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.
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
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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.
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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.
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.
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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.
