Senior Director, Research
Jun Chen is a senior director at Moody’s Analytics where he leads the commercial real estate (CRE) research team. His team conducts empirical research and develops quantitative models focused on CRE loan credit risk for Moody’s Analytics product and service offerings.
Jun has many years of experience and is an established domain expert in the real estate finance industry. His expertise covers a wide range, including areas such as commercial real estate market analysis, credit risk modeling, stress testing, and portfolio management. His work has been published widely in academic and professional journals and conferences. Jun has a PhD with a specialty in real estate finance and urban economics from the University of Southern California. He has an MA and a BA from Tongji University.
In this presentation for the CECL Quantification webinar series, we discuss how commercial real estate (CRE) models and methodologies can be leveraged to fulfill CECL requirements, and key considerations in transitioning these models.
The second in our CECL Quantification webinar series, this webinar discussed how commercial real estate (CRE) models and methodologies can be leveraged to fulfill CECL requirements, and key considerations in transitioning these models.
Estimating Commercial Real Estate (CRE) Stressed Loss Measures Under Federal Reserve 2015 Comprehensive Capital Analysis and Review (CCAR) Scenarios
The Comprehensive Capital Analysis and Review (CCAR) program is an annual capital adequacy exercise conducted under the requirements of the Dodd-Frank Wall Street Reform and Consumer Protection Act rules.
Estimating Commercial Real Estate (CRE) Stressed Loss Measures Under Federal Reserve 2013 Comprehensive Capital Analysis and Review (CCAR) Scenarios
Download this whitepaper to understand how Moody's Analytics' analysis derives the credit loss estimates for the CRE loan portfolios held by CCAR firms. Our analysis estimates that the expected nine quarter, cumulative CRE portfolio loss through the end of 2014 is 4.7% under the CCAR 2013 Severely Adverse scenario. We attribute the lower loss estimate compared to last year's stressed scenario to a number of factors, which we discuss.
The future remains inherently uncertain. This white paper describes how Scenario-based credit risk models are becoming a business necessity, given increased regulatory and internal risk management requirements for periodic stress tests.
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.