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January 2018

Moody's Analytics is pleased to announce the release of version 5.2 of the RiskFrontier™ software, which includes the GCorr™ 2017 Update.

The latest version includes the following enhancements:

GCorr™ 2017 Updates

The following correlation modules were updated: GCorr Corporate, Emerging Markets, U.S. Commercial Real Estate, U.S. Retail, Canada Retail, Macro, and Interest Rate Risk. In addition, GCorr 2017 was expanded to include a new module – Canada Commercial Real Estate. The new framework allows for the concentration and diversification effects of Canadian Commercial Real Estate exposures to be properly captured.

Credit Earnings Metrics Improvements

With RiskFrontier 5.2, clients can now input their own loss allowance estimations as of the analysis date, calculate credit earnings under different staging assumptions (IFRS 9, CECL, and 1 Year ECL), access new outputs, and view and export results at the portfolio and exposure level.

Composite Capital Measure Simplifications

The composite capital measure module allows users to integrate regulatory capital, while taking into account concentration effect, and diversification benefit of the assets in the portfolio. The method and outputs of composite capital measure are simplified, helping clients to make better decisions on how to allocate capital to different assets without losing view of regulatory constraint and economic consideration.

Additional Enhancements

  • Custom templates available for exposure results reports
  • Conditional Simulation inclusion of additional analysis types
  • DealAnalyzer™ calculation of mark-to-par spread output

For further details on these enhancements, please refer to the RiskFrontier™ 5.2 release notes.
Related Insights

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

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.

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CECL Treatment for the Investment Portfolio

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Investment Portfolios CECL Methodologies

In this fifth webinar in our series, our experts discussed common CECL considerations for structured credit and answered key questions on how to provide CECL estimates for structured credit.

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Quantitative Research Webinar Series: Recovery Correlation Dynamics

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.

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Expanding Sensitivity Analysis and Stress Testing for CECL

To ease the transition to CECL, firms can leverage and align existing risk management practices. Institutions are in the process of trying to determine which methodologies can be expanded to meet the CECL impairment model requirements, while retaining a consistency between other regulatory and risk management activities.

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Expanding Sensitivity Analysis and Stress Testing for CECL

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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.

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Leveraging Basel and Stress Testing Models for CECL

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Modeling IFRS 9 Impairments – Tactical Implementation Approaches

Learn how Moody’s Analytics is helping institutions of all sizes address the challenges of implementing the IFRS 9 impairment model.

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Managing Credit Portfolio Risk Under Basel III: Integrating Regulatory Capital with Economic Risks

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Implementing an IFRS 9 Solution: Challenges Faced by Financial Institutions

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

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Making Risk Appetite Stick: How Data and Analytics Can Help

In this webinar, we discuss how institutions can overcome challenges to ensure that risk appetite can be monitored as well as key analytic metrics which can be leveraged for strategic decision-making.

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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.

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GCorr™ Emerging Markets

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