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Nils Engler: Large exposure asymptotics in valuation and reserving

Time: Wed 2023-11-22 15.15 - 16.00

Location: Cramér room, Department of Mathematics, Albano

Participating: Nils Engler (SU)


First Project: Approximations of multi-period liability values by simple formulas
This paper is motivated by computational challenges arising in multi-period val- uation in insurance. Aggregate insurance liability cashflows typically correspond to stochastic payments several years into the future. However, insurance regulation re- quires that capital requirements are computed for a one-year horizon, by considering cashflows during the year and end-of-year liability values. This implies that liability values must be computed recursively, backwards in time, starting from the year of the most distant liability payments. Solving such backward recursions with paper and pen is rarely possible, and numerical solutions give rise to major computational challenges. The aim of this paper is to provide explicit and easily computable expressions for multi-period valuations that appear as limit objects for a sequence of multi-period models that converge in terms of conditional weak convergence. Such convergence appears naturally if we consider large insurance portfolios such that the liability cash- flows, appropriately centered and scaled, converge weakly as the size of the portfolio tends to infinity.

Second Project: 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, i.e. of compound Poisson type, are not consistent with Mack’s distribution-free chain lad- der. 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 estima- tor 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.