Research Papers‎ > ‎

A New Parametrization of Correlation Matrices

posted Nov 20, 2018, 11:33 AM by Peter Hansen
Abstract: For the modeling of covariance matrices, the literature has proposed a variety of methods to enforce the positive (semi) definiteness. In this paper, we propose a method that is based on a novel parametrization of the correlation matrix, specifically the off-diagonal elements of the matrix logarithmic transformed correlations. This parametrization has many attractive properties, a wide range of applications, and may be viewed as a multivariate generalization of Fisher's Z-transformation of a single correlation.

Keywords: Covariance Modeling, Covariance Regularization, Fisher Transformation, Multivariate GARCH, Stochastic Volatility. 

Authors: Ilya Archakov and Peter Reinhard Hansen

Ċ
CorNote.pdf
(1043k)
Peter Hansen,
Dec 18, 2018, 8:53 AM
Ċ
Peter Hansen,
Dec 18, 2018, 8:54 AM
Comments