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.
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.
To help our clients build benchmark commercial and industrial (C&I) loss models for the Federal Reserve's Comprehensive Capital Analysis and Review (“CCAR”)/DFAST exercises, we have developed an approach designed specifically to calculate provisions for losses of C&I portfolios. Our approach utilizes Moody's Analytics probability of default (PD), loss given default (LGD), and exposure at default (EAD) econometric models, which are intuitive, parsimonious, make economic sense, and have good statistical fit. We construct these models using our public EDF credit measures, RiskCalc™ private firm EDF credit measures, and Moody's Default & Recovery Database and Credit Research Database.
A major source of firm funding and liquidity, credit lines can pose significant credit risk to the underwriting banks. Using a unique dataset pooled from multiple U.S. financial institutions, we empirically study the credit line usage of middle market corporate borrowers. We examine to what extent borrowers draw down their credit lines and the characteristics of those firms with high usage. We study how line usage changes with banks' internal ratings, collateral, and commitment size and through various economic cycles. We find that defaulted borrowers draw down more of their lines than non-defaulted borrowers. They also increase their usage when approaching default. Risky borrowers tend to utilize a higher percentage of their credit lines as well.
Comprehensive Capital Analysis and Review (CCAR) has quickly become one of the most dominant regulatory regimes for US banks in recent years. Of the new regulatory requirements BHCs must address, CCAR is widely considered to have the greatest influence on banks' risk management and business practices. Against the backdrop of CCAR's profound impact, there have been few, if any, systematic treatments of the subject. CCAR and Beyond: Capital Assessment, Stress Testing and Applications, a new book published by Risk Books, is designed as a unique source of information and insight from key figures involved in CCAR. This book, with fifteen contributed chapters, represents a timely, concerted and collective effort to provide comprehensive and authoritative coverage of CCAR and its implications.
In this presentation, Dr. Jing Zhang, Divisional Managing Director and Global Head of Quantitative Research at Moody's Analytics, shares new solutions to joint modeling of condition credit migration and default that are lightweight, simple to implement, and complementary to your bank's internal data and approach.
In this paper, we introduce two new measures that incorporate both RegC and EC: return on risk-adjusted capital (RORAC) and economic value added (EVA™). These measures allow institutions to rank-order their portfolios and potential deals in a way that accounts for economic risk and regulatory charges.
Required economic capital (EC) and regulatory capital (RegC) are two measures frequently used in loan origination and other decisions related to portfolio construction. EC accounts for economic risks such as diversification and concentration effects. When used in measures such as return on risk-adjusted capital (RORAC) or Economic Value Added (EVA™), EC can provide useful insights that allow institutions to optimize risk-return profiles, facilitate strategic planning and limit setting, as well as quantify risk appetite. Meanwhile, when RegC is binding, an institution faces a tangible cost, in that additional capital is needed for new investments that face a positive risk weight. Given these observations, both EC and RegC should influence decision making. After all, a deal with lower RegC but the same EC is favorable, and a deal with lower EC but the same RegC is favorable. In this paper, we formalize RORAC and EVA measures that incorporate both RegC and EC. The new measures allow institutions to rank-order their portfolios and potential deals in a way that accounts for economic risks and regulatory charges.
CCAR Stress Testing and Its Implications for Risk Modeling
In this whitepaper, we discuss our model-based approach for constructing investment grade and high yield corporate bond portfolios that consistently beat representative market benchmarks.
In this paper, we present the Moody's Analytics framework for measuring commercial real estate loan credit risk, which is the model at the core of our Commercial Mortgage Metrics (CMM)™ product. We describe our modeling approaches for default probability, loss given default (LGD), Expected Loss (EL), and other related risk measures.
This methodology takes a hard look at "too-big-to-fail" financials and the market value of the government's guarantee to them.
Corporate credit is an important investment class with potentially attractive returns. The asymmetric nature of corporate bond returns implies that investing in credit is not about picking “winners,” but rather about avoiding “losers” (i.e., defaults and spreads blow-ups). Investors can utilize quantitative measures of credit risk to minimize the risk of such events.
Assessing credit risk and ensuring the effectiveness and reliability of credit models are critically important to many risk managers and portfolio managers, especially during financial crises. This validation study examines the measurement accuracy of the portfolio credit risk models employed in Moody's Analytics RiskFrontier.
In this paper, we present a framework that links two commonly used risk metrics: default probabilities and credit spreads. This framework provides credit default swap-implied (CDS-implied) EDF™ (Expected Default Frequency) credit measures that can be compared directly with equity-based EDF credit measures.
Asset correlation and default probability are critical drivers in modeling portfolio credit risk. It is generally assumed, as in the Basel II Accord, that average asset correlation decreases with default probability. We examine the empirical validity of this assumption in this paper. Overall, we find little empirical support for this decreasing relationship in the data for corporate, commercial real estate (CRE), and retail exposures. For corporate exposures, there is no strong decreasing relationship between average asset correlation and default probability when firm size is properly accounted for. For CRE and retail exposures, the empirical evidence suggests that the relationship is more likely to be an increasing one.
Asset correlation is a critical driver in modeling portfolio credit risk. Despite its importance, there have been few studies on the empirical relationship between asset correlation and subsequently realized default correlation, and portfolio credit risk. This three three-way relationship is the focus of our study using U.S. public firm default data from 1981 to 2006. We find the magnitude of default-implied asset correlations is significantly higher than has been reported by other studies. There is a reasonably good agreement between our default-implied asset correlations and the asset correlation parameters in the Basel II Accord for large corporate borrowers. However, the recommended small size adjustment in the Basel II Accord still produces asset correlation higher than what we observe in our data. More importantly, we find that measuring asset correlation ex ante accurately can improve the measurement of subsequently realized default correlation and portfolio credit risk, in both statistical and economic terms. These results have several important practical implications for the calculation of economic and regulatory capital, and for pricing portfolio credit risk. Furthermore, the empirical framework that we developed in this paper can serve as a model validation framework for asset correlation models in measuring portfolio credit risk.
In the Moody's KMV Vasicek-Kealhofer (VK) model, asset values and asset returns are calculated separately. Moody's KMV GCorr™ uses weekly asset returns directly from the VK model to calculate asset correlations. As an alternative, asset returns estimated from monthly asset values from Credit Monitor® can be used to estimate asset correlations. This study shows that the asset returns backed out from asset values are vulnerable to capital structure changes and other corporate activities, especially for financial firms. The frequent capital structure changes in financial firms make their correlations from asset values much smaller than the correlations from VK asset returns. Moreover, it is demonstrated that confidence intervals for correlation estimates from three years of monthly returns are much wider than correlation estimates from three years of weekly VK asset returns.
Rigorously validating and monitoring the out-of-sample performance of correlation models is vital to their acceptance and successful implementation in practice. In this paper, by applying both statistical and economic criteria, we validate the out-of-sample performance of a number of asset correlation models using data on more than 27,000 firms worldwide. Our results show that well-constructed correlation models can produce reasonably accurate estimates of future correlations, thereby leading to more optimal portfolio construction and more accurate measurement of risk contributions of individual assets. The results also show that the widely used one factor models and Industry Average Model perform worse than suggested in the financial literature. Furthermore, we show that country effects play an important role in determining correlation structure. These results have significantly extended the current understanding of correlation models.