Volatility Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Multivariate Volatility

Chung Eun Lee, Xiaofeng Shao

Research output: Contribution to journalArticle

Abstract

In this article, we propose the so-called volatility martingale difference divergence matrix (VMDDM) to quantify the conditional variance dependence of a random vector (Formula presented.) given (Formula presented.), building on the recent work on martigale difference divergence matrix (MDDM) that measures the conditional mean dependence. We further generalize VMDDM to the time series context and apply it to do dimension reduction for multivariate volatility, following the recent work by Hu and Tsay and Li et al. Unlike the latter two papers, our metric is easy to compute, can fully capture nonlinear serial dependence and involves less user-chosen numbers. Furthermore, we propose a variant of VMDDM and apply it to the estimation of conditional uncorrelated components model (Fan, Wang, and Yao 2008). Simulation and data illustration show that our method can perform well in comparison with the existing ones with less computational time, and can outperform others in cases of strong nonlinear dependence.

Original languageEnglish (US)
Pages (from-to)80-92
Number of pages13
JournalJournal of Business and Economic Statistics
Volume38
Issue number1
DOIs
StatePublished - Jan 2 2020

Fingerprint

Martingale Difference
Dimension Reduction
Volatility
divergence
Divergence
Matrix Measure
Serial Dependence
Divergence Measure
Conditional Variance
Conditional Model
Component Model
fan
Random Vector
time series
Quantify
Time series
Metric
Generalise
simulation
Dimension reduction

Keywords

  • Conditional heteroscedasticity
  • Dimension reduction
  • Nonlinear dependence
  • Principal components

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this

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