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Direct and Hierarchical Models for Aggregating Spatially Dependent Catastrophe Risks

Author

Listed:
  • Rafał Wójcik

    (AIR-Worldwide, Financial Modeling Group, 131 Dartmouth St., Boston, MA 02116, USA)

  • Charlie Wusuo Liu

    (AIR-Worldwide, Financial Modeling Group, 131 Dartmouth St., Boston, MA 02116, USA)

  • Jayanta Guin

    (AIR-Worldwide, Financial Modeling Group, 131 Dartmouth St., Boston, MA 02116, USA)

Abstract

We present several fast algorithms for computing the distribution of a sum of spatially dependent, discrete random variables to aggregate catastrophe risk. The algorithms are based on direct and hierarchical copula trees. Computing speed comes from the fact that loss aggregation at branching nodes is based on combination of fast approximation to brute-force convolution, arithmetization (regriding) and linear complexity of the method for computing the distribution of comonotonic sum of risks. We discuss the impact of tree topology on the second-order moments and tail statistics of the resulting distribution of the total risk. We test the performance of the presented models by accumulating ground-up loss for 29,000 risks affected by hurricane peril.

Suggested Citation

  • Rafał Wójcik & Charlie Wusuo Liu & Jayanta Guin, 2019. "Direct and Hierarchical Models for Aggregating Spatially Dependent Catastrophe Risks," Risks, MDPI, vol. 7(2), pages 1-22, May.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:2:p:54-:d:229383
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    References listed on IDEAS

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    1. Chaubey, Yogendra P. & Garrido, Jose & Trudeau, Sonia, 1998. "On the computation of aggregate claims distributions: some new approximations," Insurance: Mathematics and Economics, Elsevier, vol. 23(3), pages 215-230, December.
    2. Dhaene, J. & Denuit, M. & Goovaerts, M. J. & Kaas, R. & Vyncke, D., 2002. "The concept of comonotonicity in actuarial science and finance: theory," Insurance: Mathematics and Economics, Elsevier, vol. 31(1), pages 3-33, August.
    3. Lee, Woojoo & Ahn, Jae Youn, 2014. "On the multidimensional extension of countermonotonicity and its applications," Insurance: Mathematics and Economics, Elsevier, vol. 56(C), pages 68-79.
    4. Pavel V. Shevchenko, 2010. "Calculation of aggregate loss distributions," Papers 1008.1108, arXiv.org.
    5. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    6. KOCH, Inge & DE SCHEPPER, Ann, 2006. "The comonotonicity coefficient: A new measure of positive dependence in a multivariate setting," Working Papers 2006030, University of Antwerp, Faculty of Business and Economics.
    7. Gerber, Hans U., 1982. "On the numerical evaluation of the distribution of aggregate claims and its stop-loss premiums," Insurance: Mathematics and Economics, Elsevier, vol. 1(1), pages 13-18, January.
    8. Walhin, J.F. & Paris, J., 1998. "On the Use of Equispaced Discrete Distributions," ASTIN Bulletin, Cambridge University Press, vol. 28(2), pages 241-255, November.
    9. Arbenz, Philipp & Hummel, Christoph & Mainik, Georg, 2012. "Copula based hierarchical risk aggregation through sample reordering," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 122-133.
    10. Vilar, Jose L., 2000. "Arithmetization of distributions and linear goal programming," Insurance: Mathematics and Economics, Elsevier, vol. 27(1), pages 113-122, August.
    11. Koch, Inge & De Schepper, Ann, 2011. "Measuring Comonotonicity in M-Dimensional Vectors," ASTIN Bulletin, Cambridge University Press, vol. 41(1), pages 191-213, May.
    12. Dhaene, J. & Denuit, M. & Goovaerts, M. J. & Kaas, R. & Vyncke, D., 2002. "The concept of comonotonicity in actuarial science and finance: applications," Insurance: Mathematics and Economics, Elsevier, vol. 31(2), pages 133-161, October.
    13. Denuit, Michel & Dhaene, Jan & Ribas, Carmen, 2001. "Does positive dependence between individual risks increase stop-loss premiums?," Insurance: Mathematics and Economics, Elsevier, vol. 28(3), pages 305-308, June.
    14. Panjer, H. H. & Lutek, B. W., 1983. "Practical aspects of stop-loss calculations," Insurance: Mathematics and Economics, Elsevier, vol. 2(3), pages 159-177, July.
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    Cited by:

    1. Rafał Wójcik & Charlie Wusuo Liu, 2022. "Bivariate Copula Trees for Gross Loss Aggregation with Positively Dependent Risks," Risks, MDPI, vol. 10(8), pages 1-24, July.

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