A Matrix-Variate Log-Normal Model for Covariance Matrices

TL;DR

This paper introduces a novel matrix-variate log-normal model for dynamic covariance matrices, ensuring positive definiteness and reducing dimensionality issues through a BEKK-type structure and bias correction.

Contribution

It develops a new modeling framework for time-varying covariance matrices using matrix-log-normal assumptions with a parsimonious BEKK-type structure and bias correction techniques.

Findings

Guarantees positive definiteness without extra constraints

Provides a bias-corrected estimation method for the covariance matrix

Applicable to various problems involving symmetric positive definite matrices

Abstract

We propose a modeling framework for time-varying covariance matrices based on the assumption that the logarithm of a realized covariance matrix follows a matrix-variate oNrmal distribution. By operating in the space of symmetric matrices, the approach guarantees positive definiteness without imposing parameter constraints beyond stationarity. The conditional mean of the logarithmic covariance matrix is specified through a BEKK-type structure that can be rewritten as a diagonal vector representation, yielding a parsimonious specification that mitigates the curse of dimensionality. Estimation is performed by maximum likelihood exploiting properties of matrix-variate Normal distributions and expressing the scale parameter matrix as a function of the location matrix. The covariance matrix is recovered via the matrix exponential. Since this transformation induces an upward bias, an approximate, time-specific bias correction based on a second-order Taylor expansion is proposed. The framework is flexible and applicable to a wide class of problems involving symmetric positive definite matrices.