Insurance design with tail risk measure and likelihood ratio uncertainty

Friday, 1 December 2023

10.30 am - 12 noon

Venue: NTU Gaia, ABS Meeting Room 12 (ABS-06-118) 

Chairperson: Prof Wenjun Zhu

Abstract

The model uncertainty is of crucial importance when market participants are making risk management strategies. From a conservative consideration, decision makers seek the worst-case scenario among all conceivable models for a given risk measure. In this work, we use the likelihood ratio to quantify discrepancies between models, and study the optimal reinsurance strategy for an insurance company in the worst-case scenario. We establish a link between the insurer’s optimal strategies in scenarios with and without the model uncertainty. If the insurer’s optimal reinsurance is known under the reference model, 

applying our method, our approach facilitates the direct determination of the insurer’s optimal strategy, accounting for the likelihood ratio model uncertainty. Meanwhile, the likelihood ratio uncertainty has a close connection with tail risk measures. Therefore, our method is also applicable when the insurer adopts a tail risk measure in the optimization problem. 

Authors: David Landriault (University of Waterloo), Fangda Liu (UW), Ziyue Shi (UW), 

About the Speaker

Dr. Fangda Liu is an assistant professor at the Department of Statistics and Actuarial Science, University of Waterloo. She is also an associated fellow of the Society of Actuaries. Her research focuses on optimal insurance and reinsurance design, risk sharing problems, distributional model uncertainty and its application insurance.