This paper estimates the amount of fiscal stress likely to be applied to state budgets under different recession scenarios and comparing that stress to the amount of money states have set aside in reserve. This year's exercise also expands the scope of stress-testing by including a look at how economic stress translates to public pensions.
Performance optimization through business insight, dealing with IFRS 17 in a post-Solvency II world, and the challenges associated with stress testing for insurance firms in the US. These were the focus areas for Moody's Analytics at this year's Moody's Insurance Summits in London and New York.
In this webinar, Mark Zandi and the Moody's Analytics team discuss recent changes to our Global Macroeconomic Model, and provide an overview of Scenario Studio, our new platform for custom scenario development. Learn more: www.moodysanalytics.com/scenariostudio
Mark Zandi, Mark Hopkins
Asia Deep Downturn Scenario Narrative
In this article, we propose an innovative algorithm that is well suited to building dynamic models for credit and market risk metrics, consistent with regulatory requirements around stress testing, forecasting, and IFRS 9.
This article proposes a method of modeling realized losses given default (LGDs) as a function of macroeconomic drivers for stress testing purposes.
This article discusses areas such as capital stress testing where simplification of regulations could improve the flow of credit while protecting the financial system.
For the 2017 CCAR program, the Federal Reserve published three macroeconomic and financial scenarios to be used in stress testing 34 CCAR financial institutions. In this study, we analyze 27 institutions, with a total of more than $760 billion in exposures to commercial real estate loans, using Moody's CMM Stress Testing framework. This report describes how we derive credit loss estimates for the CRE loan portfolios held by CCAR firms. This is our first study leveraging the loan-level commercial banks' data collected via Moody's Analytics CRE Credit Research Database (CRD™). Our analysis estimates that the expected nine-quarter, cumulative CRE portfolio loss through the first quarter of 2019 is 6.5% under the CCAR 2017 Severely Adverse Scenario. The primary factors behind the higher loss estimate compared to last year's stressed scenario (5.1% loss) is that this year's scenario features a slightly more severe economic downturn and a significantly larger decline in commercial real estate prices.
Higher capital standards imposed by new stress testing requirements have forced organizations to address how to better manage capital to meet regulatory constraints. While maintaining higher capital levels is indeed mandatory, simply satisfying the requirement does not necessarily align with stakeholders' preferences for optimal capital deployment and investment decisions. CCAR-style stress tests are requirements that organizations must adhere to; however, these exercises likely do not reflect how stakeholders actually trade off risk and return.
With auto leasing close to record highs, the need for accurate and transparent used-car price forecasts is paramount. Concerns about the effect of off-lease volume on prices have recently peaked, and those exposed to risks associated with vehicle valuations are seeking new forms of intelligence. With these forces in mind, Moody's Analytics AutoCycle™ has been developed to address these evolving market dynamics.
This paper proposes and illustrates a multi-period capital planning framework that can be used to calculate a portfolio's capital requirement over time and to determine the appropriate capital buffer level under various economic scenarios. Such analysis can help financial institutions gain a better understanding of credit portfolios' risk dynamics, allowing them to foresee and to prepare for potential increases in capital requirements resulting from economic shocks.
Andrew Kaplin, Xuan Liang
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.
Noelle Hong, Jimmy Huang, Albert Lee, Dr. Amnon Levy, Marc Mitrovic, Libor Pospisil, Olcay Ozkanoglu