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Volatility estimation via hidden Markov models

Listed author(s):
  • Rossi, Alessandro
  • Gallo, Giampiero M.

In this paper we suggest a convenient way to obtain parameter estimates of a discrete state hidden Markov volatility process within a framework consistent with observed option prices and stochastic volatility. Relative to similar proposals, we simplify the model estimation by resorting to some parametric approximation of the model in a maximum likelihood context. We show how correlation between returns and volatility innovations can be easily accommodated within this framework. Empirical applications illustrate model search strategies for the SP500 stock index, comparing the performances to a standard GARCH model.

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File URL: http://www.sciencedirect.com/science/article/pii/S0927-5398(05)00075-7
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Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 13 (2006)
Issue (Month): 2 (March)
Pages: 203-230

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Handle: RePEc:eee:empfin:v:13:y:2006:i:2:p:203-230
Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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