Testing Density Forecasts, With Applications to Risk Management
提出一种检验密度预测的新方法，利用整个预测分布与事后实现的信息构建检验，在样本量小至100时仍有效，适用于金融风险中的区间预测评估。
The forecast evaluation literature has traditionally focused on methods of assessing point forecasts. However, in the context of many models of financial risk, interest centers on more than just a single point of the forecast distribution. For example, value-at-risk models that are currently in extremely wide use form interval forecasts. Many other important financial calculations also involve estimates not summarized by a point forecast. Although some techniques are currently available for assessing interval and density forecasts, existing methods tend to display low power in sample sizes typically available. This article suggests a new approach to evaluating such forecasts. It requires evaluation of the entire forecast distribution, rather than a scalar or interval. The information content of forecast distributions combined with ex post realizations is enough to construct a powerful test even with sample sizes as small as 100.