A Log-normal chain ladder model closely aligning with Mack's assumptions

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      <subfield code="a">A Log-normal chain ladder model closely aligning with Mack's assumptions</subfield>
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      <subfield code="a">The article introduces the log-normal Mack chain ladder (LMCL) model, a stochastic formulation of the chain ladder method that is closely aligned with Mack's classical assumptions and compatible with maximum likelihood estimation. Simple iterative procedures are developed for parameter estimation, and reserving methods with and without bias correction are proposed. In addition, analytical estimators of the mean squared error of prediction are derived. The model is empirically compared with the traditional chain ladder method, Hertig's model, and other approaches, showing good performance even for triangles with high volatility</subfield>
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