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Sunshine-Factor Model with Treshold GARCH for Predicting Temperature of Weather Contracts

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  • Hélène Hamisultane

    () (EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

Abstract

Climate changes have sparked growing interest for the weather derivatives which are financial contracts relied on a meteorological index and allowing companies to hedge against climate risk. These contracts present the particularity of providing compensation to the buyer when the meteorological index crossed a limit agreed in advance with the seller. In order to evaluate these products and to manage at best the risks associated with their exchange, it is important to be able to accurately predict the evolution of the climate variable. Several processes have been proposed in the literature to model the behaviour of the temperature which is the basis of most of the traded weather instruments. These processes relate mainly to the univariate time series modelling which is founded on the study of the autocorrelation of the stationary variable. But we know that the behaviour of the temperature can be influenced by climatic factors such as rain, wind or sunshine. In our paper, we propose to take into account the impact of sunshine on the temperature as well as the asymmetric effect of the shocks on the volatility by estimating a structural model with a periodic threshold GARCH. We show that this model provides better out-sample forecasts for 30 and 60 days ahead than those obtained by the univariate autoregressive-conditional heteroskedasticity process.

Suggested Citation

  • Hélène Hamisultane, 2008. "Sunshine-Factor Model with Treshold GARCH for Predicting Temperature of Weather Contracts," Working Papers halshs-00355857, HAL.
  • Handle: RePEc:hal:wpaper:halshs-00355857
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00355857
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    References listed on IDEAS

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    1. Fred Espen Benth & Jurate Saltyte-Benth, 2005. "Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(1), pages 53-85.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    4. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Keywords

    Value-at-Risk; weather derivatives; structural model; Markov chain; threshold GARCH; Monte-Carlo simulations; Value-at-Risk.;

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