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A0259
Title: Volatility martingale difference divergence matrix for dimension reduction of multivariate volatility Authors:  Chung Eun Lee - University of Tennessee, Knoxville (United States) [presenting]
Xiaofeng Shao - University of Illinois at Urbana-Champaign (United States)
Abstract: The so-called volatility martingale difference divergence matrix (VMDDM)is proposed to quantify the conditional variance dependence of a random vector $Y$ given $X$, building on the recent work on the martingale difference divergence matrix (MDDM) measures the conditional mean dependence. We further generalize VMDDM to the time series context and apply it to make dimension reduction for multivariate volatility, following the recent work. Unlike the latter work, our metric is easy to compute, can fully capture nonlinear serial dependence and involves fewer user-chosen numbers. Furthermore, we propose a variant of VMDDM and apply it to the estimation of the conditional uncorrelated components model. Simulation and data illustration show that our method can perform well compared to the existing ones with the less computational time and can outperform others in cases of strong nonlinear dependence.