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Jing’s group is responsible for the quantitative modeling behind the EDF and LGD models for both public and private firms, commercial real estate, and portfolio and balance sheet analytics. Jing joined the research team at the former KMV in 1998, eventually becoming a Director in the research group. In that role, besides managing day-to-day research operations, he made major contributions to a number of KMV quantitative models.

Jing obtained his PhD from the Wharton School of the University of Pennsylvania and his MA from Tulane University. He was a lecturer for the Master of Financial Engineering program at the University of California, Berkeley from 2010 to 2012. He is also the editor of "CCAR and Beyond - Capital Assessment, Stress Testing and Applications" published by Risk Books.

Related Insights

Introduction to CECL Quantification Webinar Slides

In this presentation, our experts Emil Lopez and Jing Zhang, introduce some key CECL quantification methodologies and enhancements that can be made to existing approaches to make them CECL compliant.

February 2017 Pdf Emil LopezDr. Jing Zhang

CECL Webinar Series: Introduction to CECL Quantification

In this presentation, our experts Emil Lopez and Jing Zhang, introduce some key CECL quantification methodologies and enhancements that can be made to existing approaches to make them CECL-compliant.

February 2017 WebPage Emil LopezDr. Jing Zhang

Measuring and Managing Credit Earnings Volatility of a Loan Portfolio Under IFRS 9

IFRS 9 materially changes how institutions set aside loss allowance. With allowances flowing into earnings, the new rules can have dramatic effects on earnings volatility. In this paper, we propose general methodologies to measure and manage credit earnings volatility of a loan portfolio under IFRS 9. We walk through IFRS 9 rules and the different mechanisms that it interacts with which flow into earnings dynamics. We demonstrate that earnings will be impacted significantly by credit migration under IFRS 9. In addition, the increased sensitivity to migration will be further compounded by the impact of correlation and concentration. We propose a modeling framework that measures portfolio credit earnings volatility and discuss several metrics that can be used to better manage earnings risk.

January 2017 Pdf Dr. Amnon LevyDr. Yanping PanDr. Yashan Wang, Dr. Pierre Xu, Dr. Jing Zhang, Xuan Liang