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
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
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1984
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1980
1978
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1974
1972
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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.
Humanitarian Aid: Ethiopian StructureHumanitarian Aid: Ethiopian Structure
Location: 26 Weather Stations
(Agricultural Areas Only)Start Date: 11th March 2006End Date 31st October 2006
Humanitarian Aid: Ethiopian StructureHumanitarian Aid: Ethiopian Structure
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
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1963
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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