Portfolio optimization based on GARCH-EVT-Copula forecasting models

Maziar Sahamkhadam, Andreas Stephan, Ralf Östermark

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)


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.
Original languageUndefined/Unknown
Pages (from-to)497–506
Number of pages10
JournalInternational Journal of Forecasting
Issue number3
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


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

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