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Semiparametric Representation Of A Generalized Stochastic Volatility Model And Hidden Markov Approximation

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  • Henry Z. Li

    (University of Toronto)

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.

Suggested Citation

  • Henry Z. Li, 2000. "Semiparametric Representation Of A Generalized Stochastic Volatility Model And Hidden Markov Approximation," Computing in Economics and Finance 2000 159, Society for Computational Economics.
  • Handle: RePEc:sce:scecf0:159
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