Robust Actuarial Risk Analysis

Jose Blanchet, Henry Lam, Qihe Tang, Zhongyi Yuan

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This article investigates techniques for the assessment of model error in the context of insurance risk analysis. The methodology is based on finding robust estimates for actuarial quantities of interest, which are obtained by solving optimization problems over the unknown probabilistic models, with constraints capturing potential nonparametric misspecification of the true model. We demonstrate the solution techniques and the interpretations of these optimization problems, and illustrate several examples, including calculating loss probabilities and conditional value-at-risk.

Original languageEnglish (US)
Pages (from-to)33-63
Number of pages31
JournalNorth American Actuarial Journal
Issue number1
StatePublished - Jan 2 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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