Covariance Forecasting in Equity Markets

Symitsi, Efthymia, Symeonidis, Lazaros, Kourtis, Apostolos and Markellos, Raphael (2018) Covariance Forecasting in Equity Markets. Journal of Banking and Finance, 96. pp. 153-168. ISSN 0378-4266

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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.

Item Type: Article
Uncontrolled Keywords: covariance forecasting,high-frequency data,implied volatility,asset allocation,risk-return trade-of
Faculty \ School: Faculty of Social Sciences > Norwich Business School
Depositing User: LivePure Connector
Date Deposited: 29 Aug 2018 09:32
Last Modified: 30 Sep 2021 11:50
DOI: 10.1016/j.jbankfin.2018.08.013

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