Portfolio optimization based on GARCH-EVT-Copula forecasting models

Maziar Sahamkhadam, Andreas Stephan, Ralf Östermark

    Research output: Contribution to journalArticleScientificpeer-review

    48 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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