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A comparison of temperature density forecasts from GARCH and atmospheric models

Author

Listed:
  • Roberto Buizza

    (European Centre for Medium-range Weather Forecasts, Reading, UK)

  • James W. Taylor

    (Saïd Business School, University of Oxford, UK)

Abstract

Density forecasts for weather variables are useful for the many industries exposed to weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. The distribution of the scenarios can be used as a density forecast, which is needed for pricing weather derivatives. We consider one to 10-day-ahead density forecasts provided by temperature ensemble predictions. More specifically, we evaluate forecasts of the mean and quantiles of the density. The mean of the ensemble scenarios is the most accurate forecast for the mean of the density. We use quantile regression to debias the quantiles of the distribution of the ensemble scenarios. The resultant quantile forecasts compare favourably with those from a GARCH model. These results indicate the strong potential for the use of ensemble prediction in temperature density forecasting. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
  • Handle: RePEc:jof:jforec:v:23:y:2004:i:5:p:337-355
    DOI: 10.1002/for.917
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    References listed on IDEAS

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    1. 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.
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    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
    6. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    7. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
    8. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
    9. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
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    Cited by:

    1. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, Open Access Journal, vol. 5(3), pages 1-37, March.
    2. repec:eee:ejores:v:261:y:2017:i:2:p:715-734 is not listed on IDEAS
    3. Eirini Konstantinidi & Gkaren Papazian & George Skiadopoulos, 2015. "Modeling the Dynamics of Temperature with a View to Weather Derivatives," World Scientific Book Chapters,in: THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 17, pages 511-544 World Scientific Publishing Co. Pte. Ltd..
    4. Huisman, Ronald, 2008. "The influence of temperature on spike probability in day-ahead power prices," Energy Economics, Elsevier, vol. 30(5), pages 2697-2704, September.

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