Portfolio optimization based on GARCH-EVT-Copula forecasting models

By Maziar Sahamkhadam, Andreas Stephan, R. Östermark. International Journal of Forecasting, 2018

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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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/d99ffea4e3efa3d0a08224b1e54cf45026dc51b8