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Forecasting compositional risk allocations

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  • Boonen, Tim J.
  • Guillen, Montserrat
  • Santolino, Miguel

Abstract

We analyse models for panel data that arise in risk allocation problems, when a given set of sources are the cause of an aggregate risk value. We focus on the modelling and forecasting of proportional contributions to risk over time. Compositional data methods are proposed and the time-series regression is flexible to incorporate external information from other variables. We guarantee that projected proportional contributions add up to 100%, and we introduce a method to generate confidence regions with the same restriction. An illustration is provided for risk capital allocations.

Suggested Citation

  • Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
    • Handle: RePEc:eee:insuma:v:84:y:2019:i:c:p:79-86
    DOI: 10.1016/j.insmatheco.2018.10.002
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    Cited by:

    1. Juan David Vega Baquero & Miguel Santolino, 2021. ""Too big to fail? An analysis of the Colombian banking system through compositional data"," IREA Working Papers 202111, University of Barcelona, Research Institute of Applied Economics, revised Apr 2021.
    2. Jilber Urbina & Miguel Santolino & Montserrat Guillen, 2021. "Covariance Principle for Capital Allocation: A Time-Varying Approach," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    3. Salvador Linares-Mustar'os & Maria `Angels Farreras-Noguer & N'uria Arimany-Serrat & Germ`a Coenders, 2022. "New financial ratios based on the compositional data methodology," Papers 2210.11138, arXiv.org.
    4. Jaume Belles-Sampera & Montserrat Guillen & Miguel Santolino, 2023. "Haircut Capital Allocation as the Solution of a Quadratic Optimisation Problem," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    5. Mohammed, Nawaf & Furman, Edward & Su, Jianxi, 2021. "Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of conditional tail expectation," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 425-436.
    6. Germ`a Coenders & N'uria Arimany Serrat, 2023. "Accounting statement analysis at industry level. A gentle introduction to the compositional approach," Papers 2305.16842, arXiv.org, revised Feb 2024.
    7. Nawaf Mohammed & Edward Furman & Jianxi Su, 2021. "Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of Conditional Tail Expectation," Papers 2102.05003, arXiv.org, revised Aug 2021.
    8. Vega Baquero, Juan David & Santolino, Miguel, 2022. "Too big to fail? An analysis of the Colombian banking system through compositional data," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(2).
    9. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    10. Takaaki Koike & Cathy W. S. Chen & Edward M. H. Lin, 2024. "Forecasting and Backtesting Gradient Allocations of Expected Shortfall," Papers 2401.11701, arXiv.org.
    11. Anna Maria Fiori & Francesco Porro, 2023. "A compositional analysis of systemic risk in European financial institutions," Annals of Finance, Springer, vol. 19(3), pages 325-354, September.

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    More about this item

    Keywords

    Simplex; Capital allocation; Dynamic risk management; Isometric logratio; Aitchison geometry;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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