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VaR bounds for joint portfolios with dependence constraints

Author

Listed:
  • Puccetti Giovanni

    (Department of Economics, Management and Quantitative Methods, University of Milano, Italy)

  • Rüschendorf Ludger

    (Department of Mathematical Stochastics, University of Freiburg, Germany)

  • Manko Dennis

    (Department of Mathematical Stochastics, University of Freiburg, Germany)

Abstract

Based on a novel extension of classical Hoeffding-Fréchet bounds, we provide an upper VaR bound for joint risk portfolios with fixed marginal distributions and positive dependence information. The positive dependence information can be assumed to hold in the tails, in some central part, or on a general subset of the domain of the distribution function of a risk portfolio. The newly provided VaR bound can be interpreted as a comonotonic VaR computed at a distorted confidence level and its quality is illustrated in a series of examples of practical interest.

Suggested Citation

  • Puccetti Giovanni & Rüschendorf Ludger & Manko Dennis, 2016. "VaR bounds for joint portfolios with dependence constraints," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-14, December.
    • Handle: RePEc:vrs:demode:v:4:y:2016:i:1:p:14:n:21
    DOI: 10.1515/demo-2016-0021
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    Citations

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    Cited by:

    1. Papapantoleon Antonis & Yanez Sarmiento Paulo, 2021. "Detection of arbitrage opportunities in multi-asset derivatives markets," Dependence Modeling, De Gruyter, vol. 9(1), pages 439-459, January.
    2. Thibaut Lux & Antonis Papapantoleon, 2016. "Model-free bounds on Value-at-Risk using extreme value information and statistical distances," Papers 1610.09734, arXiv.org, revised Nov 2018.
    3. Antonis Papapantoleon & Paulo Yanez Sarmiento, 2020. "Detection of arbitrage opportunities in multi-asset derivatives markets," Papers 2002.06227, arXiv.org, revised Nov 2021.
    4. Bernard, Carole & Kazzi, Rodrigue & Vanduffel, Steven, 2020. "Range Value-at-Risk bounds for unimodal distributions under partial information," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 9-24.
    5. Ariel Neufeld & Antonis Papapantoleon & Qikun Xiang, 2023. "Model-Free Bounds for Multi-Asset Options Using Option-Implied Information and Their Exact Computation," Management Science, INFORMS, vol. 69(4), pages 2051-2068, April.
    6. Ariel Neufeld & Antonis Papapantoleon & Qikun Xiang, 2020. "Model-free bounds for multi-asset options using option-implied information and their exact computation," Papers 2006.14288, arXiv.org, revised Jan 2022.
    7. Chen, Yuyu & Lin, Liyuan & Wang, Ruodu, 2022. "Risk aggregation under dependence uncertainty and an order constraint," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 169-187.
    8. Lux, Thibaut & Papapantoleon, Antonis, 2019. "Model-free bounds on Value-at-Risk using extreme value information and statistical distances," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 73-83.
    9. Yuyu Chen & Liyuan Lin & Ruodu Wang, 2021. "Risk Aggregation under Dependence Uncertainty and an Order Constraint," Papers 2104.07718, arXiv.org, revised Oct 2021.
    10. Rüschendorf, L., 2019. "Analysis of risk bounds in partially specified additive factor models," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 115-121.
    11. Yuen, Robert & Stoev, Stilian & Cooley, Daniel, 2020. "Distributionally robust inference for extreme Value-at-Risk," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 70-89.
    12. Rüschendorf L., 2018. "Risk bounds with additional information on functionals of the risk vector," Dependence Modeling, De Gruyter, vol. 6(1), pages 102-113, June.
    13. Roberto Fontana & Elisa Luciano & Patrizia Semeraro, 2021. "Model risk in credit risk," Mathematical Finance, Wiley Blackwell, vol. 31(1), pages 176-202, January.
    14. Evangelia Dragazi & Shuaiqiang Liu & Antonis Papapantoleon, 2024. "Improved model-free bounds for multi-asset options using option-implied information and deep learning," Papers 2404.02343, arXiv.org.

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