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Forecasting temperature to price CME temperature derivatives

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  • Dupuis, Debbie J.
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    Abstract

    This paper seeks to forecast temperatures in US cities in order to price temperature derivatives on the Chicago Mercantile Exchange (CME). The CME defines the average daily temperature underlying its contracts as the average of the maximum and minimum daily temperatures, yet all published work on temperature forecasting for pricing purposes has ignored this peculiar definition of the average and sought to model the average temperature directly. This paper is the first to look at the average temperature forecasting problem as an analysis of extreme values. The theory of extreme values guides model selection for temperature maxima and minima, and a forecast distribution for the CME's daily average temperature is found through convolution. While univariate time series AR-GARCH and regression models generally yield superior point forecasts of temperatures, our extreme-value-based model consistently outperforms these models in density forecasting, the most important risk management tool.

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    File URL: http://www.sciencedirect.com/science/article/B6V92-5088M86-2/2/f7a125b92756df1e029a3b1df3d86e88
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 2 (April)
    Pages: 602-618

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    Handle: RePEc:eee:intfor:v:27:y::i:2:p:602-618

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Extreme value theory Density forecasts AR-GARCH models Regression models Weather forecasting Financial markets;

    References

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    1. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
    2. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    3. Vasicek, Oldrich Alfonso, 1977. "Abstract: An Equilibrium Characterization of the Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(04), pages 627-627, November.
    4. Sean D. Campbell & Francis X. Diebold, 2002. "Weather Forecasting for Weather Derivatives," Center for Financial Institutions Working Papers 02-42, Wharton School Center for Financial Institutions, University of Pennsylvania.
    5. 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.
    6. Peter Alaton & Boualem Djehiche & David Stillberger, 2002. "On modelling and pricing weather derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 1-20.
    7. Fred ESPEN Benth & Jurate saltyte Benth, 2007. "The volatility of temperature and pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 7(5), pages 553-561.
    8. Taylor, James W. & Buizza, Roberto, 2006. "Density forecasting for weather derivative pricing," International Journal of Forecasting, Elsevier, vol. 22(1), pages 29-42.
    9. M. Davis, 2001. "Pricing weather derivatives by marginal value," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 305-308.
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