A Mixed Frequency Stochastic Volatility Model for Intraday Stock Market Returns

Posted: 29 Jan 2016 Last revised: 17 May 2019

See all articles by Jeremias Bekierman

Jeremias Bekierman

University of Cologne - Department of Econometrics and Statistics

Bastian Gribisch

University of Cologne - Department of Econometrics and Statistics

Date Written: August 17, 2016

Abstract

We propose a mixed frequency stochastic volatility (MFSV) model for the dynamics of intraday asset return volatility. In order to account for long-memory we separate stochastic daily and intraday volatility patterns by introducing a long-run component that changes at daily frequency and a short-run component that captures the remaining intraday volatility dynamics. An additional component captures deterministic intraday patterns. We analyze the stochastic properties of the resulting non-linear state-space model both on the daily and the intraday frequency and show how the model can be estimated in a single step using simulated maximum likelihood based on Efficient Importance Sampling (EIS). We apply the model to intraday returns of five New York Stock Exchange traded stocks. The estimation results indicate distinct dynamic patterns for daily and intradaily volatility components, where about 50% of intraday volatility dynamics are explained by the daily component. In-sample diagnostic tests and an out-of-sample forecasting experiment indicate that already the very basic model specification successfully accounts for the complex dynamic and distributional properties of asset returns both on the intraday and the daily frequency.

Keywords: Efficient Importance Sampling, Intraday Stochastic Volatility, Mixed Frequency, Realized Volatility.

JEL Classification: C32, C51, C58, G17

Suggested Citation

Bekierman, Jeremias and Gribisch, Bastian, A Mixed Frequency Stochastic Volatility Model for Intraday Stock Market Returns (August 17, 2016). Available at SSRN: https://ssrn.com/abstract=2724538 or http://dx.doi.org/10.2139/ssrn.2724538

Jeremias Bekierman

University of Cologne - Department of Econometrics and Statistics ( email )

Albertus-Magnus-Platz
Cologne, DE 50923
Germany

Bastian Gribisch (Contact Author)

University of Cologne - Department of Econometrics and Statistics ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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