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.
(joint with Chico Blasques, Andre Lucas and Anne Opschoor)
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Secretariat Econometrics
Phone: +31 (0)10 408 12 59/ 12 64
Email: eb-secr@ese.eur.nl