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

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003  MAP
005  20260422174843.0
008  260421e20260413che|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20140007493‎$a‎Riegel, Ulrich
24512‎$a‎A Log-normal chain ladder model closely aligning with Mack's assumptions‎$c‎Ulrich Riegel
520  ‎$a‎The 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
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
650 4‎$0‎MAPA20080598532‎$a‎Provisiones técnicas
650 4‎$0‎MAPA20080573935‎$a‎Seguros no vida
650 4‎$0‎MAPA20080606688‎$a‎Inferencia estadística
650 4‎$0‎MAPA20080597733‎$a‎Modelos estadísticos
7102 ‎$0‎MAPA20180008764‎$a‎Springer
7730 ‎$w‎MAP20220007085‎$g‎13/04/2026 Número 16 issue 1 - abril 2026 , 68 p.‎$t‎European Actuarial Journal‎$d‎Cham, Switzerland : Springer Nature Switzerland AG, 2021-2022