The credit portfolio framework developed by Moody’s Analytics accounts for links between default risk and recovery risk. We refer to these links as PD-LGD correlations.
GCorr 2016 LGD leverages similar methodology for estimating parameters as the previous version of the PD-LGD correlation model, with some modifications required due to the nature of new data. While the previous version of the PD-LGD correlation model employed data provided by Moody’s Analytics LossCalc2.0 model, GCorr 2016 LGD utilizes LossCalc4.0 output data as well as the default-recovery database (DRD). In addition to differing data sources, GCorr 2016 LGD incorporates more recent data than the previous model, including the effects of the financial crisis of 2008-2009.
- Empirical patterns in recovery dynamics over recent periods
- New estimation of parameters describing link between defaults and recoveries
- Impact of the new parameters on portfolio risk metrics.
Moody's Analytics is pleased to announce the release of versions 5.3 and 5.4 of the RiskFrontier™ software. Join our experts as they discuss the latest enhancements and updates.
Moody's Analytics is pleased to announce the release of versions 5.3 and 5.4 of the RiskFrontier software. The latest version includes the following enhancements:
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.
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.
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.
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.