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• W2890124632 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." @default.
• W2890124632 created "2018-09-27" @default.
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• W2890124632 date "2018-11-01" @default.
• W2890124632 modified "2023-10-06" @default.
• W2890124632 title "Covariance forecasting in equity markets" @default.
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• W2890124632 doi "https://doi.org/10.1016/j.jbankfin.2018.08.013" @default.
• W2890124632 hasPublicationYear "2018" @default.
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