General Information & Client Service
  • Americas: +1.212.553.1653
  • Asia: +852.3551.3077
  • China: +86.10.6319.6580
  • EMEA: +44.20.7772.5454
  • Japan: +81.3.5408.4100
Media Relations
  • New York: +1.212.553.0376
  • London: +44.20.7772.5456
  • Hong Kong: +852.3758.1350
  • Tokyo: +813.5408.4110
  • Sydney: +61.2.9270.8141
  • Mexico City: +001.888.779.5833
  • Buenos Aires: +0800.666.3506
  • São Paulo: +0800.891.2518

Moody's Analytics Insights

Financial data on monitor

Weekly Market Outlook: Debt-to-Profits Outperforms Debt-to-GDP

In 2017's final quarter, the 7.7% yearly advance by nonfinancial-corporate profits from current production outran the accompanying 6.6% increase of nonfinancial-corporate debt. The record shows that if pretax operating profits continue to outpace corporate debt, corporate credit quality will improve. The correlation between the high-yield default rate's quarter-long average and the yearlong ratio of debt-tooperating profits for US nonfinancial corporations is a meaningful 0.82.

March 2018
John Lonski, Franklin Kim, Yukyung Choi,  Ryan Sweet, Kathryn Asher, Michael Ferlez, Thomas Nichols, Barbara Teixeira Araujo, Katrina Ell

Tail Risk contribution

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
Jimmy Huang, Libor Pospisil

Figure 1: Forthcoming Acceleration by US Government Debt May Be Offset By Below-Trend
Growth of Non-Federal Debt

Weekly Market Outlook: Borrowing Restraint Elsewhere Makes Room for Federal Debt Surge

Partly as a means of offsetting the loss of business activity to deleveraging by households, businesses, as well as state and local governments, the federal government's share of the U.S.' broadest estimate of public and private nonfinancial-sector debt has soared from year-end 2007's 18% to the 34% of 2017's third quarter. The latter share is the highest since 1960's third quarter.

February 2018
John Lonski, Njundu Sanneh, Franklin Kim, Yukyung Choi,  Ryan Sweet, Barbara Teixeira Araujo, Reka Sulyok, Katrina Ell, Faraz Syed

Double exposure of city and graph on rows of coins for finance and banking concept

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
Scott Hoyt,  Brian Poi, Mark Zandi

Business and financial report

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

New Origination Indices for All Loans

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
Dr. Pierre Xu, Tomer Yahalom, May Jeng

Abstract interior architecture design

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
Jimmy Huang

Figure 15 Market shocks of selected macroeconomic variables

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

Figure 10 Factor Correlations by Region Under GCorr Emerging Markets

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
Jimmy Huang, Libor Pospisil, Noelle Hong

Figure 9 EDF Value Change Over Time

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
Andrew Kaplin,  Dr. Amnon Levy, Qiang Meng, Libor Pospisil

Putting the CCAR 2013 Severely Adverse Scenario in Perspective

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

Figure 1 US corporate default and credit card delinquency rates

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