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Stochastic Loss Reserving in Discrete Time: Individual vs. Aggregate Data Models

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  • Jinlong Huang
  • Chunjuan Qiu
  • Xianyi Wu

Abstract

In this paper, a stochastic individual data model is considered. It accommodates occurrence times, reporting, and settlement delays and severity of every individual claims. This formulation gives rise to a model for the corresponding aggregate data under which classical chain ladder and Bornhuetter–Ferguson algorithms apply. A claims reserving algorithm is developed under this individual data model and comparisons of its performance with chain ladder and Bornhuetter–Ferguson algorithms are made to reveal the effects of using individual data to instead aggregate data. The research findings indicate a remarkable promotion in accuracy of loss reserving, especially when the claims amounts are not too heavy-tailed.

Suggested Citation

  • Jinlong Huang & Chunjuan Qiu & Xianyi Wu, 2015. "Stochastic Loss Reserving in Discrete Time: Individual vs. Aggregate Data Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(10), pages 2180-2206, May.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:10:p:2180-2206
    DOI: 10.1080/03610926.2014.976473
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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. Francis Duval & Mathieu Pigeon, 2019. "Individual Loss Reserving Using a Gradient Boosting-Based Approach," Risks, MDPI, vol. 7(3), pages 1-18, July.
    3. Lu Xiong & Vajira Manathunga & Jiyao Luo & Nicholas Dennison & Ruicheng Zhang & Zhenhai Xiang, 2023. "AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods," Risks, MDPI, vol. 11(7), pages 1-17, July.
    4. Benjamin Avanzi & Gregory Clive Taylor & Bernard Wong & Xinda Yang, 2020. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Papers 2004.11169, arXiv.org, revised Dec 2020.

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