Realized wishart-garch: A score-driven multi-Asset volatility model

P. Gorgi, P. R. Hansen, P. Janus, S. J. Koopman*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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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 languageEnglish
Pages (from-to)1-32
Number of pages32
JournalJournal of Financial Econometrics
Issue number1
Early online date16 Apr 2018
Publication statusPublished - Jan 2019


  • high-frequency data
  • multivariate GARCH
  • multivariate volatility
  • realized covariance
  • score
  • Wishart distribution


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