Constructing Insurable Risk Portfolios
1st Edition
Published on 9. April 2025
338 pages
978-1-040-31077-9 (ISBN)
Description
Constructing Insurable Risk Portfolios offers a data-driven approach to devising risk retention programs that safeguard firms from a multitude of risks. Because firms face many risks, including fire damage to their buildings, liability from management misconduct, and external threats like cyberattacks, this book treats these potential liabilities as a "portfolio." Drawing inspiration from Markowitz portfolio theory, the text leverages techniques from probability, statistics, and optimization to build algorithms that construct optimal risk insurable portfolios under budget constraints.
Features
Through engaging case studies and supporting statistical (R) code, readers will learn how to build optimal insurable risk portfolios.
This book illustrates a frontier that depicts the trade-off between the uncertainty of a portfolio and the cost of risk transfer. This visual representation, mirroring familiar Markowitz investment tools, enables informed decision-making and easy adoption by risk advisors.
This book lays the mathematical groundwork for constructing optimal insurable risk portfolios in an effective and aesthetically pleasing manner.
For those interested in the detailed mathematical aspects of insurable risk portfolio optimization, comprehensive proofs and derivations are available in an online supplement.
This book equips students, academics, and practitioners with quantitative tools to analyze real-world risk portfolios. Additionally, it empowers financial analysts to provide data-driven insights that enhance their advisory roles for risk managers.
Features
Through engaging case studies and supporting statistical (R) code, readers will learn how to build optimal insurable risk portfolios.
This book illustrates a frontier that depicts the trade-off between the uncertainty of a portfolio and the cost of risk transfer. This visual representation, mirroring familiar Markowitz investment tools, enables informed decision-making and easy adoption by risk advisors.
This book lays the mathematical groundwork for constructing optimal insurable risk portfolios in an effective and aesthetically pleasing manner.
For those interested in the detailed mathematical aspects of insurable risk portfolio optimization, comprehensive proofs and derivations are available in an online supplement.
This book equips students, academics, and practitioners with quantitative tools to analyze real-world risk portfolios. Additionally, it empowers financial analysts to provide data-driven insights that enhance their advisory roles for risk managers.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
89 Tables, black and white; 80 Line drawings, black and white; 80 Illustrations, black and white
File size
12,64 MB
ISBN-13
978-1-040-31077-9 (9781040310779)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Edward W. Frees
Constructing Insurable Risk Portfolios
Book
05/2025
CRC Press
Unfortunately, price unknown
Available (delivery time upon request)
Edward W. Frees
Constructing Insurable Risk Portfolios
Book
04/2025
1st Edition
Chapman & Hall/CRC
€164.60
Shipment within 10-20 days
Person
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).
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).
Content
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.
