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|Title:||A Minimum Distance Estimator for Dynamic Conditional Correlations|
|Other Identifiers:||EUR 21889 EN|
|Type:||EUR - Scientific and Technical Research Reports|
|Abstract:||The crucial problem in estimating dynamic conditional correlation models is the need to guarantee a positive-definite covariance matrix. In order to avoid any violation of this property, many estimators impose strong restrictions on the model. In addition, many models do not parameterize the correlations directly but the covariance. This paper avoids this problem in proposing a minimum distance estimator (MDE) to estimate dynamic conditional correlations or multivariate GARCH models. The model allows full flexibility in the estimation. Violations of the positive-definiteness of the covariance matrices are part of the specification tests. A simulation study shows the performance of the estimator and the empirical section compares estimates of the MDE with the DCC estimator of Engle (2002). This paper does not only demonstrate an alternative estimator but also outlines how this model can be used to analyze the influence of the restrictions on the estimates in multivariate GARCH models.|
|JRC Institute:||Institute for the Protection and Security of the Citizen|
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