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Building Loss Models

  • Krzysztof Burnecki
  • Joanna Janczura
  • Rafal Weron

This paper is intended as a guide to building insurance risk (loss) models. A typical model for insurance risk, the so-called collective risk model, treats the aggregate loss as having a compound distribution with two main components: one characterizing the arrival of claims and another describing the severity (or size) of loss resulting from the occurrence of a claim. In this paper we first present efficient simulation algorithms for several classes of claim arrival processes. Then we review a collection of loss distributions and present methods that can be used to assess the goodness-of-fit of the claim size distribution. The collective risk model is often used in health insurance and in general insurance, whenever the main risk components are the number of insurance claims and the amount of the claims. It can also be used for modeling other non-insurance product risks, such as credit and operational risk.

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Paper provided by Hugo Steinhaus Center, Wroclaw University of Technology in its series HSC Research Reports with number HSC/10/03.

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Length: 40 pages
Date of creation: 2010
Date of revision:
Handle: RePEc:wuu:wpaper:hsc1003
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  1. Weron, Rafał, 2004. "Computationally intensive Value at Risk calculations," Papers 2004,32, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
  2. Christian Basteck & Tijmen R. Daniëls & Frank Heinemann, 2010. "Characterising Equilibrium Selection in Global Games with Strategic Complementarities," SFB 649 Discussion Papers SFB649DP2010-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Hormann, W., 1993. "The transformed rejection method for generating Poisson random variables," Insurance: Mathematics and Economics, Elsevier, vol. 12(1), pages 39-45, February.
  4. Erik R. Fasten & Dirk Hofmann, 2010. "Two-sided Certification: The market for Rating Agencies," SFB 649 Discussion Papers SFB649DP2010-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Anna Chernobai & Krzysztof Burnecki & Svetlozar Rachev & Stefan Trück & Rafał Weron, 2006. "Modelling catastrophe claims with left-truncated severity distributions," Computational Statistics, Springer, vol. 21(3), pages 537-555, December.
  6. Härdle, Wolfgang Karl & Burnecki, Krzysztof & Weron, Rafał, 2004. "Simulation of risk processes," Papers 2004,01, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
  7. Juliane Scheffel, 2010. "Honey, I’ll Be Working Late Tonight. The Effect of Individual Work Routines on Leisure Time Synchronization of Couples," SFB 649 Discussion Papers SFB649DP2010-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  8. Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.
  9. Burnecki, Krzysztof & Misiorek, Adam & Weron, Rafal, 2010. "Loss Distributions," MPRA Paper 22163, University Library of Munich, Germany.
  10. Fasten, Erik R. & Hofmann, Dirk, 2010. "Two-sided Certification: The market for Rating Agencies," Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 338, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
  11. Krzysztof Burnecki & Grzegorz Kukla & Rafal Weron, 2000. "Property insurance loss distributions," HSC Research Reports HSC/00/03, Hugo Steinhaus Center, Wroclaw University of Technology.
  12. Wolfgang Karl Härdle & Yuichi Mori & Jürgen Symanzik, 2012. "Computational Statistics (Journal)," SFB 649 Discussion Papers SFB649DP2012-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  13. Dorothee Schneider, 2010. "The Impact of ICT Investments on the Relative Demand for High-, Medium-, and Low-Skilled Workers: Industry versus Country Analysis," SFB 649 Discussion Papers SFB649DP2010-017, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  14. Wolfgang Karl Härdle & Ostap Okhrin & Yarema Okhrin, 2010. "Time varying Hierarchical Archimedean Copulae," SFB 649 Discussion Papers SFB649DP2010-018, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  15. Puriya Abbassi & Dieter Nautz, 2010. "Monetary Transmission Right from the Start: The (Dis)Connection Between the Money Market and the ECB’s Main Refinancing Rates," SFB 649 Discussion Papers SFB649DP2010-019, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  16. Pavel Cizek & Wolfgang Karl Härdle & Rafal Weron, 2005. "Statistical Tools for Finance and Insurance," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0501.
  17. Wolfgang Karl Härdle & Yarema Okhrin & Weining Wang, 2010. "Uniform confidence bands for pricing kernels," SFB 649 Discussion Papers SFB649DP2010-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  18. Tse,Yiu-Kuen, 2009. "Nonlife Actuarial Models," Cambridge Books, Cambridge University Press, number 9780521764650, October.
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