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Econometrica Vol. 88 No. 4 2020

Realized Semicovariances

Tim Bollerslev1,2,3; Jia Li1; Andrew J. Patton1; Rogier Quaedvlieg4

1 Department of Economics, Duke University · 2 NBER · 3 CREATES · 4 Department of Business Economics, Erasmus University Rotterdam

open access

Abstract

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.

DOI
10.3982/ecta17056
Volume
88
Issue
4
Pages
1515-1551
Language
en
Sources
bibtex:phds-export.bib crossref openalex

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