Choosing the Best Volatility Models: The Model Confidence Set Approach*
应用模型置信集（MCS）方法比较55个波动率模型，发现基于均方误差时约三分之一模型入选，而基于平均绝对偏差时仅一个VGARCH模型入选，MCS能有效识别最优模型。
Abstract This paper applies the model confidence set (MCS) procedure of Hansen, Lunde and Nason (2003) to a set of volatility models. An MCS is analogous to the confidence interval of a parameter in the sense that it contains the best forecasting model with a certain probability. The key to the MCS is that it acknowledges the limitations of the information in the data. The empirical exercise is based on 55 volatility models and the MCS includes about a third of these when evaluated by mean square error, whereas the MCS contains only a VGARCH model when mean absolute deviation criterion is used. We conduct a simulation study which shows that the MCS captures the superior models across a range of significance levels. When we benchmark the MCS relative to a Bonferroni bound, the latter delivers inferior performance.