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Forecasting in an extended chain-ladder-type model

Author

Listed:
  • Di Kuang

    () (Aon, 8 Devonshire Square, London EC2M 4PL, U.K.)

  • Bent Nielsen

    () (Nuffield College, Oxford OX1 1NF, U.K.)

  • Jens Perch Nielsen

    () (Cass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ, U.K.)

Abstract

Reserving in general insurance is often done using chain-ladder-type methods. We propose a method aimed at situations where there is a sudden change in the economic environment affecting the policies for all accident years in the reserving triangle. It is shown that methods for forecasting non-stationary time series are helpful. We illustrate the method using data published in Barnett and Zehnwirth (2000). These data illustrate features we also found in data from the general insurer RSA during the recent credit crunch.

Suggested Citation

  • Di Kuang & Bent Nielsen & Jens Perch Nielsen, 2010. "Forecasting in an extended chain-ladder-type model," Economics Papers 2010-W05, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:1005
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    Cited by:

    1. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
    2. repec:eee:insuma:v:75:y:2017:i:c:p:117-125 is not listed on IDEAS
    3. 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.
    4. D Kuang & Bent Nielsen & J P Nielsen, 2013. "The Geometric Chain-Ladder," Economics Papers 2013-W11, Economics Group, Nuffield College, University of Oxford.
    5. María Dolores Martínez Miranda & Bent Nielsen & Jens Perch Nielsen, 2015. "Inference and forecasting in the age–period–cohort model with unknown exposure with an application to mesothelioma mortality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 29-55, 01.

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