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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
Whitepaper

Measuring and Managing the Impact of IFRS 9 and CECL Requirements on Dynamics in Allowance, Earnings, and Bank Capital

Reserving for loan loss is one of the most important accounting aspects for banks. Its objective is to cover estimated losses on impaired financial instruments due to defaults and non-payment. Reserve measurement affects both the balance sheet and income statement. It impacts earnings, capital, dividends and bonuses, and attracts the attention of bank stakeholders ranging from the board of directors and regulators to equity investors. In response to the so-called “too-little, too-late” problem experienced with loan loss reserve during the Great Financial Crisis, accounting standard setters now require that banks provision against loan loss based on expected credit losses (ECL). Arguably, calculating the Expected Credit Loss Model under IFRS 9 and CECL presents a momentous accounting change for banks, with the new standards coming into effect sometime between 2018 and 2021, depending on the jurisdiction.

March 2018 Pdf Dr. Amnon Levy, Dr. Jing Zhang
Whitepaper

Economic Capital Model Validation: A Comparative Study

Using a long history of public firm defaults from Moody's Investor Services and Moody's Analytics, this study illustrates a validation approach for jointly testing the impact of PD and correlation upon model performance. We construct predicted default distributions using a variety of PD and correlation inputs and examine how the predicted distribution compares with the realized distribution. The comparison is done by looking at the percentile of realized defaults with respect to the predicted default distribution. We compare the performance of two typical portfolio parameterizations: (1) a through-the-cycle style parameterization using agency ratings-based long-term average default rates and Basel II correlations; and (2) a point-in-time style parameterization using public EDF credit measure, and Moody's Analytics Global Correlation Model (GCorr™). Results demonstrate that a through-the-cycle style parameterization results in a less conservative view of economic capital and substantial serial correlation in capital estimates. Results also show that when point-in-time measures are used, the tested economic capital model produces consistent and conservative economic capital estimates over time. A version of this paper appears in the Journal of Risk Model Validation, March 2013.

February 2018 Pdf Zhenya Hu, Dr. Amnon Levy, Dr. Jing Zhang
Article

Project Finance: The Potential Returns

Effective risk assessment approaches to project finance must reflect a true understanding of complex issues. These assessments include the macroeconomic context, which provides an early indication of the potential risks and returns of infrastructure investments.

October 2017 Pdf Dr. Jing Zhang, Kevin Kelhoffer, Jorge A. Chan-Lau