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Modeling insurance claims via a mixture exponential model combined with peaks-over-threshold approach

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  • Lee, David
  • Li, Wai Keung
  • Wong, Tony Siu Tung

Abstract

We consider a model which allows data-driven threshold selection in extreme value analysis. A mixture exponential distribution is employed as the thin-tailed distribution in view of the special structure of insurance claims, where individuals are often grouped into categories. An EM algorithm-based procedure is described in model fitting. We then demonstrate how a multi-level fitting procedure will substantially reduce computation time when the data set is large. The fitted model is applied to derive statistics such as return level and expected tail loss of the claim distribution, and ruin probability bounds are obtained. Finally we propose a statistical test to justify the choice of mixture exponential distribution over the homogeneous exponential distribution in terms of improved fit.

Suggested Citation

  • Lee, David & Li, Wai Keung & Wong, Tony Siu Tung, 2012. "Modeling insurance claims via a mixture exponential model combined with peaks-over-threshold approach," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 538-550.
    • Handle: RePEc:eee:insuma:v:51:y:2012:i:3:p:538-550
    DOI: 10.1016/j.insmatheco.2012.07.008
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    References listed on IDEAS

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    1. Uditha Balasooriya & Chan-Kee Low, 2008. "Modeling Insurance Claims with Extreme Observations: Transformed Kernel Density and Generalized Lambda Distribution," North American Actuarial Journal, Taylor & Francis Journals, vol. 12(2), pages 129-142.
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    4. Gerber, Hans U. & Goovaerts, Marc J. & Kaas, Rob, 1987. "On the Probability and Severity of Ruin," ASTIN Bulletin, Cambridge University Press, vol. 17(2), pages 151-163, November.
    5. Dufresne, Francois & Gerber, Hans U., 1988. "The probability and severity of ruin for combinations of exponential claim amount distributions and their translations," Insurance: Mathematics and Economics, Elsevier, vol. 7(2), pages 75-80, April.
    6. Politis, Konstadinos, 2005. "Bounds for the probability and severity of ruin in the Sparre Andersen model," Insurance: Mathematics and Economics, Elsevier, vol. 36(2), pages 165-177, April.
    7. Simon Lee & X. Lin, 2010. "Modeling and Evaluating Insurance Losses Via Mixtures of Erlang Distributions," North American Actuarial Journal, Taylor & Francis Journals, vol. 14(1), pages 107-130.
    8. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
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    Citations

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    Cited by:

    1. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    2. Reynkens, Tom & Verbelen, Roel & Beirlant, Jan & Antonio, Katrien, 2017. "Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 65-77.
    3. Bignozzi, Valeria & Macci, Claudio & Petrella, Lea, 2018. "Large deviations for risk measures in finite mixture models," Insurance: Mathematics and Economics, Elsevier, vol. 80(C), pages 84-92.
    4. Li, Shengguo & Peng, Jin & Zhang, Bo, 2013. "The uncertain premium principle based on the distortion function," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 317-324.
    5. Sarra Ghaddab & Manel Kacem & Christian Peretti & Lotfi Belkacem, 2023. "Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty," Empirical Economics, Springer, vol. 65(3), pages 1105-1127, September.
    6. Hansjörg Albrecher & Martin Bladt & Eleni Vatamidou, 2021. "Efficient Simulation of Ruin Probabilities When Claims are Mixtures of Heavy and Light Tails," Methodology and Computing in Applied Probability, Springer, vol. 23(4), pages 1237-1255, December.
    7. Laudagé, Christian & Desmettre, Sascha & Wenzel, Jörg, 2019. "Severity modeling of extreme insurance claims for tariffication," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 77-92.
    8. Tong Siu Tung Wong & Wai Keung Li, 2015. "Extreme values identification in regression using a peaks-over-threshold approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 566-576, March.

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    More about this item

    Keywords

    Mixture exponential distribution; Extreme value theory; Threshold model; Mixture component testing; Insurance claims modeling;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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