Jing Zhang manages a research team responsible for quantitative modeling behind EDF and LGD models for public and private firms, commercial real estate, and portfolio analytics. He and his researchers build balance sheet analytics and credit risk models that power award-winning solutions. Jing has held multiple senior roles in product management and client solutions, and has extensive experience advising clients on risk management issues.
Credit Risk Modeling: Moody’s Analytics delivers award-winning credit models and expert advisory services to provide you with best-in-class credit risk modeling solutions.
Stress Testing: Moody’s Analytics helps financial institutions develop collaborative, auditable, repeatable, and transparent stress testing programs to meet regulatory demands.
Regulatory Reporting: Moody's Analytics regulatory reporting solution delivers comprehensive, automated, and streamlined regulatory reporting.
Portfolio Models: Models that enable portfolio managers to assess and optimize portfolio risk.
Regulatory Capital: Amount of capital financial institutions must hold as required by financial regulators.
Stress Testing: Gauge of how certain stressors will affect a company, industry, or specific portfolio.
We examine past histories of recessions and crises to identify hidden vulnerabilities and weak links in the economy and financial system.
Climate risk is increasingly a discussion topic amongst financial market participants and regulators. And yet, there are still many questions about its provenance and impacts, and applications to best practices in the financial sectors. We will address these questions and more in an interactive, informative format.
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.
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.