A Flexible Dynamic Correlation Model
Econometric Analysis of Financial and Economic Time Series
ISBN: 978-0-76231-274-0, eISBN: 978-1-84950-389-1
Publication date: 29 March 2006
Abstract
Existing multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models either impose strong restrictions on the parameters or do not guarantee a well-defined (positive-definite) covariance matrix. I discuss the main multivariate GARCH models and focus on the BEKK model for which it is shown that the covariance and correlation is not adequately specified under certain conditions. This implies that any analysis of the persistence and the asymmetry of the correlation is potentially inaccurate. I therefore propose a new Flexible Dynamic Correlation (FDC) model that parameterizes the conditional correlation directly and eliminates various shortcomings. Most importantly, the number of exogenous variables in the correlation equation can be flexibly augmented without risking an indefinite covariance matrix. Empirical results of daily and monthly returns of four international stock market indices reveal that correlations exhibit different degrees of persistence and different asymmetric reactions to shocks than variances. In addition, I find that correlations do not always increase with jointly negative shocks implying a justification for international portfolio diversification.
Citation
Baur, D. (2006), "A Flexible Dynamic Correlation Model", Terrell, D. and Fomby, T.B. (Ed.) Econometric Analysis of Financial and Economic Time Series (Advances in Econometrics, Vol. 20 Part 1), Emerald Group Publishing Limited, Leeds, pp. 3-31. https://doi.org/10.1016/S0731-9053(05)20001-4
Publisher
:Emerald Group Publishing Limited
Copyright © 2006, Emerald Group Publishing Limited