PRICING AND HEDGING OF TEMPERATURE DERIVATIVES IN A MODEL WITH MEMORY

Markus Hess ()
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Markus Hess: Department of Mechanical and Process Engineering, University of Kaiserslautern-Landau, Gottlieb-Daimler-Straße, 67663 Kaiserslautern, Germany

International Journal of Theoretical and Applied Finance (IJTAF), 2024, vol. 27, issue 01, 1-34

Abstract: With view on global warming and the ongoing climate change, weather derivatives play an increasingly important role for many companies and financial investors, as they constitute useful hedging instruments against disadvantageous weather conditions. In this paper, we present a new temperature model based on generalized Langevin equations driven by Lévy processes. The proposed arithmetic approach captures numerous stylized facts of empirical temperature behavior like seasonal variations, time-dependent volatilities, memory effects, heavy tails and skewness. We further derive a representation for the related meteorological temperature forecast curve and infer the risk-neutral price dynamics of temperature derivatives like CAT, CDD and HDD futures. We finally deduce the minimal variance hedging portfolio in a specific temperature futures market by an application of a stochastic maximum principle and present several practical examples.

Keywords: Temperature futures pricing; temperature forecast curve; minimal variance hedging; generalized Langevin equation; stochastic differential equation; Lévy process; Malliavin calculus; Clark–Ocone formula; stochastic maximum principle; Fourier/Laplace transform (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219024923500310

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