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Drawing inspiration from Markowitz portfolio theory, it leverages techniques from probability, statistics, and optimization to build algorithms that construct optimal risk insurable portfolios under budget constraints.
List of contents
1. Introduction. 2. Risk Retention Functions. 3. Balancing Retained Risk and Risk Transfer Cost. 4. Transferring Multiple Risks including Reinsurance. 5. Excess of Loss for Two Risks. 6. Managing Portfolios of Insurable Risks. 7. Simulating Multivariate Risks. 8. Risk Retention Case Studies. 9. Stress Testing, Sensitivity, and Robustness. 10. Sensitivity and Data Uncertainty. 11. Risk Retention Conditions. 12. The Role of Dependence in Managing Insurable Risks.
About the author
Edward W. Frees is an emeritus professor affiliated with the University of Wisconsin-Madison where he served as the Hickman Larson Chair of Actuarial Science. Until recently, he enjoyed a fractional research appointment with the Australian National University. He received his PhD in mathematical statistics from the University of North Carolina at Chapel Hill. He works at the intersection of data science and actuarial studies; he is a fellow of the American Statistical Association and was a fellow of the Society of Actuaries (SOA) (the only fellow of both organizations).
Prof. Frees has provided extensive service to the profession, including serving as the founding chairperson of the SOA Education and Research Section, member of the SOA Board of Directors, trustee of the Actuarial Foundation, editor of the
North American Actuarial Journal, and actuarial representative to the Social Security Advisory Board's Technical Panel on Methods and Assumptions. He has written three books, edited a two-volume series on predictive modeling applications in actuarial science, and is editing an online, open source book,
Loss Data Analytics.Regarding his research, Prof. Frees has published extensively and won several awards for his work. He has won the Society of Actuaries' Annual Prize for the best paper published by the Society, the SOA's Ed Lew Award for research in modeling, the Casualty Actuarial Society's Hachmeister award, and the Halmstad Prize for best paper published in the actuarial literature (four times).
Summary
Drawing inspiration from Markowitz portfolio theory, it leverages techniques from probability, statistics, and optimization to build algorithms that construct optimal risk insurable portfolios under budget constraints.
