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Volatility Estimation via Hidden Markov Models

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Abstract

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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Bibliographic Info

Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number wp2002_14.

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Length: 33 pages
Date of creation: 17 Jun 2002
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Handle: RePEc:fir:econom:wp2002_14

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Keywords: Stochastic volatility; Hidden Markov; GARCH; Smile-consistent option pricing; Forecasting.;

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Citations

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Cited by:
  1. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
  2. Frömmel, Michael, 2006. "Volatility Regimes in Central and Eastern European Countries' Exchange Rates," Hannover Economic Papers (HEP) dp-333, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  3. L. Grossi & G. Morelli, 2006. "Robust volatility forecasts and model selection in financial time series," Economics Department Working Papers 2006-SE02, Department of Economics, Parma University (Italy).
  4. repec:onb:oenbwp:y:2010:i:2:b:1 is not listed on IDEAS
  5. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
  6. Thomas Lux & Leonardo Morales-Arias & Cristina Sattarhoff, 2011. "A Markov-switching Multifractal Approach to Forecasting Realized Volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy.

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