Choosing the Best Volatility Models:The Model Confidence Set Approach
AbstractThis paper applies the model confidence set (MCS) procedure of Hansen, Lunde and Nason (2003) to a set of volatility models. An MCS is analogous to the confidence interval of a parameter in the sense that it 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 that 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. Copyright 2003 Blackwell Publishing Ltd.
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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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