A Bayesian Semiparametric Realized Stochastic Volatility Model

Jia Liu
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Jia Liu: Sobey School of Business, Saint Mary’s University, Halifax, NS B3H 3C3, Canada

JRFM, 2021, vol. 14, issue 12, 1-22

Abstract: This paper proposes a semiparametric realized stochastic volatility model by integrating the parametric stochastic volatility model utilizing realized volatility information and the Bayesian nonparametric framework. The flexible framework offered by Bayesian nonparametric mixtures not only improves the fitting of asymmetric and leptokurtic densities of asset returns and logarithmic realized volatility but also enables flexible adjustments for estimation bias in realized volatility. Applications to equity data show that the proposed model offers superior density forecasts for returns and improved estimates of parameters and latent volatility compared with existing alternatives.

Keywords: stochastic volatility; Dirichlet process mixture; realized volatility; density forecast (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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