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Model Selection and Testing of Conditional and Stochastic Volatility Models

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Author Info

  • Massimiliano Caporin

    (Department of Economics and Management "Marco Fanno", University of Padova)

  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

Abstract

This paper focuses on the selection and comparison of alternative non-nested volatility models. We review the traditional in-sample methods commonly applied in the volatility framework, namely diagnostic checking procedures, information criteria, and conditions for the existence of moments and asymptotic theory, as well as the out-of-sample model selection approaches, such as mean squared error and Model Confidence Set approaches. The paper develops some innovative loss functions which are based on Value-at-Risk forecasts. Finally, we present an empirical application based on simple univariate volatility models, namely GARCH, GJR, EGARCH, and Stochastic Volatility that are widely used to capture asymmetry and leverage.

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File URL: http://www.kier.kyoto-u.ac.jp/DP/DP724.pdf
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Bibliographic Info

Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 724.

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Length: 30pages
Date of creation: Sep 2010
Date of revision:
Handle: RePEc:kyo:wpaper:724

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Related research

Keywords: Volatility model selection; volatility model comparison; non-nested models; model confidence set; Value-at-Risk forecasts; asymmetry; leverage;

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References

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