Portfolio optimization based on GARCH-EVT-Copula forecasting models

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Maziar Sahamkhadam, Andreas Stephan, Ralf Ostermark
Publisher: ELSEVIER SCIENCE BV
Publication year: 2018
Journal: International Journal of Forecasting
Journal acronym: INT J FORECASTING
Volume number: 34
Issue number: 3
Start page: 497
End page: 506
Number of pages: 10
ISSN: 0169-2070


Abstract

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-t and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-t copulas, are best at reducing the portfolio risk. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.


Keywords

Conditional value-at-risk, Copula models, Extreme value theory, GARCH models, Portfolio optimization

Last updated on 2019-16-10 at 03:19