Weather Risk Management - Paris EUROPLACE

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Weather Risk ManagementWeather Risk Management

JeanJean--Christophe GARAIX,Christophe GARAIX,Class Manager, Class Manager, WeatherWeather & Agriculture & Agriculture CoversCovers,,

Paris REParis RE

Index Based Weather Covers

Energy

Index Based Reinsurance

Humanitarian Aid

Market Figures

TABLE

Concept of index based weather covers:Concept of index based weather covers:DefinitionsDefinitions

Index based cover :Risk exposure is based on the strong correlation between company’s

sales/income/expenses and daily meteorological variations.

Payout is agreed and triggered only by a weather index, assuming the index is a good proxy to the exposure.

Traditional insurance :Material damage and loss of profit following an exceptional event such

as storm, typhoon, flood…

Covered by “usual insurance” products with an indemnification of incurred and adjusted losses

Concept of index based weather covers: Concept of index based weather covers: Multiple choice of indicesMultiple choice of indices

Index: Critical day

Mean (weighted or not)

Cumulative (with threshold or not)

Combinations

Underlying:Temperature

Precipitation : rainfall, snowfall

Others : wind speed, relative humidity...

Combinations

Concept of index based weather covers:Concept of index based weather covers:Key elements of the productKey elements of the product

A cover is defined by :

The hedge is tailor made :Index is best proxy to the correlation between

the risk exposure and the weather conditions

A period (November to March)An index (cumulated rainfall, HDD, Mean Temp...)A weather station (Paris Orly)A payment structure (Put, Call, Swap, etc)A legal agreement (insurance or derivative)

Energy sector example:Energy sector example:Heating demand fluctuates in winter (1/2)Heating demand fluctuates in winter (1/2)

Energy sector example: Energy sector example: Heating demand fluctuates in winter (2/2)Heating demand fluctuates in winter (2/2)

Weather risk exposures :

Solution :

Sales decrease when winter is too mild (heating demand)Profitability decreases when winter is too cold (costs)

HDD Put against mild winter and a decrease in salesHDD or CTD Call, against a too cold winter and increase in

costs

HDD, Heating Degree Day : Number of degrees below 18°CCTD, Critical Temperature Day : Number of days where temperature < 0°C

Energy sector example:Energy sector example:Wind farm financing secured by wind guaranteeWind farm financing secured by wind guarantee

Weather risk exposure :

Solution :

Index: WPI: Wind Power IndexPut : protection against lack of wind ie drop in power

generationFinancial leverage to decrease the cost of capitalGuaranteed minimum income is a security for creditors

Sales are secured and prices are regulatedWind speed is the key factor: power generation is linked to

wind speed through the power curve

Insurance or energy example: Marketing Insurance or energy example: Marketing Smoothing your Heating/Air conditioning billSmoothing your Heating/Air conditioning bill

Weather risk exposures :

Solution :

When summer is too hot (or the winter is too cold) end user electricity bill increase (Air conditioning/Heating demand)

Monthly or seasonal digital protection against a high average daily maximum temperature. (ex $100 if Av. TMax > 30°C)Client pay off: credit on the next billGuarantee paid by the client or free and provided by the

distributorDevelopment of customer loyalty and marketing differentiation

Index Based Reinsurance: Index Based Reinsurance: Wind trigger and intensity of damagesWind trigger and intensity of damages

Weather risk exposure:

Solution:

Property and motor physical damages due to strong storms are strongly correlated to the wind speed

Annual aggregate excess of loss property treaty10 weather stations spread all over France Each station has a specific weightEach station has a specific wind speed threshold high enough

to capture only strong storms (e.g. 120 Km/h)Each station has a specific wind speed limit high enough from

the threshold (e.g. 160 km/h) as to capture storms severity

Index Based Reinsurance example: Index Based Reinsurance example: Wind trigger and intensity of damagesWind trigger and intensity of damages

Wind speed index at a basket of 10 stations (EUR Km/h)Mean = 2,8 ; SD = 3,7

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

40000000

1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999

Index Based Reinsurance example: Index Based Reinsurance example: Wind trigger and frequency of damages Wind trigger and frequency of damages

Weather risk exposure:

Solution:

Property and motor physical damages due to strong storms are strongly correlated to the wind speed

January-December SL treaty45 weather stations spread over 5 European countries Each station has a specific weightEach station has a specific wind speed threshold high enough

to capture storms (e.g. 110 Km/h)Each station has a specific wind speed limit close to the

threshold (e.g. 115 km/h) as to capture storms frequency only

Index Based Reinsurance example: Index Based Reinsurance example: Wind trigger and frequency of damages Wind trigger and frequency of damages

Seasonal Wind speed index (Km/h) at 45 stationsMean = 155 ; SD = 188

0

200

400

600

800

1 000

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

1960

1958

1956

Humanitarian Aid: Ethiopian StructureHumanitarian Aid: Ethiopian Structure

Target : to establish contingency funding for an effective aid response for the WFP

Rapid availability of funds: More efficient Aid

Vehicle : based on FAO’s crop water balance model and 26 primary weather stations with daily data

Structure defined by crop and by weather station.


Location: 26 Weather Stations

(Agricultural Areas Only)Start Date: 11th March 2006End Date 31st October 2006


0

10 000 000

20 000 000

30 000 000

40 000 000

50 000 000

60 000 000

70 000 000

80 000 000

90 000 000

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

Historique Strike Limit

Ethiopia drought Index value

Market figures: Distribution of Inquiries, by Market figures: Distribution of Inquiries, by Sector of Potential EndSector of Potential End--User (OTC)User (OTC)

69%

7% 5% 4%

2%

13%Energy AgricultureRetailConstructionTransportationOther

46%

12%

7%5% 4%

26%

2005 Survey 2006 Survey

Market figures: Number of Contracts (OTC)Market figures: Number of Contracts (OTC)

01 000

2 0003 000

4 0005 000

2000/1 2001/2 2002/3 2003/4 2004/5 2005/6

Summer Winter

Market figures: Distribution of Total Number of Market figures: Distribution of Total Number of Contracts by Region (OTC)Contracts by Region (OTC)

0500

1,0001,5002,0002,5003,0003,5004,0004,5005,000

2000/1 2001/2 2002/3 2003/4 2004/5 2005/6

NA AsiaEurope Other

Market figures: Distribution of Number of Market figures: Distribution of Number of Contracts by Type (OTC)Contracts by Type (OTC)

0%

20%

40%

60%

80%

100%

2000/1 2001/2 2002/3 2003/4 2004/5 2005/6

OtherRainOther TempCDDHDD

Market figures: Total Notional Value (OTC)Market figures: Total Notional Value (OTC)

$0$500

$1,000$1,500$2,000$2,500$3,000$3,500$4,000$4,500$5,000

2000/1 2001/2 2002/3 2003/4 2004/5 2005/6

Mill

ons

$

Summer Winter

Market figures: Number of Trades on the CMEMarket figures: Number of Trades on the CME

0

5,000

10,000

15,000

20,000

25,000

2002/3 2003/4 2004/5 2005/6

Summer Winter

Market figures: Total Notional Value on the Market figures: Total Notional Value on the CMECME

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

2002/3 2003/4 2004/5 2005/6

Mill

ions

$

CME SummerCME Winter

E-mail: [email protected]: + (33) 1 56 43 98 31 Fax: + (33) 1 56 43 93 70

Salah Dhouib

Françoise Bollotte

David Grégori

Jean-Christophe Garaix

Weather and Agriculture Covers TeamWeather and Agriculture Covers Team

Nicolas Chatelain

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Weather Risk Management - Paris EUROPLACE