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A comparison of temperature density forecasts from GARCH and atmospheric models

Listed author(s):
  • Roberto Buizza

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

  • James W. Taylor

    (Saïd Business School, University of Oxford, UK)

Registered author(s):

    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.

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    File URL: http://hdl.handle.net/10.1002/for.917
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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 23 (2004)
    Issue (Month): 5 ()
    Pages: 337-355

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    Handle: RePEc:jof:jforec:v:23:y:2004:i:5:p:337-355
    DOI: 10.1002/for.917
    Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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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.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    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.
    10. 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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