Please use this identifier to cite or link to this item:
Golosnoy, Vasyl
Gribisch, Bastian
Liesenfeld, Roman
Year of Publication: 
Series/Report no.: 
Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2010,07
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.
Component volatility models
Covariance matrix
Mixed data sampling
Observation-driven models
Realized volatility
Document Type: 
Working Paper

Files in This Item:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.