Speaker: Zhiwei Tong

Bio:

Dr. Tong earned his BSc in Statistics from the University of Science and Technology of China in 2016, an MS in Actuarial Science from the University of Iowa in 2018, and a PhD in Risk and Actuarial Studies from UNSW Sydney in late 2021. After completing his PhD, he joined the Department of Statistics and Actuarial Science at the University of Iowa as an Assistant Professor of Actuarial Science. His research interests lie in broad topics in quantitative risk management and the interface between actuarial science and statistics, with a special focus on asymptotic studies of credit portfolio losses, dependence modeling, insurance systemic risk, and modeling climate-related losses. His research has been supported by the Society of Actuaries.

Abstract:

This presentation will illustrate how varying strengths of dependence affect risk via two theoretical studies. In the first, we analyze a portfolio of defaultable assets with low individual risk but consider the impact of heavy-tailed idiosyncratic risk factors with two dependence scenarios among them. In the independence scenario, even with heavy-tailed idiosyncratic risk factors, the probability of substantial portfolio loss remains low unless a single asset carries a disproportionately large weight. In the asymptotic dependence scenario, the dependent idiosyncratic risk factors become the primary drivers of increased exceedance probability. In the second study, we investigate default risk propagation in a multilayer system. We derive asymptotic equivalences for the conditional probability of a cluster of defaults in one layer given a certain number of defaults in another. We find that even when intradependence is weak, the presence of strong interdependence results in the conditional probability being nonnegligible.