ASTIN Webinar: Mack’s estimator motivated by large exposure

ASTIN WEBINAR 11 June 2024 08:00 EDT | 18:00

11 June, 2024
10:00 - 11:00 EDT
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This webinar titled "Mack’s estimator motivated by large exposure asymptotics in a compound poisson setting" The distribution-free chain ladder of Mack justified the use of the chain ladder predictor and enabled Mack to derive an estimator of conditional mean squared error of prediction for the chain ladder predictor. Classical insurance loss models, that is of compound Poisson type, are not consistent with Mack’s distribution-free chain ladder. However, for a sequence of compound Poisson loss models indexed by exposure (e.g., number of contracts), we show that the chain ladder predictor and Mack’s estimator of conditional mean squared error of prediction can be derived by considering large exposure asymptotics. Hence, quantifying chain ladder prediction uncertainty can be done with Mack’s estimator without relying on the validity of the model assumptions of the distribution-free chain ladder.



Filip Lindskog
Filip obtained a PhD in mathematics at the Swiss Federal Institute of Technology (ETHZ) and is currently Professor of Insurance Mathematics at Stockholm University. He is head of the division Mathematical Statistics at Stockholm University, director of the Master’s Program in Actuarial Mathematics, and an editor of Scandinavian Actuarial Journal.


Brian Fannin
Brian Fannin has been an actuary for over 20 years. The data lack sufficient credibility for him to give a more precise estimate. Brian has been an Associate of the CAS since 2002 and a Certified Specialist in Predictive Analytics (CSPA) since 2017. He has worked in a variety of roles in commercial insurance, both primary and excess, here in the US as well as Europe, London and Asia. He has taught various workshops and seminars on R and is the author of the book “R for Actuaries and Data Scientists”, published by Actex. He currently works for Milliman, supporting their Arius loss reserving software.

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6/11/2024 10:00 AM - 11:00 AM