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1 A MERICAN R E 4. Weather Derivatives. Sean Devlin ACAS, MAAA CAS Annual Meeting November 1999. Topics. What is the Product Who are the Customers How is the Business Transacted How is the Deal Priced What are the Risk Management Controls Future. Product. - PowerPoint PPT Presentation
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Weather Derivatives
Sean Devlin ACAS, MAAACAS Annual Meeting
November 1999
1AMERICAN RE4
Topics What is the Product
Who are the Customers
How is the Business Transacted
How is the Deal Priced
What are the Risk Management Controls
Future
Product
Weather Derivatives provide coverage for the risk that the weather is different from the historical averages for a period of time
Risks covered Average Temperature - HDDs/CDDs
Abnormal Temperature - # Days above 90F
Precipitation/Snowfall
Snowpack
Windspeed
Riverflow
Barometric Pressure
Humidity
Combination of two or more of the above
Customers Energy Suppliers
Utilities
Municipalities
Individual Corporations
Agricultural Products
Airlines
Clothing Manufacturers and Retailers
Resorts
Beverage Companies
How the Business is Transacted Each contract has a stated limit
Risk is actively managed, traded and hedged
Transacted through SEC-licensed broker-dealer on public exchanges and in private transactions
Why Not Use Insurance Policies?
Insurers and reinsurers in the market are at a significant disadvantage due to:
More cumbersome and expensive insurance transaction.
Inability to hedge and manage risk efficiently.
No access to complete market data and trading strategies or other players.
Transformed Deals
Electric Company Bermuda Re
Bermuda ReAmerican Re
ISDA Agreement
Insurance Policy
Reinsurance Treaty
Sample Deal
Problem: Phoenix Energy Company knows during hotter summers, the cost of producing abnormal amounts of electricity is extremely expensive. The company estimates that it loses $25,000 for every Cooling Degree Day (CDD*) above a certain threshold.
Solution: Company takes out a CDD call option with an attachment point of cumulative 4600 CDDs. For every CDD above 4600, AmRe pays $25,000 with a limit of $10M.
The temperature reference station is Phoenix Sky Harbor Airport.
*CDD = Average Daily Temperature - 65
Pricing: Underlying Data
Collect and adjust data. Coverage is based on measured temperatures at fixed locations.
Time series needs to be adjusted due to biases
The Key to Pricing is Understanding the Data
Fit a distribution. Use adjusted measurements to determine the probability distribution of temperature index per season
Step 1:
Step 2:
Pricing: Underlying Data
Time series needs to be adjusted due to bias in:
Surrounding environment
Measuring instrument
Climate change
The Key to Pricing is Understanding the Data
No Loss Loss
Apply Contract Structure.
Determine Loss Distribution and Premium.
Obtain the loss distribution using transformed data obtained in Step 3
Determine mean and standard deviation of loss distribution
Determine coverage premium by using a risk load factor that is a function of mean payoff, standard deviation, frictional costs, long term climate forecast and marginal impact on portfolio.
AttachmentPoint
Limit
Pricing: Loss Distribution and PremiumStep 3:
Step 4:
Risk LoadMean
Premium
Pricing: Methodology Black-Scholes Versus Actuarial-Based Pricing
“Do the Black-Scholes Pricing Assumptions Apply to Weather Covers?”
Assumptions
•Is the market liquid?
•Are the mean and standard deviation time-independent?
•Do arbitrage conditions exist (Put-Call parity)?
•Is the underlying asset traded?
•Does a lognormal distribution of the underlying asset exist?
Applicable
•No (?)
•No
•No
•No
•No
Actuarial Pricing Method is Most Appropriate
Puts and Calls
Put Cover: Covers for accumulated index (CDD or HDD) being below a level.
Call Cover: Covers for accumulated index (CDD or HDD) being Above a level.
Trading Objectives Objective is to establish a climate-neutral
portfolio during a given season:
profit scenarios are slightly skewed but do not depend on very warm or very cold temperatures
we do not speculate on temperature
We seek to realize profits through:
taking advantage of the disparity of prices in geographic regions
creating positions by combining two or more contracts
1AMERICAN RE4
Underwriting and Investment Guidelines The portfolio is subject to maximum trading limits
based on Maximum Potential Economic Loss (MPEL) and Value at Risk (VaR).
MPEL aggregates the stated limit of all contracts. VaR reduces MPEL by taking into account the offsetting nature of correlated events.
The portfolio is also subject to certain other guidelines: individual transaction size counterparty exposure limits contract length minimum years of related weather data for analysis regional exposure limits
1AMERICAN RE4
Portfolio Management
LogNormalDistribution@2,0.85D
LOSS DISTRIBUTION
Loss Size
Pro
babi
lity
Mean PML
1.0% of area to right of PML
Portfolio Risk Metrics•Expected Loss
– Measure for mean of loss distribution•Expected Loss Ratio
– Expected loss normalized by premiums: Mean/Total Premium•Median
– 50% of losses will be less than this value; 50% are greater•Probable Maximum Loss (PML)
– Measure for the tail of the loss distribution– Loss exceeded once every 100 years:– More appropriate measure of risk than variance for skewed distributions
Median
Portfolio’s Risk & RewardScenario 1 2 3 4Net Premium ('000 USD) 5,000 10,000 20,000 33,000Limit ('000 USD) 34,000 63,000 125,000 230,00050 Yr PML ('000 USD) 10,860 16,540 30,068 45,829100 Yr PML ('000 USD) 12,147 17,786 33,253 49,711Std Dev ('000 USD) 2,857 4,676 7,219 10,678Mean Loss ('000 USD) 3,000 6,000 12,000 19,800CV = St Dev / Mean 95% 78% 60% 54%Tech. Gain ('000 USD) 2,000 4,000 8,000 13,200
Analyzed four portfolios, varying in spread of risk
Quantified the risk and reward parameters:
Capacity Consumption
Portfolio Uncertainty
Technical Gain
Reward to Risk Ratio
Portfolio Reward - Premium less the expected loss
Portfolio Risk - Probable loss at a return period of 100 years
Reward/Risk
Coefficient of Variation
Coefficient of Variation (CV) - Ratio between portfolio’s standard deviation and its expected loss
CV reflects level of uncertainty or variability of the portfolio
Plot indicates that CVs decrease as capacity / volume of premiums increases, allowing for an optimal portfolio mix
WEATHER MARKET PLAYERS
Power Distributors
Natural Gas Distributors
Heating Oil Distributors
Energy Producers
Trading Companies
Investment Banks
Energy Marketers
Reinsurance Companies
Commercial Banks
Providers
End Users
BROKER or DIRECT
Energy Consumers
Other Applications
Combining weather risk within overall risk management program. Dual trigger or combined retention programs.
Combining weather risk(volume) risk with commodity(price) risk, i.e. gas, oil, electricity.
Weather-linked debt to finance power generation equipment.
Offered as insurance or reinsurance contracts.
Weather Market Outlook Continued growth in frequency of
transactions
Faster deal negotiations and closings
Larger sized, multi-year deals
Short-term monthly/weekly markets (e.g. CME)
International expansion
More participation by banks, financial intermediaries and consultants
More end user hedging participation
Retail weather products and services