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Clark, David R., Hang Ding, and Lianmin Zhou. 2022. “Making Bootstrap Reserve Ranges More Realistic.” CAS E-Forum Summer (September).
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  • Figure 2.1.1. Examples of excluded companies
  • Figure 2.1.2. Examples of companies with data issues but not excluded
  • Figure 2.1.3. An examples with normal triangle and abnormal ultimate
  • Figure 2.3.1
  • Figure 2.3.2
  • Figure 3.1
  • Figure 3.2


The use of bootstrapping methods to evaluate the variance and range of future payments has become very popular in reserving. However, prior studies have shown that the ranges produced may be too narrow and do not always contain the actual outcomes when performing back-testing. This paper will look at some ways that the ranges determined by bootstrapping methods can be made more realistic. A central idea will be relaxing the independence assumption and allowing correlation of the random draws in the bootstrap resampling. Using a publicly available dataset from Schedule P, we show that this can improve the back testing results.

Accepted: August 23, 2022 EDT