Journal article
Robust Actuarial Risk Analysis
North American Actuarial Journal, Vol.23(1), pp.33-63
01/02/2019
DOI: 10.1080/10920277.2018.1504686
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.
Details
- Title: Subtitle
- Robust Actuarial Risk Analysis
- Creators
- Jose Blanchet - Department of Management Science and Engineering, Stanford UniversityHenry Lam - Department of Industrial Engineering and Operations Research, Columbia UniversityQihe Tang - Department of Statistics and Actuarial Science, University of IowaZhongyi Yuan - Department of Risk Management, Pennsylvania State University
- Resource Type
- Journal article
- Publication Details
- North American Actuarial Journal, Vol.23(1), pp.33-63
- DOI
- 10.1080/10920277.2018.1504686
- ISSN
- 1092-0277
- eISSN
- 2325-0453
- Publisher
- Routledge
- Grant note
- Society of Actuaries
- Language
- English
- Date published
- 01/02/2019
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985829802771
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