The conditional autoregressive Wishart model for multivariate stock market volatility

Vasyl Golosnoy, Bastian Gribisch and Roman Liesenfeld

Journal of Econometrics, 2012, vol. 167, issue 1, 211-223

Abstract: We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a Mixed Data Sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to realized variances and covariances for five New York Stock Exchange stocks.

Keywords: Component volatility models; Covariance matrix; Mixed data sampling; Observation-driven models; Realized volatility (search for similar items in EconPapers)
JEL-codes: C32 C58 G17 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (94)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:167:y:2012:i:1:p:211-223

DOI: 10.1016/j.jeconom.2011.11.004

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