Modelling multivariate volatilities via conditionally uncorrelated components

Jianqing Fan, Mingjin Wang, Qiwei Yao

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the estimated CUCs has been established. A bootstrap method is proposed for testing the existence of CUCs. The methodology proposed is illustrated with both simulated and real data sets.

Original languageEnglish (US)
Pages (from-to)679-702
Number of pages24
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume70
Issue number4
DOIs
StatePublished - Sep 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Bootstrap test
  • Causality in variance
  • Dimension reduction
  • Extended GARCH(1,1) model
  • Financial returns
  • Portfolio volatility
  • Quasi-maximum-likelihood estimator
  • Time series

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