Modeling new origination is important for forecasting the future dynamics of a portfolio, and it is becoming prevalent for banks to use these models for capital and risk management, stress testing, and strategic planning. In recent years regulators have laid out stress testing frameworks that focus on modeling the relationship between new origination and the macro environment. The main challenge with modeling new origination is finding data on new origination dynamics over time.
In this webinar we propose using the Loan Accounting System data extracted from Moody’s Analytics Credit Research Database to construct and examine new origination dynamics of C&I loans to middle-market borrowers over time, and highlight the different patterns that emerge for different portfolio segments. Our analysis shows how important different types of segmentation are for understanding new origination dynamics.
Loan Accounting System data validation
New origination of C&I loans
Understanding new origination dynamics for different portfolio segments (segmented by loan type, tenor, industry, and credit quality)
What Do 20 Million C&I Loan Observations Say about New Origination Dynamics? — Insights from Moody's Analytics CRD Data
We construct and examine new origination of C&I loans to middle-market borrowers using the Loan Accounting System data extracted from Moody's Analytics Credit Research Database (CRD/LAS). We find that C&I loan origination declines during the Great Recession and recovers soon after. The magnitude of the decline and the speed of the recovery varies across segments. For example, new lending to the financial industry decreases more than to the non-financial industry during the recession and recovers faster afterwards. Another example, new originations during the recession consists predominantly of short-term loans, while long-term lending becomes more dominant post crisis. This finding suggests that banks are using loan tenor as a means to mitigate risk during crises, at times even more so than credit quality.
February 2017 Pdf Dr. Pierre Xu, Tomer Yahalom, May Jeng
Portfolio risk analysis of structured instruments requires a suitable representation of the instrument calibrated to match tranche-level quantitative measures of risk. Such quantitative risk measures are hard to find; ratings are sticky, coarse, and hard to translate to quantitative risk measures, and analysis of structured instruments under one or a few prescribed scenarios cannot provide accurate tranche-level risk measures.
December 2016 WebPage Ian Ward, Tomer Yahalom
Traditional approaches to modeling economic capital, credit-VaR, or structured instruments whose underlying collateral is comprised of structured instruments treat structured instruments as a single-name credit instrument i.e., a loan-equivalent). While tractable, the loan-equivalent approach requires appropriate parameterization to achieve a reasonable description of the cross correlation between the structured instrument and the rest of the portfolio. This article provides an overview of how one can calibrate loan-equivalent correlation parameters. Results from taking the approach to the data suggest that structured instruments have far higher correlation parameters than single-name instruments.
November 2008 Pdf Tomer Yahalom, Dr. Amnon Levy, Andrew Kaplin