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Mack the chain ladder predictor of ultimate aggregate claims is unbiased but shares this property with many other predictors (Section 6). Optimality of the chain ladder predictor of ultimate aggregate claims remains an open problem. Throughout this paper, let (fL 7, P) be a probability space.
chain-ladder bias The issue of chain-ladder bias was raised by James N. Stanard in his 1985 Proceedings pa-per, “A Simulation Test of Prediction Errors of Loss Reserve Estimation Techniques” [15]. Sta-nard simulated thousands of (5£5)2 loss rectan-gles,appliedfourprojectionmethods(viz.,chain-ladder or age-to-age, Bornhuetter-Ferguson,
allows to characterize optimality of the chain ladder factors as predictors of non-observable development factors and hence optimality of the chain ladder predictors of aggregate claims at the end of the first non-observable calendar year. We also present a model in which the chain ladder predictor of ultimate aggregate claims turns out to be ...
The model allows to characterize optimality of the chain ladder factors as predictors of non-observable development factors and hence optimality of the chain ladder predictors of aggregate claims at the end of the first non-observable calendar year.
- Klaus D. Schmidt, Anja Schnaus
- 1996
- What Is the Chain Ladder Method?
- Chain Ladder Method
- Key Assumptions
- Steps for Applying Chain Ladder Method
The Chain Ladder Method (CLM) is a method for computing the
requirement in an insurance company’s financial statement. The chain ladder method is used by insurers to forecast the amount of reserves that must be established in order to cover projected future claims by projecting past claims experience into the future. CLM therefore only works when prior patterns of losses are assumed to persist in the future. When insurer’s current claims experience changes for some reason, the chain-ladder method will not produce an accurate estimate without proper adjustments.
This actuarial method is one of the most popular reserve methods used by insurance companies. The chain ladder method can be compared with the
(ELR) method for calculating insurance company reserves.
The chain ladder method (CLM) is a popular way that insurance companies estimate their required claim reserves.
CLM computes incurred but not reported (IBNR) losses by way of run-off triangles, a probabilistic binomial tree that contains losses for the current year as well as premiums and prior loss estimators.
The chain ladder method 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. Insurance companies are required to set aside a portion of the premiums they receive from their
activities to pay for claims that may be filed in the future. The accuracy of claims forecasts and reserving has a big impact on an insurance company's financial situation.
At its core, the chain ladder method operates under the assumption that patterns in claims activities in the past will continue to be seen in the future. In order for this assumption to hold, data from past loss experiences must be accurate. Several factors can impact accuracy, including changes to the product offerings, regulatory and legal changes, periods of high severity claims, and changes in the claims settlement process. If the assumptions built into the model differ from observed claims, insurers may have to make adjustments to the model.
Creating estimations can be difficult because random fluctuations in claims data and a small data set can result in forecasting errors. To smooth over these problems, insurers combine both company claims data with data from the industry in general.
According to Jacqueline Friedland's "Estimating Unpaid Claims Using Basic Techniques," the seven steps to applying the chain-ladder method are:
Compile claims data in a development triangle
Calculate averages of the age-to-age factors
Calculate cumulative claim development factors
- Julia Kagan
We propose a new estimator for the ultimate prediction uncertainty within the famous Mack’s distribution-free chain-ladder model, which can be proved to be unbiased (conditionally given the first triangle column) under some additional technical assumptions. A peculiar behaviour of the unbiased estimator is given by its possible negativity.
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Is the chain ladder predictor unbiased?
Are chain ladder factors a predictor of non-observable development factors?
Is the chain ladder predictor of aggregate claims unbiased?
Is the chain ladder forecast biased upward?
What is the optimum predictor of the chain ladder?
Can a chain ladder be used in a stochastic model?
Efficiency of Chain Ladder Forecasts 9 5. Minimum variance unbiased estimation 5.1 Extended DFCL model By Section 3.3.2, the extended DFCL model is a special case of the DFCL model. Mack (1993) shows that the CL algorithm produces unbiased forecasts under the latter, and therefore under its extended form. He also shows that the F j
