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Modelling multivariate volatilities via conditionally uncorrelated components
Author(s) -
Fan Jianqing,
Wang Mingjin,
Yao Qiwei
Publication year - 2008
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2008.00654.x
Subject(s) - uncorrelated , univariate , multivariate statistics , consistency (knowledge bases) , volatility (finance) , representation (politics) , econometrics , mathematics , computer science , conditional independence , algorithm , statistics , artificial intelligence , politics , political science , law
Summary. 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.