Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/100842 
Year of Publication: 
2003
Series/Report no.: 
Working Paper No. 2003-28
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, GA
Abstract: 
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.
Document Type: 
Working Paper

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