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Tail Risk in Commercial Property Insurance

Author

Listed:
  • Enrico Biffis

    () (Department of Finance, Imperial College Business School, Imperial College London, LondonSW7 2AZ, UK)

  • Erik Chavez

    () (Department of Finance, Imperial College Business School, Imperial College London, LondonSW7 2AZ, UK
    Civil & Environmental Engineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

We present some new evidence on the tail distribution of commercial property losses based on a recently constructed dataset on large commercial risks. The dataset is based on contributions from Lloyd’s of London syndicates, and provides information on over three thousand claims occurred during the period 2000–2012, including detailed information on exposures. We use occupancy characteristics to compare the tail risk profiles of different commercial property exposures, and find evidence of substantial heterogeneity in tail behavior. The results demonstrate the benefits of aggregating granular information on both claims and exposures from different data sources, and provide warning against the use of reserving and capital modeling approaches that are not robust to heavy tails.

Suggested Citation

  • Enrico Biffis & Erik Chavez, 2014. "Tail Risk in Commercial Property Insurance," Risks, MDPI, Open Access Journal, vol. 2(4), pages 1-18, September.
  • Handle: RePEc:gam:jrisks:v:2:y:2014:i:4:p:393-410:d:40817
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    References listed on IDEAS

    as
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    2. Silverberg, Gerald & Verspagen, Bart, 2007. "The size distribution of innovations revisited: An application of extreme value statistics to citation and value measures of patent significance," Journal of Econometrics, Elsevier, vol. 139(2), pages 318-339, August.
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    4. Rustam Ibragimov, 2009. "Portfolio diversification and value at risk under thick-tailedness," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 565-580.
    5. Beirlant, Jan & Goegebeur, Yuri, 2003. "Regression with response distributions of Pareto-type," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 595-619, April.
    6. Xavier Gabaix & Rustam Ibragimov, 2011. "Rank - 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 24-39, January.
    7. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
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    10. Desmedt, S. & Snoussi, M. & Chenut, X. & Walhin, J. F., 2012. "Experience and Exposure Rating for Property Per Risk Excess of Loss Reinsurance Revisited," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 42(01), pages 233-270, May.
    11. M. Ivette Gomes & Laurens de Haan & Lígia Henriques Rodrigues, 2008. "Tail index estimation for heavy-tailed models: accommodation of bias in weighted log-excesses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 31-52.
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    More about this item

    Keywords

    commercial property insurance; exposure rating; heavy tails; tail index; tail regression;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting
    • K2 - Law and Economics - - Regulation and Business Law

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