Department of Economics and Business Economics

Realized Semicovariances

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DOI

  • Tim Bollerslev
  • Jia Li, Duke University
  • ,
  • Andrew J. Patton, Duke University
  • ,
  • Rogier Quaedvlieg, Erasmus University Rotterdam

We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high-frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first-order asymptotic results highlight how the same-sign and mixed-sign components load differently on economic information related to stochastic correlation and jumps. The second-order asymptotic results reveal the structure underlying the same-sign semicovariances, as manifested in the form of co-drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross-section of individual stocks to empirically document distinct dynamic dependencies in the different realized semicovariance components. We show that the accuracy of portfolio return variance forecasts may be significantly improved by exploiting the information in realized semicovariances.

Original languageEnglish
JournalEconometrica
Volume88
Issue4
Pages (from-to)1515-1551
Number of pages37
ISSN0012-9682
DOIs
Publication statusPublished - Jul 2020

    Research areas

  • co-jumps, High-frequency data, realized variances, semicovariances, volatility forecasting

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