Spurious Dynamic Conditional Correlation
University of St. Gallen - SoF: School of Finance
Thorsten W. Glück
European Business School (EBS) Wiesbaden, Germany - Department of Finance, Accounting and Real Estate
EBS Universität für Wirtschaft und Recht - EBS Business School
September 1, 2013
Autoregressive-type multivariate GARCH (MGARCH) models have been widely adopted by scholars to measure time-varying correlation structures. It is a well-known stylized fact that conditional correlations generated by these models tend to exhibit a highly unstable and erratic behavior under certain conditions. Therefore, the value added of these scientifically demanding MGARCH techniques remains sometimes ambiguous. In this paper, we provide a measure for the autocovariance structure of conditional correlations generated by a DCC MGARCH under the assumption that true conditional correlations are constant. This allows us to formally demonstrate that autocovariances of conditional correlations generated by an autoregressive-type MGARCH model are highly sensitive to small changes in model parameters. We provide empirical evidence for the impact of parameter changes on forecasting accuracy and show under which conditions a simple rolling window estimator yields superior results.
Number of Pages in PDF File: 34
Keywords: Multivariate GARCH models, dynamic conditional correlation (DCC), constant conditional correlation, rolling window estimator, spurious correlation
JEL Classification: C52, C53
Date posted: November 24, 2011 ; Last revised: September 27, 2013
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