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TOPIC:
A MULTIVARIATE REALIZED GARCH MODEL
ABSTRACT
We propose a novel class of multivariate Realized GARCH models that utilize realized measures of volatility and correlations. The key property of the model is a convenient parametrization of the correlation matrix that requires no additional structure to ensure positive definiteness. The correlation matrix is characterized by a vector, that can vary freely in the real vector space. A more parsimonious structure is often desired in practice, in particularly in high dimensional systems, and the framework facilitates simple and intuitive dimension reductions. We apply the model to returns of nine assets and illustrate a dimension reduction that arises from a natural block equicorrelation structure. Interestingly, we find that the empirical distribution of the transformed realized correlations is approximately Gaussian.
Keywords: Financial volatility, realized GARCH, high frequency data, multivariate modeling.