Work

The Conditional Autoregressive F-Riesz Model for Realized Covariance Matrices

Year: 2024

Type: article

Abstract: We introduce a new model for the dynamics of fat-tailed (realized) covariance-matrix-valued time-series using the F-Riesz distribution. The model allows for heterogeneous tail behavior across the coordinates of the covariance matrix via two vector-valued degrees of freedom parameters, thus generalizing the familiar Wishart and matrix-F distributions. We show that the filter implied by the new model is invertible and that a two-step targeted maximum likelihood estimator is consistent. Applying the new F-Riesz model to U.S. stocks, both tail heterogeneity and tail fatness turn out to be empirically relevant: they produce significant in-sample and out-of-sample likelihood increases, ex-post portfolio standard deviations that are in the global minimum variance model confidence set, and economic differences that are either in favor of the new model or competitive with a range of benchmark models.

Source: Journal of Financial Econometrics

DOI: 10.1093/jjfinec/nbae023

Authors: Anne Opschoor, André Lucas, Luca Rossini

Institutions: Tinbergen Institute, Vrije Universiteit Amsterdam, University of Milan, Fondazione Eni Enrico Mattei

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $5,094

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek