Covariance Forecasting in Equity Markets

79 Pages Posted: 16 Jul 2018 Last revised: 4 Oct 2018

See all articles by Efthymia Symitsi

Efthymia Symitsi

University of Leeds - Division of Accounting and Finance; University of East Anglia (UEA), Norwich Business School

Lazaros Symeonidis

University of East Anglia (UEA) - Norwich Business School

Apostolos Kourtis

University of East Anglia (UEA) - Norwich Business School

Raphael N. Markellos

University of East Anglia (UEA) - Norwich Business School

Date Written: June 25, 2018

Abstract

We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions from an empirical asset pricing perspective, and, lead to superior out-of-sample portfolio performance. Overall, a parsimonious Vector Heterogeneous Autoregressive (VHAR) model that involves lagged daily, weekly and monthly realised covariances achieves the best performance out of the competing models. A promising new simple hybrid covariance estimator is developed that exploits option-implied information and high-frequency data while adjusting for the volatility risk premium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed, respectively. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks.

Keywords: covariance forecasting, high-frequency data, implied volatility, asset allocation, risk-return trade-off

JEL Classification: C50, C58, G11, G12

Suggested Citation

Symitsi, Efthymia and Symeonidis, Lazaros and Kourtis, Apostolos and Markellos, Raphael N., Covariance Forecasting in Equity Markets (June 25, 2018). Journal of Banking and Finance, vol. 96, pp. 153-168. Available at SSRN: https://ssrn.com/abstract=3203283 or http://dx.doi.org/10.2139/ssrn.3203283

Efthymia Symitsi

University of Leeds - Division of Accounting and Finance ( email )

Leeds LS2 9JT
United Kingdom

University of East Anglia (UEA), Norwich Business School ( email )

Norwich
United Kingdom

Lazaros Symeonidis (Contact Author)

University of East Anglia (UEA) - Norwich Business School ( email )

Norwich
NR4 7TJ
United Kingdom

Apostolos Kourtis

University of East Anglia (UEA) - Norwich Business School ( email )

Norwich
NR4 7TJ
United Kingdom

Raphael N. Markellos

University of East Anglia (UEA) - Norwich Business School ( email )

Norwich
NR4 7TJ
United Kingdom

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