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Modeling multivariate volatility using wavelet-based realized covariance estimator
Baruník, Jozef ; Vácha, Lukáš
Abstract. Study of the covariation have become one of the most active and successful areas of research in the time series econometrics and economic forecasting during the recent decades. Our work brings complete theory for the realized covariation estimation generalizing current knowledge and bringing the estimation to the time-frequency domain for the first time. The results generalize the popular realized volatility framework by bringing the robustness to noise as well jumps and ability to measure the realized covariance not only in time but also in frequency domain. Noticeable contribution is brought also by the application of the presented theory. Our time-frequency estimators bring not only more efficient estimates, but decomposes the realized covariation into arbitrarily chosen investment horizons. Results thus bring better understanding of the dynamics of dependence between the stock markets.

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