Covariance Forecasting in Equity Markets

Efthymia Symitsi, Lazaros Symeonidis, Apostolos Kourtis, Raphael Markellos

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
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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 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.
Original languageEnglish
Pages (from-to)153-168
Number of pages16
JournalJournal of Banking and Finance
Volume96
Early online date3 Sep 2018
DOIs
Publication statusPublished - Nov 2018

Keywords

  • Covariance forecasting
  • High-frequency data
  • Implied volatility
  • Asset allocation
  • Risk-return trade-of

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