Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity
AbstractDue to dependency of 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 examine functional principal components of temperature variation for this spatial risk premium. We employ a pricing model for temperature derivatives based on dynamics modelled 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2011-013.
Length: 27 pages
Date of creation: Mar 2011
Date of revision:
risk premium; weather derivatives; Ornstein-Uhlenbeck process; functional principal components; geographically weighted regression;
Other versions of this item:
- Wolfgang Karl Hardle and Maria Osipenko, 2012. "Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity," The Energy Journal, International Association for Energy Economics, International Association for Energy Economics, vol. 0(Number 2).
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
This paper has been announced in the following NEP Reports:
- NEP-AGR-2011-03-12 (Agricultural Economics)
- NEP-ALL-2011-03-12 (All new papers)
- NEP-ENE-2011-03-12 (Energy Economics)
- NEP-URE-2011-03-12 (Urban & Real Estate Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Akdeniz Duran, Esra & Härdle, Wolfgang Karl & Osipenko, Maria, 2012.
"Difference based ridge and Liu type estimators in semiparametric regression models,"
Journal of Multivariate Analysis, Elsevier,
Elsevier, vol. 105(1), pages 164-175.
- Esra Akdeniz Duran & Wolfgang Karl Härdle & Maria Osipenko, 2011. "Difference based Ridge and Liu type Estimators in Semiparametric Regression Models," SFB 649 Discussion Papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany SFB649DP2011-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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