Facebook LinkedIn Twitter
Shop

IAEE Members and subscribers to The Energy Journal: Please log in to access the full text article or receive discounted pricing for this article.

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

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. Keywords: Risk premium, Weather derivatives, Ornstein-Uhlenbeck process, Functional principal components, Geographically weighted regression

Purchase ( $25 )

Energy Specializations: Energy Investment and Finance – Trading Strategies and Financial Instruments; Energy Modeling – Sectoral Energy Demand & Technology; Electricity – Markets and Prices

JEL Codes:
G13 - Contingent Pricing; Futures Pricing; option pricing
Q55 - Environmental Economics: Technological Innovation
D42 - Market Structure, Pricing, and Design: Monopoly

Keywords: Risk premium, Weather derivatives, Ornstein-Uhlenbeck process, Functional principal components, Geographically weighted regression

DOI: 10.5547/01956574.33.2.7

References: View References


Published in Volume 33, Number 2 of The Quarterly Journal of the IAEE's Energy Economics Education Foundation.