Choosing the Best Volatility Models:The Model Confidence Set Approach
AbstractThis 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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Bibliographic InfoPaper provided by Brown University, Department of Economics in its series Working Papers with number 2003-05.
Date of creation: 2003
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Postal: Department of Economics, Brown University, Providence, RI 02912
Other versions of this item:
- Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
- Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the best volatility models: the model confidence set approach," Working Paper 2003-28, Federal Reserve Bank of Atlanta.
- NEP-ALL-2003-04-27 (All new papers)
- NEP-ECM-2003-05-15 (Econometrics)
- NEP-ETS-2003-04-27 (Econometric Time Series)
- NEP-FIN-2003-04-27 (Finance)
- NEP-RMG-2003-04-27 (Risk Management)
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