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Covariance forecasting in equity markets

Symitsi, Efthymia and Symeonidis, Lazaros and Kourtis, Apostolos and Markellos, Raphael (2018) 'Covariance forecasting in equity markets.' Journal of Banking & Finance, 96. 153 - 168. ISSN 0378-4266

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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 riskpremium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks.

Item Type: Article
Uncontrolled Keywords: Covariance forecasting, High-frequency data, Implied volatility, Asset allocation, Risk-return trade-off
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 28 Jan 2020 13:09
Last Modified: 03 Mar 2020 02:00
URI: http://repository.essex.ac.uk/id/eprint/26585

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