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Multivariate heavy-tailed models for Value-at-Risk estimation

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  • Carlo Marinelli
  • Stefano d'Addona
  • Svetlozar T. Rachev

Abstract

For purposes of Value-at-Risk estimation, we consider several multivariate families of heavy-tailed distributions, which can be seen as multidimensional versions of Paretian stable and Student's t distributions allowing different marginals to have different tail thickness. After a discussion of relevant estimation and simulation issues, we conduct a backtesting study on a set of portfolios containing derivative instruments, using historical US stock price data.

Suggested Citation

  • Carlo Marinelli & Stefano d'Addona & Svetlozar T. Rachev, 2010. "Multivariate heavy-tailed models for Value-at-Risk estimation," Papers 1005.2862, arXiv.org, revised Dec 2011.
    • Handle: RePEc:arx:papers:1005.2862
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    2. Simon Hediger & Jeffrey Näf & Marc S. Paolella & Paweł Polak, 2023. "Heterogeneous tail generalized common factor modeling," Digital Finance, Springer, vol. 5(2), pages 389-420, June.
    3. Hediger, Simon & Näf, Jeffrey, 2024. "Combining the MGHyp distribution with nonlinear shrinkage in modeling financial asset returns," Journal of Empirical Finance, Elsevier, vol. 77(C).

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