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Monte Carlo Value-At-Risk and Expected Shortfall of Efficient-Frontier Investment Portfolios: Testing Gaussian Versus Vine Copulas and Normal Versus Empirical Marginals

In: New Perspectives and Paradigms in Applied Economics and Business

Author

Listed:
  • Poomjai Nacaskul

    (Chulalongkorn University)

  • Kongkan Kalakan

    (Chulalongkorn University)

  • Napat Tonglongya

    (Chulalongkorn University)

Abstract

We examined historical return distributions of 60 global investment funds, generated mean–variance efficient frontier “funds of funds”, and compared the industry-standard risk metrics, notably Value-at-Risk (VaR) and Expected Shortfall (ES), obtained from parametric Monte Carlo simulations under 3 sets of assumptions, namely multivariate normality, Gaussian copula with empirical marginals, and vine copula with empirical marginals, to see which came closest to results from historical Monte Carlo simulations, herewith taken as ground truth. We found that multivariate normality runs got closest to historical simulation in terms of mean and standard deviation (quite a surprise) but fared worst in terms of VaR and ES (to be expected). In addition, vine copula runs outperformed Gaussian copula runs, albeit the performance differentials were less dramatic when compared with the gain from ditching Gaussian marginals in favour of empirical marginals.

Suggested Citation

  • Poomjai Nacaskul & Kongkan Kalakan & Napat Tonglongya, 2024. "Monte Carlo Value-At-Risk and Expected Shortfall of Efficient-Frontier Investment Portfolios: Testing Gaussian Versus Vine Copulas and Normal Versus Empirical Marginals," Springer Proceedings in Business and Economics, in: William C. Gartner (ed.), New Perspectives and Paradigms in Applied Economics and Business, pages 53-82, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-49951-7_5
    DOI: 10.1007/978-3-031-49951-7_5
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