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    Dr. Jing Zhang

    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.

    Published Work
    Whitepaper

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

    This paper explores how CECL and IFRS 9 might impact loss allowance, earnings, and capital dynamics, and how these dynamics might affect credit portfolio management.

    April 2018
    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
    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
    Presentation

    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
    Webinar-on-Demand

    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
    Whitepaper

    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