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Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity

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  • Wolfgang Karl Hrdle
  • Maria Osipenko

Abstract

Due to the dependency of the energy demand on temperature, weather derivatives enable the effective hedging of temperature related fluctuations. However, temperature varies in space and time and therefore the contingent weather derivatives also vary. The spatial derivative price distribution involves a risk premium. We employ a pricing model for temperature derivatives based on dynamics modeled via a vectorial Ornstein-Uhlenbeck process with seasonal variation. We use an analytical expression for the risk premia depending on variation curves of temperature in the measurement period. The dependence is exploited by a functional principal component analysis of the curves. We compute risk premia on cumulative average temperature futures for locations traded on CME and fit to it a geographically weighted regression on functional principal component scores. It allows us to predict risk premia for nontraded locations and to adopt, on this basis, a hedging strategy, which we illustrate in the example of Leipzig.

Suggested Citation

  • Wolfgang Karl Hrdle & Maria Osipenko, 2012. "Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity," The Energy Journal, , vol. 33(2), pages 149-170, April.
    • Handle: RePEc:sae:enejou:v:33:y:2012:i:2:p:149-170
    DOI: 10.5547/01956574.33.2.7
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    References listed on IDEAS

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    1. repec:aen:journl:1995v16-02-a04 is not listed on IDEAS
    2. Lee, Yongheon & Oren, Shmuel S., 2009. "An equilibrium pricing model for weather derivatives in a multi-commodity setting," Energy Economics, Elsevier, vol. 31(5), pages 702-713, September.
    3. Jurate saltyte Benth & Fred Espen Benth & Paulius Jalinskas, 2007. "A Spatial-temporal Model for Temperature with Seasonal Variance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(7), pages 823-841.
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