The generalized conditional autoregressive Wishart model for multivariate realized volatility

Research output: Contribution to journalArticles

5 Citations (Scopus)

Abstract

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting. Copyright © 2017 American Statistical Association.
Original languageEnglish
Pages (from-to)513-527
JournalJournal of Business and Economic Statistics
Volume35
Issue number4
Early online dateApr 2017
DOIs
Publication statusPublished - 2017

Citation

Yu, P. L. H., Li, W. K., & Ng, F. C. (2017). The generalized conditional autoregressive Wishart model for multivariate realized volatility. Journal of Business & Economic Statistics, 35(4), 513-527. doi: 10.1080/07350015.2015.1096788

Keywords

  • Covariance matrix
  • High frequency data
  • Time series

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