Robust Actuarial Risk Analysis

Jose Blanchet, Henry Lam, Qihe Tang, Zhongyi Yuan

Research output: Contribution to journalArticle

Abstract

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)
JournalNorth American Actuarial Journal
DOIs
StatePublished - Jan 1 2019

Fingerprint

Risk Analysis
Optimization Problem
Conditional Value at Risk
Robust Estimate
Loss Probability
Model Error
Misspecification
Insurance
Probabilistic Model
Unknown
Methodology
Demonstrate
Risk analysis
Optimization problem
Model
Interpretation
Context
Insurance risk
Conditional value at risk
Probabilistic model

All Science Journal Classification (ASJC) codes

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

Cite this

Blanchet, Jose ; Lam, Henry ; Tang, Qihe ; Yuan, Zhongyi. / Robust Actuarial Risk Analysis. In: North American Actuarial Journal. 2019.
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Robust Actuarial Risk Analysis. / Blanchet, Jose; Lam, Henry; Tang, Qihe; Yuan, Zhongyi.

In: North American Actuarial Journal, 01.01.2019.

Research output: Contribution to journalArticle

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