Model Selection for Volatility Prediction

Masayuki Uchida and Nakahiro Yoshida ()
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Masayuki Uchida: Osaka University, Graduate School of Engineering Science
Nakahiro Yoshida: University of Tokyo, Graduate School of Mathematical Sciences

A chapter in The Fascination of Probability, Statistics and their Applications, 2016, pp 343-360 from Springer

Abstract: 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.

Keywords: Non-ergodic diffusions; Stable convergence; Stochastic differential equation; Volatility; Model selection; Information criterion (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-25826-3_16

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DOI: 10.1007/978-3-319-25826-3_16

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