Optimal Stopping Of A Credit Lending Process With Multiple Disorders

Abstract

Zhang studies optimal stopping time problems arising from risk management of credit lending processes. Loss is driven by the credit status of borrowers when multiple disorders are possible. Goals of lenders are to find stopping strategies to maximize expected profits, which are at risk due to the lack of principal and interest payment recovery when the borrower defaults. Zhang shows that optimal stopping times form threshold type policies. Applications of responsive energy load scheduling will also be discussed.

Speaker

Xiaoxuan Zhang is currently with risk management at JP Morgan, New York. Before joining JP Morgan, Xiaoxuan worked with risk and information management at American Express, New York, where she focused on a broad range of theoretical and applied credit risk and recommendation problems in large scale. Before joining Amex, Xiaoxuan was a Postdoctoral researcher at IBM Watson research, Yorktown Heights. Xiaoxuan's research interests include applied probability, stochastic control, as well as revenue management and pricing applied in financial risk management, retail supply chain and energy market mechanism design. Xiaoxuan received a Ph.D. in Operations Research at Applied Mathematics and Statistics department from Stony Brook University in 2010, and a B.S. in Mathematics from Nanjing University, China in 2005.