature - # of Days above 100F Precipitation or snowfall Humidity Wind speed Riverflow Combinations of the above

6 © Zurich Re 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)

7 © Zurich Re 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

8 © Zurich Re 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

9 © Zurich Re Option Payoffs

10 © Zurich Re 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.

11 © Zurich Re 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

12 © Zurich Re Snowfall Distribution

13 © Zurich Re Removal Costs With & Without the Call

14 © Zurich Re 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

15 © Zurich Re 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!!!

16 © Zurich Re Data Adjustments Station Changes –Instrumentation –Location Trends –Global Climate Cycles –Urban Heat Island Effect ENSO Cycles Forecasting

17 © Zurich Re Phoenix CDD Data

18 © Zurich Re Phoenix CDD Data - Adjusted

19 © Zurich Re Phoenix CDD Call Graph

20 © Zurich Re 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

21 © Zurich Re 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

22 © Zurich Re Portfolio Management Diversify Geographically & Directionally Track Correlations Between Cities Manage Transactional & Aggregate Limits Hedging & Trading Strategies

23 © Zurich Re 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

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