Choosing the best volatility models: the model confidence set approach
Peter Hansen (),
Asger Lunde () and
James Nason ()
No 2003-28, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta
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.
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Journal Article: Choosing the Best Volatility Models: The Model Confidence Set Approach* (2003)
Working Paper: Choosing the Best Volatility Models:The Model Confidence Set Approach (2003)
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