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Measuring Portfolio Risk Under Partial Dependence Information

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  • Bernard, Carole
  • Denuit, Michel
  • Vanduffel, Steven

Abstract

The bounds for risk measures of a portfolio when its components have known marginal distributions but the dependence among the risks is unknown are often too wide to be useful in practice. Moreover, availability of additional dependence information, such as knowledge of some higher‐order moments, makes the problem significantly more difficult. We show that replacing knowledge of the marginal distributions with knowledge of the mean of the portfolio does not result in significant loss of information when estimating bounds on value‐at‐risk. These results are used to assess the margin by which total capital can be underestimated when using the Solvency II or RBC capital aggregation formulas.
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Suggested Citation

  • Bernard, Carole & Denuit, Michel & Vanduffel, Steven, 2018. "Measuring Portfolio Risk Under Partial Dependence Information," LIDAM Reprints ISBA 2018025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  • Handle: RePEc:aiz:louvar:2018025
    Note: In : Journal of Risk and Insurance, vol. 85, no. 3, p. 843-863 (2018)
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    Cited by:

    1. Natalia Nehrebecka, 2019. "Credit risk measurement: Evidence of concentration risk in Polish banks’ credit exposures," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(2), pages 681-712.
    2. Cornilly, Dries & Vanduffel, Steven, 2019. "Equivalent distortion risk measures on moment spaces," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 187-192.
    3. Paulusch, Joachim & Schlütter, Sebastian, 2022. "Sensitivity-implied tail-correlation matrices," Journal of Banking & Finance, Elsevier, vol. 134(C).
    4. Rüschendorf, L., 2019. "Analysis of risk bounds in partially specified additive factor models," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 115-121.
    5. Asimit, Alexandru V. & Gerrard, Russell, 2016. "On the worst and least possible asymptotic dependence," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 218-234.
    6. Marius Hofert, 2020. "Implementing the Rearrangement Algorithm: An Example from Computational Risk Management," Risks, MDPI, vol. 8(2), pages 1-28, May.
    7. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    8. Paulusch, Joachim & Schlütter, Sebastian, 2021. "Sensitivity-implied tail-correlation matrices," ICIR Working Paper Series 33/19, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR), revised 2021.
    9. Lambert, Philippe, 2023. "Nonparametric density estimation and risk quantification from tabulated sample moments," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 177-189.
    10. Carole Bernard & Silvana M. Pesenti & Steven Vanduffel, 2022. "Robust Distortion Risk Measures," Papers 2205.08850, arXiv.org, revised Mar 2023.
    11. 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.
    12. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel & Jing Yao, 2017. "How robust is the value-at-risk of credit risk portfolios?," The European Journal of Finance, Taylor & Francis Journals, vol. 23(6), pages 507-534, May.

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