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The chain ladder technique (equivalently, age-to-age development factors) is one of the oldest actuarial techniques to be applied widely for estimating loss reserves.
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Dec 1, 1994 · In this note, we show that a different distribution-free stochastic model is underlying the chain ladder method, i.e. yields exactly the same claims reserves as the usual chain ladder method.
- Thomas Mack
- 1994
May 17, 2019 · The chain ladder method (clm) calculates incurred but not reported (IBNR) loss estimates, using run-off triangles of paid losses and incurred losses, representing the sum of paid losses and case reserves.
Dec 20, 2002 · Because of the stochastic nature of the quantities to which the chain–ladder method is applied, several authors have studied the question whether the chain–ladder method can be justified by a stochastic model and a statistical method related to the model.
- Klaus Th. Hess, Klaus D. Schmidt
- 2002
Stochastic models underlying the CL algorithm. CL algorithm is not based on a stochastic model (deterministic algorithm). We need a stochastic representation to quantify prediction uncertainty. Stochastic models introduced providing the CL reserves: Mack's distribution-free CL model (1993)
A chain ladder process is a discrete-time, real-valued stochastic process fXj > 0g 0, such that for each j > 0. j. E [XjjXj 1; : : : ; X0] = fj Xj 1; V [XjjXj 1; : : : ; X0] = j Xj 1. with parameters fj > 0 (development factors) and j 0. Standard estimators from loss triangle, 1 j J: ^fj CIj;j. := ; CIj;j 1.
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Is the chain ladder method based on a distribution-free stochastic model?
Can a chain ladder be used in a stochastic model?
Can a stochastic model justify the chain-ladder method?
Is the chain ladder predictor unbiased?
Does the chain ladder technique include a random component?
What is the chain ladder method?
The chain ladder method is a simple and suggestive tool in claims reserving, and various attempts have been made aiming at its justification in a stochastic model. Remarkable progress has been achieved by Schnieper and Mack who considered models involving assumptions on conditional distributions.
