Improving Volatility Identification and Prediction of Realized Stochastic Volatility Model with Implied volatility

33 Pages Posted: 3 Jun 2019 Last revised: 15 Jul 2020

See all articles by Zehua Zhang

Zehua Zhang

Hunan University

Ran Zhao

San Diego State University

Date Written: August 4, 2019

Abstract

We examine a group of extended realized stochastic volatility (RSV) models with ex-ante volatility information added to the framework. The most advantageous specification is the one with implied volatility (IV) as an explanatory variable in the latent volatility process, which produces an estimated latent volatility almost identical to the realized volatility. Including IV generalizes the traditional AR(1) specification of the volatility process to a market-based process, which captures the dynamics of the daily realized volatility. The purposed RSV model with IV outperforms all benchmark models in out-of-sample return density prediction. The empirical finding is valid for both equity index and major individual stocks for a data sample with daily observations over twenty years.

Keywords: Stochastic volatility, realized volatility, implied volatility, MCMC, Bayesian forecasting

JEL Classification: C11, C15, C22, C52

Suggested Citation

Zhang, Zehua and Zhao, Ran, Improving Volatility Identification and Prediction of Realized Stochastic Volatility Model with Implied volatility (August 4, 2019). Available at SSRN: https://ssrn.com/abstract=3386412 or http://dx.doi.org/10.2139/ssrn.3386412

Zehua Zhang

Hunan University ( email )

Lushan Road, Yuelu District
Changsha, Hunan
China

Ran Zhao (Contact Author)

San Diego State University ( email )

5500 Campanile Dr
San Diego, CA 92182
United States

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