Covariance forecasting in equity markets
Lazaros Symeonidis (),
Apostolos Kourtis and
Journal of Banking & Finance, 2018, vol. 96, issue C, 153-168
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.
Keywords: Covariance forecasting; High-frequency data; Implied volatility; Asset allocation; Risk-return trade-off (search for similar items in EconPapers)
JEL-codes: C50 C58 G11 G12 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:96:y:2018:i:c:p:153-168
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