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Model Selection for Volatility Prediction

In: The Fascination of Probability, Statistics and their Applications

Author

Listed:
  • Masayuki Uchida

    (Osaka University, Graduate School of Engineering Science
    Japan Science and Technology Agency, CREST)

  • Nakahiro Yoshida

    (University of Tokyo, Graduate School of Mathematical Sciences
    Japan Science and Technology Agency, CREST)

Abstract

We consider a stochastic regression model defined by stochastic differential equations. Based on an expected Kullback-Leibler information for the approximated distributions, we propose an information criterion for selection of volatility models. We show that the information criterion is asymptotically unbiased for the expected Kullback-Leibler information. We also give examples and simulation results of model selection.

Suggested Citation

  • Masayuki Uchida & Nakahiro Yoshida, 2016. "Model Selection for Volatility Prediction," Springer Books, in: Mark Podolskij & Robert Stelzer & Steen Thorbjørnsen & Almut E. D. Veraart (ed.), The Fascination of Probability, Statistics and their Applications, pages 343-360, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-25826-3_16
    DOI: 10.1007/978-3-319-25826-3_16
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