The risk function has emerged as an important strategic decision-making partner for critical functions such as business development, finance, operations, and technology.
Roshni Patel, Mehna Raissi, Jin Oh
Listen as Anamaria Pieschacon and Michael Brisson a discuss effective approaches for validating consumer credit risk models.
Moody's Analytics provides financial intelligence and analytical tools supported by risk expertise, expansive information resources, and the application of new technology. Its solutions, made up of research, data, software and professional services, are assembled with the aim of delivering a seamless customer experience.
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
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.