TY - JOUR
T1 - Robust Actuarial Risk Analysis
AU - Blanchet, Jose
AU - Lam, Henry
AU - Tang, Qihe
AU - Yuan, Zhongyi
N1 - Funding Information:
This project was supported by the Society of Actuaries research grant entitled “Modeling and Analyzing Extreme Risks in Insurance.”
Publisher Copyright:
© 2019, © 2019 Society of Actuaries.
PY - 2019/1/2
Y1 - 2019/1/2
N2 - 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.
AB - 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.
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U2 - 10.1080/10920277.2018.1504686
DO - 10.1080/10920277.2018.1504686
M3 - Article
AN - SCOPUS:85061316908
SN - 1092-0277
VL - 23
SP - 33
EP - 63
JO - North American Actuarial Journal
JF - North American Actuarial Journal
IS - 1
ER -