Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229716
Authors: 
Blasques, Francisco
Lucas, Andre
Opschoor, Anne
Rossini, Luca
Year of Publication: 
2021
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2021-010/III
Abstract: 
We introduce the new F-Riesz distribution to model tail-heterogeneity in fat-tailed covariance matrix observations. In contrast to the typical matrix-valued distributions from the econometric literature, the F-Riesz distribution allows for different tail behavior across all variables in the system. We study the consistency properties of the maximum likelihood estimator in both static and dynamic models with F- Riesz innovations using both one-step and two-step (targeting) estimation techniques. Allowing for tail-heterogeneity when modeling covariance matrices appears empirically highly relevant. When applying the new distribution to realized covariance matrices of 30 U.S. stocks over a 14 year period, we find huge likelihood increases both in-sample and out-of-sample compared to all competing distributions, including the Wishart, inverse Wishart, Riesz, inverse Riesz, and matrix-F distribution.
Subjects: 
Matrix Distributions
Tail Heterogeneity
(inverse) Riesz Distribution
Fat-Tails
Realized Covariance Matrices
JEL: 
C58
C32
C46
Document Type: 
Working Paper

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