Department of Economics and Business Economics

Realized Semicovariances

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

Realized Semicovariances. / Bollerslev, Tim; Li, Jia; Patton, Andrew J.; Quaedvlieg, Rogier.

In: Econometrica, Vol. 88, No. 4, 07.2020, p. 1515-1551.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Bollerslev, T, Li, J, Patton, AJ & Quaedvlieg, R 2020, 'Realized Semicovariances', Econometrica, vol. 88, no. 4, pp. 1515-1551. https://doi.org/10.3982/ECTA17056

APA

Bollerslev, T., Li, J., Patton, A. J., & Quaedvlieg, R. (2020). Realized Semicovariances. Econometrica, 88(4), 1515-1551. https://doi.org/10.3982/ECTA17056

CBE

Bollerslev T, Li J, Patton AJ, Quaedvlieg R. 2020. Realized Semicovariances. Econometrica. 88(4):1515-1551. https://doi.org/10.3982/ECTA17056

MLA

Vancouver

Bollerslev T, Li J, Patton AJ, Quaedvlieg R. Realized Semicovariances. Econometrica. 2020 Jul;88(4):1515-1551. https://doi.org/10.3982/ECTA17056

Author

Bollerslev, Tim ; Li, Jia ; Patton, Andrew J. ; Quaedvlieg, Rogier. / Realized Semicovariances. In: Econometrica. 2020 ; Vol. 88, No. 4. pp. 1515-1551.

Bibtex

@article{9da114b793e549d085c3eb5b292efb72,
title = "Realized Semicovariances",
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 = "co-jumps, High-frequency data, realized variances, semicovariances, volatility forecasting",
author = "Tim Bollerslev and Jia Li and Patton, {Andrew J.} and Rogier Quaedvlieg",
note = "Publisher Copyright: {\textcopyright} 2020 The Econometric Society Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = jul,
doi = "10.3982/ECTA17056",
language = "English",
volume = "88",
pages = "1515--1551",
journal = "Econometrica",
issn = "0012-9682",
publisher = "Wiley-Blackwell Publishing Ltd.",
number = "4",

}

RIS

TY - JOUR

T1 - Realized Semicovariances

AU - Bollerslev, Tim

AU - Li, Jia

AU - Patton, Andrew J.

AU - Quaedvlieg, Rogier

N1 - Publisher Copyright: © 2020 The Econometric Society Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/7

Y1 - 2020/7

N2 - 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.

AB - 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.

KW - co-jumps

KW - High-frequency data

KW - realized variances

KW - semicovariances

KW - volatility forecasting

UR - http://www.scopus.com/inward/record.url?scp=85088647303&partnerID=8YFLogxK

U2 - 10.3982/ECTA17056

DO - 10.3982/ECTA17056

M3 - Journal article

AN - SCOPUS:85088647303

VL - 88

SP - 1515

EP - 1551

JO - Econometrica

JF - Econometrica

SN - 0012-9682

IS - 4

ER -