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October 2016

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, such as economic capital and unexpected loss, which may not be desirable in some applications.

Jimmy Huang, Associate Director of Portfolio Research at Moody’s Analytics will discuss two approaches that financial institutions can consider to estimate Through-the-Cycle (TTC) correlation parameters.

Webinar Highlights:

Average PIT measures across years to obtain a longer-term TTC average

Calibration of a TTC correlation measure that generates a default distribution in-line with the institution’s actual default distribution

Related Insights

Impact of Using EDF9 on Credit Portfolio Analysis

This paper investigates the impact of using EDF9 instead of EDF8 values as inputs for estimating credit portfolio risk measures within Moodys Analytics RiskFrontier®. The recent EDF9 enhancements affect portfolio risk analysis via various channels — due not only to new values for default probabilities, but also because the market Sharpe ratio (i.e. market-level risk premium) and asset return-based correlations for corporate exposures depend on time series of EDF measures. In this paper, we focus on the question of how using the new EDF9 default probabilities alter patterns in portfolio risk measures.

June 2017 Pdf Noelle Hong, Jimmy Huang, Libor Pospisil, Albert Lee, Marc Mitrovic, Sunny Kanugo, Tiago Pinheiro, Andriy Protsyk, Dr. Yashan Wang

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

Through-the-Cycle Correlations

In some instances, 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, their frequent updates may create fluctuations in risk measures, such as economic capital and unexpected loss, which may not be desirable in some applications. This paper outlines two approaches that financial institutions can consider to estimate Through-the-Cycle (TTC) correlation parameters. The first approach averages PIT measures across years to obtain a longer-term TTC average. The second approach calibrates a TTC correlation measure that generates a default distribution in-line with the institution's actual default distribution.

January 2016 Pdf Jimmy Huang, Dr. Amnon Levy, Libor Pospisil, Noelle Hong, Devansh Kumar Srivastava

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

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