Choosing the Best Volatility Models: The Model Confidence Set Approach
Brown University Economics Working Paper No. 03-05; FRB of Atlanta Working Paper No. 2003-28
26 Pages Posted: 22 May 2003
Date Written: October 2003
This paper applies the model confidence sets (MCS) procedure to a set of volatility models. A MSC is analogous to a confidence interval of 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 fifty-five 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 that shows the MCS captures the superior models across a range of significance levels. When we benchmark the MCS relative to a Bonferroni bound, this bound delivers inferior performance.
Keywords: Forecasting, Model Selection, Multiple Comparisons, Data Mining
JEL Classification: C12, C19, C44, C52, C53
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