Regimes and long memory in realized volatility

Goldman Elena (), Nam Jouahn, Tsurumi Hiroki and Wang Jun
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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

Studies in Nonlinear Dynamics & Econometrics, 2013, vol. 17, issue 5, 521-549

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.

Keywords: Bayesian model selection; forecasting; realized volatility; threshold regimes (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/snde-2012-0018

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