A New Parametrization of Correlation Matrices

By I. Archakov, P. R. Hansen. 2020

63 citations recorded by the source corpus.

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 Fisther'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 d-dimensional vector (with d = n(n-1)/2) is provided, and we derive its numerical complexity.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ff43f5d76657937e0f65c0ed1df18330c5f9be66

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Gary Koop
2. Xuewen Yu
3. Joshua C. C. Chan

In Statistical and Nonlinear Physics

1. Gary Koop
2. Xuewen Yu
3. Joshua C. C. Chan

In Signal Processing

1. Gary Koop
2. Xuewen Yu
3. Joshua C. C. Chan

Citations in the corpus

• Large Order-Invariant Bayesian VARs with Stochastic Volatility (2021, methodology): “…of using the Wishart or inverse-Wishart processes, Arias, Rubio-Ramirez, and Shin (2022) introduce the DSC-SV-AH model that uses random walk processes to construct a time-varying correlation matrix based on a new parameterization of correlation matrices proposed in Archakov and Hansen (2021).”