A comparison of temperature density forecasts from GARCH and atmospheric models
A comparison of temperature density forecasts from GARCH and atmospheric models
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DOI：
10.1002/for.917
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发表时间：
2004-08
影响因子：
3.4
通讯作者：
James W. Taylor;R. Buizza
中科院分区：
文献类型：
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作者：
James W. Taylor;R. Buizza
天气变量的密度预测对于许多暴露于天气风险的行业都很有用。天气集合预报是从大气模型中生成的，由一个天气变量的多个未来情景组成。情景的分布可以用作密度预测，这是为天气衍生品定价所需的。我们认为，1至10天前的密度预报提供的温度集合预报。更具体地说，我们评估预测的平均值和分位数的密度。集合情景的平均值是密度平均值的最准确预测。我们使用分位数回归来消除集合情景分布的分位数偏差。由此产生的分位数预测相比，从Gesthem模型。这些结果表明集合预报在温度密度预报中的应用具有很大的潜力。版权所有© 2004年约翰威利父子有限公司。
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.