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Value-at-Risk-efficient portfolios for class of super- and sub-exponentially decaying assets return distributions

Author

Listed:
  • Yannick Malevergne

    (EM - EMLyon Business School)

  • Didier Sornette

Abstract

Using a family of modified Weibull distributions encompassing both sub-exponentials and super-exponentials to parametrize the marginal distributions of asset returns and their multivariate generalizations with Gaussian copulas, we offer exact formulae for the tails of the distribution P(S) of returns S of a portfolio of arbitrary composition of these assets. We find that the tail of P(S) is also asymptotically a modified Weibull distribution with a characteristic scale χ function of the asset weights with different functional forms depending on the super- or sub-exponential behaviour of the marginals and on the strength of the dependence between the assets. We then treat in detail the problem of risk minimization using the Value-at-Risk and expected shortfall which are shown to be (asymptotically) equivalent in this framework.

Suggested Citation

  • Yannick Malevergne & Didier Sornette, 2004. "Value-at-Risk-efficient portfolios for class of super- and sub-exponentially decaying assets return distributions," Post-Print hal-02312887, HAL.
    • Handle: RePEc:hal:journl:hal-02312887
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    Cited by:

    1. Chen, Qian & Gerlach, Richard H., 2013. "The two-sided Weibull distribution and forecasting financial tail risk," International Journal of Forecasting, Elsevier, vol. 29(4), pages 527-540.
    2. Saralees Nadarajah & Samuel Kotz, 2006. "The modified Weibull distribution for asset returns," Quantitative Finance, Taylor & Francis Journals, vol. 6(6), pages 449-449.
    3. Vicente Medina Martínez & Ángel Pardo Tornero, 2012. "Stylized facts of CO2 returns," Working Papers. Serie AD 2012-14, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    4. Chao Wang & Qian Chen & Richard Gerlach, 2017. "Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution," Papers 1707.03715, arXiv.org.
    5. Richard Gerlach & Cathy W. S. Chen, 2015. "Bayesian Expected Shortfall Forecasting Incorporating the Intraday Range," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(1), pages 128-158.
    6. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.

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