Abstract
We propose a novel multivariate GARCH model that incorporates realized measures for the covariance matrix of returns. The joint formulation of a multivariate dynamic model for outer-products of returns, realized variances, and realized covariances leads to a feasible approach for analysis and forecasting. The updating of the covariance matrix relies on the score function of the joint likelihood function based on Gaussian and Wishart densities. The dynamic model is parsimonious while the analysis relies on straightforward computations. In a Monte Carlo study, we show that parameters are estimated accurately for different small sample sizes. We illustrate the model with an empirical in-sample and out-of-sample analysis for a portfolio of 15 U.S. financial assets.
| Original language | English |
|---|---|
| Pages (from-to) | 1-32 |
| Number of pages | 32 |
| Journal | Journal of Financial Econometrics |
| Volume | 17 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2019 |
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Keywords
- high-frequency data
- multivariate GARCH
- multivariate volatility
- realized covariance
- score
- Wishart distribution
Cite this
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Realized wishart-garch : A score-driven multi-Asset volatility model. / Gorgi, P.; Hansen, P. R.; Janus, P.; Koopman, S. J.
In: Journal of Financial Econometrics, Vol. 17, No. 1, 01.2019, p. 1-32.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Realized wishart-garch
T2 - A score-driven multi-Asset volatility model
AU - Gorgi, P.
AU - Hansen, P. R.
AU - Janus, P.
AU - Koopman, S. J.
PY - 2019/1
Y1 - 2019/1
N2 - We propose a novel multivariate GARCH model that incorporates realized measures for the covariance matrix of returns. The joint formulation of a multivariate dynamic model for outer-products of returns, realized variances, and realized covariances leads to a feasible approach for analysis and forecasting. The updating of the covariance matrix relies on the score function of the joint likelihood function based on Gaussian and Wishart densities. The dynamic model is parsimonious while the analysis relies on straightforward computations. In a Monte Carlo study, we show that parameters are estimated accurately for different small sample sizes. We illustrate the model with an empirical in-sample and out-of-sample analysis for a portfolio of 15 U.S. financial assets.
AB - We propose a novel multivariate GARCH model that incorporates realized measures for the covariance matrix of returns. The joint formulation of a multivariate dynamic model for outer-products of returns, realized variances, and realized covariances leads to a feasible approach for analysis and forecasting. The updating of the covariance matrix relies on the score function of the joint likelihood function based on Gaussian and Wishart densities. The dynamic model is parsimonious while the analysis relies on straightforward computations. In a Monte Carlo study, we show that parameters are estimated accurately for different small sample sizes. We illustrate the model with an empirical in-sample and out-of-sample analysis for a portfolio of 15 U.S. financial assets.
KW - high-frequency data
KW - multivariate GARCH
KW - multivariate volatility
KW - realized covariance
KW - score
KW - Wishart distribution
UR - http://www.scopus.com/inward/record.url?scp=85054137869&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054137869&partnerID=8YFLogxK
U2 - 10.1093/jjfinec/nby007
DO - 10.1093/jjfinec/nby007
M3 - Article
VL - 17
SP - 1
EP - 32
JO - Journal of Financial Econometrics
JF - Journal of Financial Econometrics
SN - 1479-8409
IS - 1
ER -