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<p>This study introduces a log-normal chain ladder model, the LMCL model, designed to align closely with Mack’s chain ladder assumptions while enabling maximum likelihood estimation through a straightforward iterative algorithm. The LMCL model supports two reserving methods: the LMCL method (without bias correction) and the BLMCL method (with bias correction). We derive estimators for the mean squared errors of prediction, apply these methods to numerical examples, and compare the results to those from Mack’s chain ladder and Hertig’s log-normal chain ladder model.</p>

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A log-normal chain ladder model closely aligning with Mack’s assumptions

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Ulrich Riegel

摘要

This study introduces a log-normal chain ladder model, the LMCL model, designed to align closely with Mack’s chain ladder assumptions while enabling maximum likelihood estimation through a straightforward iterative algorithm. The LMCL model supports two reserving methods: the LMCL method (without bias correction) and the BLMCL method (with bias correction). We derive estimators for the mean squared errors of prediction, apply these methods to numerical examples, and compare the results to those from Mack’s chain ladder and Hertig’s log-normal chain ladder model.