Please join this virtual talk via the Webex link:
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(Meeting number: 2622 091 4797 Password: 3DubXd5PFe8)

Abstract: Risk sharing and optimal reinsurance arrangements have been widely studied in the actuarial literature. Main focusses in the literature are on generalizing objective functions and incorporating more business-related constraints. In this talk, we will propose a risk-sharing framework in reinsurance with translation invariant preferences of the agents involved. We characterize the set of all Pareto-optimal reinsurance contracts. This is next extended to the case with multiple reinsurers and one insurer, and we assume that the reinsurers determine the premium in a competitive manner. This leads to a specific core-type constraint, and we show the competitive prices in closed-form in case the insurance agents minimize a distortion risk measure. Finally, we study the impact of multiple insurers on the optimal indemnity functions. Specifically, we characterize Pareto-optimal risk-sharing contracts in a market with multiple insurers and one (representative) reinsurer. In the special case of coherent risk measures, the optimal indemnity schedules are further characterized in explicit form, in terms of what can be called "worst-case probability measures". The results are illustrated using a case study of flood risk insurance in the United States.

Speaker's short bio: Dr. Boonen is an Associate Professor in Actuarial Science and Mathematical Finance at the University of Amsterdam. He obtained his PhD from Tilburg University in 2014. He received the 2022 SOA Actuarial Science Early Career Award. His research interests include Insurance, Actuarial risk theory, Mathematical finance, Game theory. Please visit his website https://www.uva.nl/en/profile/b/o/t.j.boonen/t.j.boonen.html for more information.

This is an in-person seminar at MONT 214.

Abstract: While driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concern or adverse selection compared to the data points with traditional features. To handle this issue, we propose a data integration technique based on calibration weights. It is shown that the proposed technique can efficiently integrate the so-called traditional data and telematics data and also cope with possible adverse selection issues on the availability of telematics data. Our findings are supported by a simulation study and empirical analysis on a synthetic telematics dataset.

Speaker's short bio: Himchan is an assistant professor in the Department of Statistics and Actuarial Science at Simon Fraser University, Canada. He is a Fellow of the Society of Actuaries (SOA) and holds a Ph.D. from the University of Connecticut. His current research interest is predictive modeling for ratemaking and reserving of property and casualty insurance. Please visit his website https://ssauljin.github.io/hjeong/ for more information.

Please join this virtual talk via the Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mb5ca02fcc5cc8a161cbdd248ae3d0584
(Meeting number: 2621 244 4271
Password: BQrNshT65M2)

Abstract: In non-life insurance, it is essential to understand the serial dynamics and dependence structure of the longitudinal insurance data before using them. Existing actuarial literature primarily focuses on modeling, which typically assumes a lack of serial dynamics and a pre-specified dependence structure of claims across multiple years. To fill in the research gap, we develop two diagnostic tests, namely the serial dynamic test and correlation test, to assess the appropriateness of these assumptions and provide justifiable modeling directions. The tests involve the following ingredients: i) computing the change of cross-sectional estimated model parameters and the empirical residual correlations of the claims across time, which serve as the indications to detect serial dynamics and peculiar serial dependence of claims; ii) quantifying estimation uncertainty using the randomly weighted bootstrap approach; iii) developing asymptotic theories to construct proper test statistics. The proposed tests are tested by simulated data and applied to multiple non-life insurance datasets, revealing that the real insurance datasets can behave very differently.

Speaker's short bio: Tsz (Samson) is an assistant professor at Robinson College of Business of Georgia State University. He holds a Ph.D. in statistics from the University of Toronto and a B.S. in actuarial science from the University of Hong Kong. He was a postdoc at ETH Zurich from Aug 2020 to Jul 2021, before joining Georgia State. Please see his google scholar page https://scholar.google.ca/citations?user=atdCdk8AAAAJ&hl=en for more information about his research.