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Double Chain Ladder

Author

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  • Miranda, María Dolores Martínez
  • Nielsen, Jens Perch
  • Verrall, Richard

Abstract

By adding the information of reported count data to a classical triangle of reserving data, we derive a suprisingly simple method for forecasting IBNR and RBNS claims. A simple relationship between development factors allows to involve and then estimate the reporting and payment delay. Bootstrap methods provide prediction errors and make possible the inference about IBNR and RBNS claims, separately.

Suggested Citation

  • Miranda, María Dolores Martínez & Nielsen, Jens Perch & Verrall, Richard, 2012. "Double Chain Ladder," ASTIN Bulletin, Cambridge University Press, vol. 42(1), pages 59-76, May.
    • Handle: RePEc:cup:astinb:v:42:y:2012:i:01:p:59-76_00
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    Cited by:

    1. Huang, Jinlong & Wu, Xianyi & Zhou, Xian, 2016. "Asymptotic behaviors of stochastic reserving: Aggregate versus individual models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 657-666.
    2. Kevin Kuo, 2019. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Risks, MDPI, vol. 7(3), pages 1-12, September.
    3. Margraf, Carolin & Elpidorou, Valandis & Verrall, Richard, 2018. "Claims reserving in the presence of excess-of-loss reinsurance using micro models based on aggregate data," Insurance: Mathematics and Economics, Elsevier, vol. 80(C), pages 54-65.
    4. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2014. "Individual loss reserving using paid–incurred data," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 121-131.
    5. Michel Denuit & Yang Lu, 2021. "Wishart‐gamma random effects models with applications to nonlife insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(2), pages 443-481, June.
    6. Wahl, Felix, 2019. "Explicit moments for a class of micro-models in non-life insurance," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 140-156.
    7. Wahl, Felix & Lindholm, Mathias & Verrall, Richard, 2019. "The collective reserving model," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 34-50.
    8. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2015. "In-sample forecasting applied to reserving and mesothelioma mortality," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 76-86.
    9. Lindholm, Mathias & Verrall, Richard, 2020. "Regression based reserving models and partial information," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 109-124.

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