High-frequency realized stochastic volatility model

Toshiaki Watanabe and Jouchi Nakajima

Journal of Empirical Finance, 2024, vol. 79, issue C

Abstract: A new high-frequency realized stochastic volatility model is proposed. Apart from the standard daily-frequency stochastic volatility model, the high-frequency stochastic volatility model is fit to intraday returns by extensively incorporating intraday volatility patterns. The daily realized volatility calculated using intraday returns is incorporated into the high-frequency stochastic volatility model by considering the bias in the daily realized volatility caused by microstructure noise. The volatility of intraday returns is assumed to consist of the autoregressive process, the seasonal component of the intraday volatility pattern, and the announcement component responding to macroeconomic announcements. A Bayesian method via Markov chain Monte Carlo is developed for the analysis of the proposed model. The empirical analysis using the 5-minute returns of E-mini S&P 500 futures provides evidence that our high-frequency realized stochastic volatility model improves in-sample model fit and volatility forecasting over the high-frequency stochastic volatility model.

Keywords: Bayesian analysis; High-frequency data; Markov chain Monte Carlo; Realized volatility; Stochastic volatility model; Volatility forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 C58 G17 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:79:y:2024:i:c:s0927539824000938

DOI: 10.1016/j.jempfin.2024.101559

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