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Moody's Analytics Insights

Webinar-on-Demand

Meeting the Challenges of SCCL

The Federal Reserve finalized, in June 2018, the rule to prevent concentrations of risk between large banking organizations and their counterparties from undermining financial stability, known as Single Counterparty Credit Limit (SCCL).

November 2018
Olivier Brucker, Anders Rodenberg, Moun Seo

Webinar-on-Demand
Block of arrows pointing right and one pointing left.

CECL Disclosures – Required and Beyond

Our experts, Masha Muzyka and Jin Oh, cover transition disclosures focus areas, potential implication of the methodology chosen to the expected disclosures and ECL disclosure best practices emerging to date.

July 2018
Masha Muzyka, Jin Oh

Whitepaper
Businessmen and woman standing together by railing conversing

Insight, IFRS 17, and Innovative Technologies - Drivers of Change in the Insurance Industry

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.

July 2018

Presentation
Financial and business chart and graphs

Simple But Not Simpler: Day 1 Modeling Approaches

Simple But Not Simpler: Day 1 Modeling Approaches. This presentation is a review of simple approaches available to community banks on the road to their CECL journey.

July 2018

Webinar-on-Demand
Chess pawns in a circle

CECL: The "Easiest" Implementation

Our subject matter experts, Robby Holditch, Director, and Jin Oh, Director, discuss critical steps in meeting the new CECL standard.

June 2018

Presentation
Idea concept with row of light bulbs

Be Reasonable: Creating Supportable Forecast Scenarios for CECL

This presentation discusses the CECL requirement of reasonable and supportable forecasts. We discuss what makes an economic scenario reasonable and supportable and discusses structural forecast model methodology. We also compare customized, standard and off-the-shelf scenarios and examine forecasting credit losses.

June 2018

Whitepaper
Figure 7 CCM breakdown.

A Composite Capital Measure Unifying Business Decision Rules in the Face of Regulatory Requirements Under New Accounting Standards

Prudent credit risk management ensures institutions maintain sufficient capital and limit the possibility of a capital breach. With CECL and IFRS 9, the resulting trend toward greater credit earnings volatility raises uncertainty in capital supply, ultimately causing an increase in required capital. It is ever more challenging for institutions to manage their top-of-the house capital while steering their business to achieve the desired performance level. This paper introduces an approach that quantifies the additional capital buffer an institution requires, beyond the required regulatory minimum, to limit the likelihood of a capital breach. In addition, we introduce a new measure that allocates capital and recognizes an instrument's regulatory capital requirements, loss allowance, economic concentration risks, and the instrument's contribution to the uncertainty in capital supply and demand. In-line with the Composite Capital Measure introduced in Levy and Xu (2017), this extended measure includes far-reaching implications for business decisions. Using a series of case studies, we demonstrate the limitations of alternative measures and how institutions can optimize performance by allocating capital and making business decisions according to the new measure.

May 2018
Dr. Amnon Levy, Xuan Liang, Dr. Pierre Xu

Whitepaper
Average projected LGD from LossCalc 4.0, by industry, North America Firms

Measuring and Managing the Impact of IFRS 9 and CECL Requirements on Dynamics in Allowance, Earnings, and Bank Capital

Reserving for loan loss is one of the most important accounting aspects for banks. Its objective is to cover estimated losses on impaired financial instruments due to defaults and non-payment. Reserve measurement affects both the balance sheet and income statement. It impacts earnings, capital, dividends and bonuses, and attracts the attention of bank stakeholders ranging from the board of directors and regulators to equity investors. In response to the so-called “too-little, too-late” problem experienced with loan loss reserve during the Great Financial Crisis, accounting standard setters now require that banks provision against loan loss based on expected credit losses (ECL). Arguably, calculating the Expected Credit Loss Model under IFRS 9 and CECL presents a momentous accounting change for banks, with the new standards coming into effect sometime between 2018 and 2021, depending on the jurisdiction.

March 2018

Whitepaper
charts on transparent display

Economic Capital Model Validation: A Comparative Study

Using a long history of public firm defaults from Moody's Investor Services and Moody's Analytics, this study illustrates a validation approach for jointly testing the impact of PD and correlation upon model performance. We construct predicted default distributions using a variety of PD and correlation inputs and examine how the predicted distribution compares with the realized distribution. The comparison is done by looking at the percentile of realized defaults with respect to the predicted default distribution. We compare the performance of two typical portfolio parameterizations: (1) a through-the-cycle style parameterization using agency ratings-based long-term average default rates and Basel II correlations; and (2) a point-in-time style parameterization using public EDF credit measure, and Moody's Analytics Global Correlation Model (GCorr™). Results demonstrate that a through-the-cycle style parameterization results in a less conservative view of economic capital and substantial serial correlation in capital estimates. Results also show that when point-in-time measures are used, the tested economic capital model produces consistent and conservative economic capital estimates over time. A version of this paper appears in the Journal of Risk Model Validation, March 2013.

February 2018
Zhenya Hu,  Dr. Amnon LevyDr. Jing Zhang