A chain ladder method using prior information on calendar year effects

Calendar year effects introduce dependence in loss development data and challenge traditional reserving assumptions. This paper proposes an extension of the log-normal Mack chain ladder model, which closely aligns with Mack’s assumptions, by incorporating external information on the relative impact of calendar year effects. Parameter estimation is performed via an EM algorithm, and standard errors are derived. The approach enables a principled integration of market information and is illustrated using industry data exhibiting pronounced calendar year effects.