The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility
34 Pages Posted: 13 Jun 2010 Last revised: 7 Dec 2011
Date Written: June 8, 2010
We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes a generalized linear autoregressive moving average structure for the scale matrix of the Wishart distribution allowing to accommodate for complex dynamic interdependence between the variances and covariances of assets. In addition, it accounts for symmetry and positive definiteness of covariance matrices without imposing parametric restrictions, and can easily 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 time series of daily realized variances and covariances for five New York Stock Exchange (NYSE) stocks.
Keywords: Component volatility models, Covariance matrix, Mixed data sampling, Observation-driven models, Realized volatility
JEL Classification: C32,C53
Suggested Citation: Suggested Citation