Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2020
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.
Keywords
High-frequency data, realized variances, semicovariances, co-jumps, volatility forecasting
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometrica
Volume
88
Issue
4
First Page
1515
Last Page
1551
ISSN
0012-9682
Identifier
10.3982/ECTA17056
Publisher
Econometric Society
Citation
Bollerslev, Tim; LI, Jia; Patton, Andrew J.; and Quaedvlieg, Rogier.
Realized semicovariances. (2020). Econometrica. 88, (4), 1515-1551.
Available at: https://ink.library.smu.edu.sg/soe_research/2588
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3982/ECTA17056