Portfolio optimization based on GARCH-EVT-Copula forecasting models
| Autor: | Andreas Stephan, Maziar Sahamkhadam, Ralf Östermark |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
050208 finance
Autoregressive conditional heteroskedasticity 05 social sciences Copula (linguistics) Stock market index Expected shortfall Gumbel distribution 0502 economics and business Econometrics Portfolio Stock market 050207 economics Business and International Management Portfolio optimization Mathematics |
| Zdroj: | International Journal of Forecasting. 34:497-506 |
| ISSN: | 0169-2070 |
| DOI: | 10.1016/j.ijforecast.2018.02.004 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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