Actuarial Science and Quantitative Risk Management Seminars – Fall 2024

For online sessions, please use the following Zoom meeting room:

https://osu.zoom.us/j/91543120239?pwd=cCcAXbYqQI9VQyhkO7AaIAbYphyrEm.1
Meeting ID: 915 4312 0239
Password: 809706

Abstracts

Speaker: Wenyuan Li, University of Hong Kong

Title: Optimal life insurance and annuity decision under money illusion

Abstract: This paper investigates the optimal consumption, investment, and life insurance/annuity decisions for a family in an inflationary economy under money illusion. The family can invest in a financial market that consists of nominal bonds, inflation-linked bonds, and a stock index. The breadwinner can also purchase life insurance or annuities that are available continuously.  The family’s objective is to maximize the expected utility of a mixture of nominal and real consumption, as they partially overlook inflation and tend to think in terms of nominal rather than real monetary values. We formulate this life-cycle problem as a random horizon utility maximization problem and derive the optimal strategy. We calibrate our model to the U.S. data and demonstrate that money illusion decreases (increases) life insurance demand for young adults (middle-aged workers) and reduces annuity demand for retirees. Our findings highlight the role of financial literacy in an inflationary environment.

Short bio: Dr. Wenyuan Li is an assistant professor in the Department of Statistics and Actuarial Science at the University of Hong Kong. He obtained his PhD from the University of Waterloo, Canada. His research interests are optimal insurance design and pension management.

Speaker: Gee Y. Lee, Michigan State University

Title: Developments in Insurance Portfolio Risk Retention

Abstract: In this talk, new developments in the insurance risk retention problem of determining the optimal retention parameters will be explored in a multivariate context. Given an underlying claims distribution and premium constraint, an insurance company analyst may be interested in finding the optimal amount of risk to retain or, equivalently, which level of risk retention parameters to choose. The risk retention parameter may be deductible, upper limit, or coinsurance. Numerical approaches to solve the risk retention problem will be presented along with some details on how it can be applied to real data. The method will be illustrated with a case study using the Wisconsin Local Government Property Insurance Fund (LGPIF) data, where the minimum amount of aggregate premium to be collected will be used as a constraint to the optimization problem of finding the upper limits of the policies. Areas of future work will be discussed during the talk as well.

Speaker: Yechao Meng, University of Prince Edward Island

Title: Mortality Prediction via Age-Specific Band Selection

Abstract: Longevity risk, driven by increasing life expectancy, presents a major challenge for insurers, governments, and individuals. Inaccurate mortality projections can lead to pricing and valuation errors in life insurance and living benefits products, which can strain pension funds and annuity providers. An analysis of mortality data from the Human Mortality Database highlights similar patterns of development within certain age groups (e.g., young, middle-aged, and senior) and differences across them. Recent research suggests that these similarities in age-specific mortality trends can improve the accuracy of mortality models. However, traditional approaches in mortality literature model all ages together, attempting to capture these patterns by adding more parameters. This would overlook the potential drawbacks of using data with an inappropriate scope and incorporating data with conflicting signals, thus weakening the model’s performance.

Instead of increasing model complexity to account for these age-specific patterns, we propose a different approach that focuses on selecting the most relevant age ranges. This preserves model simplicity while enhancing effectiveness. The innovation lies in an age-specific solution, where an age-specific band is used to borrow information from “neighboring” ages and build prediction models for each individual age in a mortality table. By carefully screening data to balance relevant signals and exclude noise, we improve the accuracy of mortality rate predictions. This concept is further extended to share information across multiple populations and ages simultaneously. Extensive numerical analyses using the Human Mortality Database (HMD) consistently show improvements in prediction accuracy across various scenarios.

Speaker: Jackson Lautier, Bentley University

Title: On the Convergence of Credit Risk in Current Consumer Automobile Loans

Abstract: Risk-based pricing within consumer lending is ubiquitous. It considers both prevailing interest rates and the credit profile of a borrower to determine the cost of borrowing.  All else equal, higher default risks pay higher borrowing costs.  This cost is the annual percentage rate (APR), and it is set at the loan’s origination.  A borrower’s credit profile is dynamic, however, and the risk of default gradually declines for current loans.  In this article, we derive a novel large-sample statistical hypothesis test suitable for loans sampled from asset-backed securities to populate a credit risk transition matrix between consumer credit risk groups.  We find that current loans in all risk groups eventually converge to the top credit tier before scheduled termination, a phenomenon we call credit risk convergence.  We then use these convergence estimates for two empirical economic studies.  We first estimate that lender conditional risk-adjusted expected profits significantly increase as high-risk, high-APR borrowers stay active and paying. We then estimate current borrowers are entitled to $1,153-$2,327 in potential credit-based savings from their improving risk profiles. Because we study consumer auto loans, a large-scale and essential economic good, we opine on the social implications of these results and suggest areas of further study.

Zoom Link for the Fall Semester 2023

https://osu.zoom.us/j/95548356683?pwd=a0hJMTgwbzlYeFpycGlnUTJ0RjM2Zz09

Meeting ID: 955 4835 6683

Password: 330496

Abstracts

Speaker: Maochao Xu, Illinois State University

Title:  Multivariate Cyber Risks: Quantification and Dependence 

Abstract: Cyber risk has emerged as a significant threat in recent years, with potential consequences including exposure of sensitive information, identity fraud, and financial losses. Quantifying cyber risk is critical for effective risk management and underwriting in the insurance industry. However, due to the complex nature of cyber risks, assessing joint cyber risk is a significant challenge, particularly concerning network topology and risk propagation. This talk presents a novel approach to quantifying multivariate cyber risks from a micro-level perspective. We will also explore dependence-related questions, including the nature of dependence among cyber risks and how to measure it. We shed light on these interesting questions using the newly developed L-hop model. Additionally, we discuss how our approach can add value to the industry practice of cyber risk quantification and pricing.

Short bio: Dr. Maochao Xu is a Professor in the Department of Mathematics at Illinois State University. In addition to his academic role, he serves as a Cyber Risk Advisor for CloudCover Inc. and a Cyber Insurance Advisor for Rankiteo LTD. Dr. Xu also provides advisory and consultancy services in cyber insurance to various industry companies. His research focuses on cyber insurance, statistical modeling, and risk analysis, with his work being published in prestigious journals and receiving numerous awards, including the 2019 Best Paper Award from the SOA.

Speaker: Runhuan Feng, Tsinghua University 

Title: Crypto-Based and Distributed Insurance

Abstract: It is fairly common in developed economies that a small set of insurers with large capitalization often account for the majority of their insurance markets. While tight regulations of the insurance industry are well-intended to protect the interests of policyholders and ensure market stability, the legal compliance and capital requirements create prohibitively high barriers that prevent retail investors or small companies from entering the market, further exacerbating the consolidation of the market. The advancement of distributed ledger technology has enabled new models to transfer risks from policyholders to crypto capital market. There has been little to no previous study on the underpinning theory of such new mechanisms. We propose a new theoretical framework for distributed insurance, where risks and rewards can be spread in a large distributed network of retail investors, as opposed to the traditional practice of risk concentrations on insurers. Our findings show that distributed risk sharing can significantly reduce the cost of coverage, improve capital efficiency while meeting the needs for limited liabilities and common investment principles for retail investors.