Choosing the best volatility models: The model confidence set approach

This paper applies the Model Confidence Set (MCS) procedure of Hansen, Lunde, and Nason (2003) to a set of volatility models. A MCS is analogous to confidence interval of a parameter in the sense that the former 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 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. – Forecasting ; Model Selection ; Multiple Comparison ; Data Mining