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Uncertainty in Asset Correlation Estimates and Its Impact on Credit Portfolio Risk Measures

Credit portfolio models rely on estimated and calibrated parameters, such as default and rating migration probabilities, recovery rates, and asset correlations. Users of these models must understand how various errors in the parameter estimates impact model outputs, for example Unexpected Loss (UL) or Economic Capital (EC). Asset correlations estimated using asset return time series are subject to inherent uncertainty — statistical errors — arising due to a limited length of the time series. The main question this paper addresses is how these errors translate into statistical errors in the estimated UL and EC. We illustrate several properties of the errors using an analytical method. As expected, longer time series lead to lower errors in UL and EC. Increasing the number of exposures in a portfolio, however, can reduce the errors in UL and EC only to a certain degree.

March 2018 Pdf Jimmy Huang, Libor Pospisil

Weighing the Wealth Effect

In this webinar, Mark Zandi and the Moody's Analytics team discuss the impact of the wealth effect on economic expansion and quantify econometric estimates based on data from Visa and Equifax.

December 2017 WebPage Scott Hoyt, Brian Poi, Mark Zandi

Subprime Auto Credit: Navigating Risks on the Horizon

Auto lending is following a natural and expected credit cycle. Subprime performance will get better as credit tightens. Nonbank auto financiers are facing the highest loss rates when lending to low-income, subprime borrowers. Residual value pressures should begin to abate but will likely increase for trucks and SUVs.

August 2017 WebPage Michael Vogan

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

Quantitative Research Webinar Series: Modeling Through-the-Cycle Correlations

Many financial institutions prefer to take longer-term views when assessing the risks of their credit portfolio. While forward-looking or Point-in-Time (PIT) parameters might be more reflective of the current economic environment, frequent updates may create fluctuations in risk measures.

October 2016 WebPage Jimmy Huang

Using GCorr® Macro for Multi-Period Stress Testing of Credit Portfolios

This document presents a credit portfolio stress testing method that analytically determines multi-period expected losses under various macroeconomic scenarios. The methodology utilizes Moody's Analytics Global Correlation Model (GCorr) Macro model within the credit portfolio modeling framework. GCorr Macro links the systematic credit factors from GCorr to observable macroeconomic variables. We describe the stress testing calculations and estimation of GCorr Macro parameters and present several validation exercises for portfolios from various regions of the world and of various asset classes.

April 2016 Pdf Noelle Hong, Jimmy Huang, Albert Lee, Dr. Amnon Levy, Marc Mitrovic, Libor Pospisil, Olcay Ozkanoglu

GCorr™ Emerging Markets

Moody's Analytics GCorr™ Corporate model provides asset correlations of corporate borrowers for credit portfolio analysis. The GCorr Corporate model is based on 49 country factors. This paper introduces a new model, GCorr Emerging Markets, designed with more than 200 country-factors including emerging markets worldwide. The methodology expands GCorr Corporate's 49 country factors to 200+ factors, each representing individual countries to better measure country concentration and diversification effects. The expanded factors cover predominately emerging market countries where we lack firm-level asset return data. For this reason, we refer to the extension as the GCorr Emerging Markets model. This model allows financial institutions with commercial exposures to smaller and emerging countries to better describe correlations across these countries, as well as to better capture diversification effects when investing in a wide cross-section of these countries.

July 2015 Pdf Jimmy Huang, Libor Pospisil, Noelle Hong

Quantifying Risk Appetite in Limit Setting

In this paper, we explore leveraging an organization's economic capital framework to quantify the RAS via risk- and macro scenario-based limits.

June 2015 Pdf Andrew Kaplin, Dr. Amnon Levy, Qiang Meng, Libor Pospisil

Linking Stress Testing and Portfolio Credit Risk

Nihil Patel, Senior Director, provides insight on how to link stress testing with portfolio credit risk for a comprehensive risk management solution.

October 2013 Pdf Nihil Patel

An Overview of Modeling Credit Portfolios

This document provides a high-level overview of the modeling methodologies implemented in Moody's Analytics RiskFrontier™. To address the challenges faced by credit risk or credit portfolio managers, RiskFrontier models a credit investment's value at the analysis date, its value distribution at some investment horizon, as well as the portfolio-referent risk of every instrument in the portfolio. The approach is designed to explicitly analyze a wide range of credit investments and contingencies, including term loans with prepayment options and grid pricing, dynamic utilization in revolving lines of credit, bonds with put and call options, equities, credit default swaps, retail instruments, commercial real estate loans, and structured instruments.

June 2013 Pdf Dr. Amnon Levy

Applications of GCorr™ Macro: Risk Integration, Stress Testing, and Reverse Stress Testing

This research develops an approach to expand the Moody's Analytics Global Correlation Model (GCorr) to include macroeconomic variables. Within the context of this document, macroeconomic variables can include financial market variables, economic activity variables, and other risk factors. The expanded correlation model, known as GCorr Macro, lends itself to several functions that facilitate a cohesive and holistic risk management practice.

April 2013 Pdf Mariano Lanfranconi, Libor Pospisil, Andrew Kaplin, Dr. Amnon Levy, Nihil Patel

Modeling Credit Correlations: An Overview of the Moody's Analytics GCorr Model

The Moody's Analytics Global Correlation Model (GCorr™) is a multi-factor model for asset correlations. This document provides an overview of the GCorr framework, methodology, data used for estimation, and validation. In addition, this document describes the components of GCorr related to individual asset classes and their integration. The asset classes explicitly included in GCorr are: public firms, private firms, small and medium-sized enterprises, sovereigns, U.S. commercial real estate, and U.S. retail.

December 2012 Pdf Jimmy Huang, Mariano Lanfranconi, Nihil Patel, Libor Pospisil

Risk Integration: New Top-down Approaches and Correlation Calibration

While the sophistication and adoption of the data, models, and software systems for individual risk types has become more widespread, the tools for consistently measuring integrated risk lag. Typically, individual risk components are aggregated in ways ranging from simple summation to employing copula methods that describe the relationship between risk types. While useful, these “top-down” approaches are limited in their ability to describe the interactive effects of various risk factors that drive loss.

January 2010 Pdf Nan Chen, Andrew Kaplin, Dr. Amnon Levy, Dr. Yashan Wang

The Relationship Between Average Asset Correlation and Default Probability

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.

July 2009 Pdf Joseph Lee, Joy Wang, Dr. Jing Zhang

Modeling Correlation of Structured Instruments in a Portfolio Setting

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

Asset Correlation, Realized Default Correlation, and Portfolio Credit Risk

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.

March 2008 Pdf Dr. Jing Zhang, Fanlin Zhu, Joseph Lee
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