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Modelling multivariate volatilities via conditionally uncorrelated components

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Author Info
Jianqing Fan
Mingjin Wang
Qiwei Yao

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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. Copyright (c) 2008 Royal Statistical Society.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9868.2008.00654.x
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Publisher Info
Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Volume (Year): 70 (2008)
Issue (Month): 4 ()
Pages: 679-702
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Handle: RePEc:bla:jorssb:v:70:y:2008:i:4:p:679-702

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  1. Antonio García-Ferrer & Ester González-Prieto & Daniel Peña, 2008. "A multivariate generalized independent factor GARCH model with an application to financial stock returns," Statistics and Econometrics Working Papers ws087528, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
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