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Weather Risk Management

Weather Risk Management. 1999 CAS Annual Meeting. What is Weather Risk?. Uncertainty in earnings or cash flow caused by weather volatility. It can be the largest source of financial uncertainty for many companies - particularly energy companies.

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Weather Risk Management

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  1. Weather Risk Management 1999 CAS Annual Meeting

  2. What is Weather Risk? • Uncertainty in earnings or cash flow caused by weather volatility. • It can be the largest source of financial uncertainty for many companies - particularly energy companies. • There are no physical markets or underlying commodities. • Cannot be controlled (Mother Nature.)

  3. Weather Hedge: Some Examples • Utilities and energy companies can protect volume related-revenue • Agricultural companies can protect earnings from freeze and drought • Golf Course Management Companies can protect against a heavy rain season • Sports Drink Manufacturers can protect against depressed earnings from a cool summer season • Municipalities can protect snow removal budgets, utility costs, and maintain a more stable revenue flow from parks and beaches

  4. Weather Risk Management Structures Climactic protection can be designed for: •Temperature (HDD/CDD) •Precipitation or snowfall •Humidity •Wind speed

  5. 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 F)

  6. How Weather Options 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

  7. Designing Coverage for the Client • Identify the weather exposure, and the weather statistic associated with that risk. • Determine the geographic areas where you have margins at risk. Identify related weather stations. • Select a strike - normally 1/2 to 1 full standard deviation from the mean. Strikes closer to the mean are obviously more expensive, and clients should try to avoid “dollar swapping”.

  8. Basic Types of Option Contracts • Put Option - pays off if the index at the end of term is below the predetermined strike • Call Option - pays off if the index at the end of term is above the predetermined strike • Swap - pays off on both sides (above and below) a predetermined strike • Collar, Strangle, Etc.

  9. Coverage as Insurance or Derivatives Climate coverage can be structured as: •Derivatives - An international swap dealers association (ISDA) confirmation is issued to the client •Insurance - an insurance policy is issued, but still “indemnifies” for loss based on a stated amount per unit of the index

  10. Heating Degree Days - An Example • Problem: A natural gas supplier in the New York City area experiences large decreases in revenue during mild winters due to low heating demand. This significantly impacts earnings per share and puts downward pressure on their US stock price. Each lost heating degree day during the winter (below normal) decreases earnings by $50,000. • Solution: A Heating Degree Day (HDD) put option which pays $50,000 per heating degree day below the pre-determined strike.

  11. New York HDD Put Example Put Option Features Period = Nov-Mar Strike = 4,000 Floor = 3,800 Tick = $50,000 Limit = $10 mil Price = $2.5 mil

  12. Snowfall Cover - An Example • Problem: The municipality of Fort Wayne, IN has budgeted $1,000,000 for snow removal for the upcoming winter period. The budget allows for the removal of 12 inches of snow. The municipality estimates that for every 1/2 inch of snow in excess of 12, they incur $20,000 in additional product and labor costs. • Solution: A Snowfall call option which pays $20,000 per 1/2 inch of snowfall above the pre-determined strike (i.e. 12 inches).

  13. Pricing of Exposure • Historical Data • Data Adjustments • Stochastic Modeling

  14. New York HDD Data

  15. Weather Data • Historical data from NCDC • 50 years or more available for most major cities.

  16. Data Adjustments • Station Changes • Trends • Global Climate Cycles • Urban Heat Island Effect • Population Densities • ENSO Cycles • Forecasting

  17. Stochastic Modeling • Fit Distribution to Weather Data • Monte Carlo Simulation

  18. Issues • State regulatory and legal issues that may arise with a new product • Aggregate exposure management • Overcoming an undeveloped and uneducated primary market • Sophisticated market players • Basis risk associated with selected weather station • Basis risk associated with selected option structure

  19. Insurer/Reinsurer Participation • Source of Premium/Revenue Growth • Diversification of Risk Portfolio • Risk Management Solutions for Clients • Natural outgrowth - insurers already provide risk management solutions for natural events such as Hurricanes and Earthquakes.

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