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Stable-$1/2$ bridges and insurance

Tom 104 / 2015

Edward Hoyle, Lane P. Hughston, Andrea Macrina Banach Center Publications 104 (2015), 95-120 MSC: 60G52, 62P05, 62F15, 91B25, 91B30. DOI: 10.4064/bc104-0-5


We develop a class of non-life reserving models using a stable-$1/2$ random bridge to simulate the accumulation of paid claims, allowing for an essentially arbitrary choice of a priori distribution for the ultimate loss. Taking an information-based approach to the reserving problem, we derive the process of the conditional distribution of the ultimate loss. The “best-estimate ultimate loss process” is given by the conditional expectation of the ultimate loss. We derive explicit expressions for the best-estimate ultimate loss process, and for expected recoveries arising from aggregate excess-of-loss reinsurance treaties. Use of a deterministic time change allows for the matching of any initial (increasing) development pattern for the paid claims. We show that these methods are well-suited to the modelling of claims where there is a non-trivial probability of catastrophic loss. The generalized inverse-Gaussian (GIG) distribution is shown to be a natural choice for the a priori ultimate loss distribution. For particular GIG parameter choices, the best-estimate ultimate loss process can be written as a rational function of the paid-claims process. We extend the model to include a second paid-claims process, and allow the two processes to be dependent. The results obtained can be applied to the modelling of multiple lines of business or multiple origin years. The multi-dimensional model has the property that the dimensionality of calculations remains low, regardless of the number of paid-claims processes. An algorithm is provided for the simulation of the paid-claims processes.


  • Edward HoyleFulcrum Asset Management
    Marble Arch House
    London W1H 5BT, UK
  • Lane P. HughstonDepartment of Mathematics
    Brunel University London
    Uxbridge UB8 3PH, UK
    Department of Mathematics
    University College London
    London WC1E 6BT, UK
  • Andrea MacrinaDepartment of Mathematics
    University College London
    London WC1E 6BT, UK
    Department of Actuarial Science
    University of Cape Town
    Rondebosch 7701, RSA

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