Title: Volatility Estimation via Hidden Markov Models
Citation: Journal of Empirical Finance vol. 13 no. 2 p. 203-230
Publisher: Elsevier B.V.
Publication Year: 2006
JRC N°: JRC33013
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC33013
Type: Articles in periodicals and books
Abstract: We propose a stochastic volatility model where the conditional variance of asset returns switches across a potentially large number of discrete levels, and the dynamics of the switches are driven by a latent Markov chain. A simple parameterization overcomes the commonly encountered problem in Markov-switching models that the number of parameters becomes unmanageable when the number of states in the Markov chain increases. This framework presents some interesting features in modelling the persistence of volatility, and that, far from being constraining in data fitting, it performs comparably well as other popular approaches in forecasting short-term volatility.
JRC Directorate:Space, Security and Migration

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