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Hedge funds portfolio optimisation using a vine copula-GARCH-EVT-CVaR model

Author

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  • Rihab Bedoui
  • Sameh Noiali
  • Haykel Hamdi

Abstract

This paper investigates the conditional value-at-risk (CVaR) hedge funds portfolio optimisation approach using a univariate GARCH type model, extreme value theory (EVT) and the vine copula to determine the optimal allocation for hedge funds portfolio. First, we apply the generalised pareto distribution (GPD) to model the tails of the innovation of each hedge funds strategy return. Second, we capture the interdependence structure between hedge funds strategies and construct vine copula-GARCH-EVT model. Then, we combine it with Monte Carlo simulation and mean-CVaR model to optimise hedge funds portfolio, in order to estimate the risk more accurately. The empirical results of five Hedge funds indexes show that the C-vine copula can better characterise the interdependence structure between the different hedge funds strategies and the performance of C-vine copula-GARCH-EVT-CVaR model is better that of multivariate copulas-GARCH-EVT-CVaR models in portfolio optimisation.

Suggested Citation

  • Rihab Bedoui & Sameh Noiali & Haykel Hamdi, 2020. "Hedge funds portfolio optimisation using a vine copula-GARCH-EVT-CVaR model," International Journal of Entrepreneurship and Small Business, Inderscience Enterprises Ltd, vol. 39(1/2), pages 121-148.
    • Handle: RePEc:ids:ijesbu:v:39:y:2020:i:1/2:p:121-148
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    Cited by:

    1. Bedoui, Rihab & Benkraiem, Ramzi & Guesmi, Khaled & Kedidi, Islem, 2023. "Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    2. Osman, Myriam Ben & Galariotis, Emilios & Guesmi, Khaled & Hamdi, Haykel & Naoui, Kamel, 2023. "Diversification in financial and crypto markets," International Review of Financial Analysis, Elsevier, vol. 89(C).

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