Managing Director and Global Head of Research and Modeling
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.
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.
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.
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.
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.
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.
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.
Managing Earnings Volatility and Uncertainty in the Supply and Demand for Regulatory Capital: The Impact of IFRS 9
This paper presents a novel modeling approach that allows for better management of the interplay between supply and demand dynamics for regulatory capital, combining an economic framework with regulatory capital and new loss recognition rules. The framework is particularly relevant in understanding the extent to which IFRS 9 can lead to more aggressive provisioning, which feeds into earnings volatility. Our approach provides guidance on how organizations can better manage their capital buffer, considering investment concentration, its impact on earnings volatility, and the relationship with regulatory capital requirements. Imperative to portfolio management, the framework recognizes the likelihood of a capital shortfall being significantly impacted by portfolio asset class, geography, industry, and name concentration, as extreme fluctuations in capital supply and demand occur more often for institutions holding more concentrated portfolios. Finally, we discuss integrated investment and strategic decision measures that account for the full spectrum of economic risks and interactions with regulatory and accounting rules, as well as instruments' contribution to earnings volatility and capital surplus dynamics.
The answer is a definitive “Yes,” suggesting increased project finance investment could become an important tool for addressing sluggish growth concerns brought about by the Great Recession. Empirical results, based on a comprehensive and unique project finance loan database not previously available, show that increasing project finance by one percentage point of GDP could increase real GDP growth per capita by 6 to 10 percent, with growth effects higher for upper-middle income and advanced economies. In other words, in these countries, if GDP per capita is growing at three percent annually, the boost provided by project finance could deliver cumulative, additional growth as high as two percent during the next five years. These results suggest that proposals for stimulating economic growth and productivity via increased project finance merit careful consideration. In contrast, in low-income countries, project finance appears to have less of an impact, possibly owing to deficiencies and weaknesses in financial systems and regulatory frameworks. By addressing these deficiencies, less developed countries could unleash increased growth and productivity.
This article outlines recent approaches to managing credit risk when facing regulatory capital requirements. We explore how institutions should best allocate capital and make economically-optimized investment decisions under regulatory capital constraints, such as those imposed by Basel or CCAR-style rules.
Commercial real estate (CRE) exposures represent a large market share of credit portfolios for many Canadian banks, credit unions, insurance companies, and asset managers. While this segment escaped the most recent financial crisis relatively safely, Canadian CRE loan portfolios may be facing heightened credit risks given the current changing market conditions, which include sliding oil prices and reduced demand for natural resources and possible interest rate hikes. Given this environment, is critical to use an objective credit risk measurement solution that quantifies CRE loan risks consistently and objectively in order to help assess, stress test, and manage loan portfolios. This paper presents Moody's Analytics Commercial Mortgage Metrics (CMM™) framework, tailored for Canadian CRE loan credit risk, forming the core of our Commercial Mortgage Metrics: Canada (CMM Canada™) product. Based on the well-established CMM U.S. model, our enhanced framework incorporates new factors that capture unique Canada CRE market dynamics and lending practices. We describe our modeling approaches for default probability, loss given default (LGD), Expected Loss (EL), and other related risk measures.