Multi-Tail Generalized Elliptical Distributions for Asset Returns

20 Pages Posted: 8 Oct 2009

See all articles by Sebastian Kring

Sebastian Kring

University of Karlsruhe

Svetlozar Rachev

Texas Tech University

Markus Höchstötter

affiliation not provided to SSRN

Frank J. Fabozzi

EDHEC Business School

Michele Leonardo Bianchi

Bank of Italy

Date Written: 2009-01

Abstract

In the study of asset returns, the preponderance of empirical evidence finds that return distributions are not normally distributed. Despite this evidence, non-normal multivariate modelling of asset returns does not appear to play an important role in asset management or risk management because of the complexity of estimating multivariate non-normal distributions from market return data. In this paper, we present a new subclass of generalized elliptical distributions for asset returns that is sufficiently user friendly, so that it can be utilized by asset managers and risk managers for modelling multivariate non-normal distributions of asset returns. For the distribution we present, which we call the multi-tail generalized elliptical distribution, we (1) derive the densities using results of the theory of generalized elliptical distributions and (2) introduce a function, which we label the tail function, to describe their tail behaviour. We test the model on German stock returns and find that (1) the multi-tail model introduced in the paper significantly outperforms the classical elliptical model and (2) the hypothesis of homogeneous tail behaviour can be rejected.

Suggested Citation

Kring, Sebastian and Rachev, Svetlozar and Höchstötter, Markus and Fabozzi, Frank J. and Bianchi, Michele Leonardo, Multi-Tail Generalized Elliptical Distributions for Asset Returns (2009-01). Econometrics Journal, Vol. 12, Issue 2, pp. 272-291, July 2009, Available at SSRN: https://ssrn.com/abstract=1485073 or http://dx.doi.org/10.1111/j.1368-423X.2009.00290.x

Sebastian Kring (Contact Author)

University of Karlsruhe ( email )

Postbox
76128 Karlsruhe, DE 76128
Germany

Svetlozar Rachev

Texas Tech University ( email )

Dept of Mathematics and Statistics
Lubbock, TX 79409
United States
631-662-6516 (Phone)

Markus Höchstötter

affiliation not provided to SSRN

No Address Available

Frank J. Fabozzi

EDHEC Business School ( email )

France
215 598-8924 (Phone)

Michele Leonardo Bianchi

Bank of Italy ( email )

Via Nazionale 91
00184 Rome, I - 00184
Italy

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
1
Abstract Views
584
PlumX Metrics