A New Parametrization of Correlation Matrices

被引：33

作者：

Archakov, Ilya ^{[1
]}

Hansen, Peter Reinhard ^{[2
]}

机构：

[1] Univ Vienna, Dept Stat & Operat Res, Vienna, Austria

[2] Univ N Carolina, Dept Econ, Chapel Hill, NC 27515 USA

来源：

ECONOMETRICA | 2021年 / 89卷 / 04期

基金：

奥地利科学基金会; 英国经济与社会研究理事会;

关键词：

Correlation matrix; Covariance modeling; Fisher transformation; ASYMPTOTIC COVARIANCE-MATRIX; MULTIVARIATE; MODEL; VOLATILITY;

D O I：

10.3982/ECTA16910

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We introduce a novel parametrization of the correlation matrix. The reparametrization facilitates modeling of correlation and covariance matrices by an unrestricted vector, where positive definiteness is an innate property. This parametrization can be viewed as a generalization of Fisher's Z-transformation to higher dimensions and has a wide range of potential applications. An algorithm for reconstructing the unique n x n correlation matrix from any vector in Rn(n-1)/2 is provided, and we derive its numerical complexity.

引用

页码：1699 / 1715

页数：17

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