SEMIPARAMETRIC REPRESENTATION OF A GENERALIZED STOCHASTIC VOLATILITY MODEL AND HIDDEN MARKOV APPROXIMATION

Henry Z. Li
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Henry Z. Li: University of Toronto

No 159, Computing in Economics and Finance 2000 from Society for Computational Economics

Abstract: In this paper I propose a discrete hidden Markov model to approximate a general class of stochastic volatility models with homogenous volatility processes, including the popular Ornstein-Uhlenbeck process. The advantage of this model is that it allows for unknown forms of the volatility data-generating process and thus avoids model-selection problems in empirical time series analysis. Estimation and forecast procedures are introduced, and applications on exchange-rate series are evaluated. I find that this model, although requiring greater computational effort, meets various specification tests better than some GARCH models.

Date: 2000-07-05
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf0:159

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