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Regimes and long memory in realized volatility

Author

Listed:
  • Goldman Elena

    (Department of Finance and Economics, Lubin School of Business Pace University, One Pace Plaza, NY, USA)

  • Nam Jouahn

    (Department of Finance and Economics, Lubin School of Business Pace University, One Pace Plaza, NY, USA)

  • Tsurumi Hiroki

    (Department of Economics, Rutgers University, New Brunswick, NJ, USA)

  • Wang Jun

    (Department of Economics and Finance, Baruch College, One Bernard Baruch Way, New York, NY, USA)

Abstract
In this paper we model regimes and long memory in the dynamics of realized volatilities of intraday ETF and stock returns. We estimate threshold fractionally integrated (TARFIMA) models using Bayesian Markov Chain Monte Carlo (MCMC) algorithms with efficient jump. We also introduce a test based on posterior distributions of the mean squared forecast errors for model selection. Our findings are that the TARFIMA model that accounts for a different degree of long memory, persistence and variance in two regimes outperforms ARFIMA and other models using 5 day forecasts.

Suggested Citation

  • Goldman Elena & Nam Jouahn & Tsurumi Hiroki & Wang Jun, 2013. "Regimes and long memory in realized volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 521-549, December.
    • Handle: RePEc:bpj:sndecm:v:17:y:2013:i:5:p:521-549:n:1
    DOI: 10.1515/snde-2012-0018
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    References listed on IDEAS

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