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.
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.
In this paper, we have considered the use of proxy models as a way of overcoming some of the operational and computational challenges associated with measuring future solvency under different market conditions and ALM assumptions.
Listen to Domitille de Coincy and Dimitri Kaltsas of Moody's Analytics as they discuss the IFRS 9 methodology for structured finance, the SPPI test for structured finance securities, including criteria, interpretations, and credit risk comparisons, and staging and impairment calculations for structured finance securities.
Domitille de Coincy, Dimitri Kaltsas
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.
Noelle Hong, Jimmy Huang, Libor Pospisil, Albert Lee, Marc Mitrovic, Sunny Kanugo, Tiago Pinheiro, Andriy Protsyk, Dr. Yashan Wang
We propose a composite capital allocation measure integrating regulatory and economic capital. The approach builds upon the economic framework underpinning traditional RORAC-style business decision rules, allowing for an optimized risk-return tradeoff while adhering to regulatory capital constraints. The measure has a number of depictions, and it can be viewed as a weighted sum of economic and regulatory capital, as economic capital adjusted for a regulatory capital charge, or as regulatory capital adjusted for concentration risk and diversification benefits. Intuitively, when represented as economic capital adjusted for a regulatory capital charge, the adjustment can be represented as the additional top-of-the-house regulatory capital, above economic capital, allocated by each instrument's required regulatory capital. We show that the measure has ideal properties for an integrated capital measure. When regulatory capital is binding, composite capital aggregates to the institution's top-of-the-house target capitalization rate. We find the measure is higher than economic capital, but lower than regulatory capital for instruments with high credit quality, reflecting the high regulatory capital charge for this instrument class. Finally, we address how IFRS 9/CECL impacts the CCM and discuss the broader implications of the new accounting standards.
This paper develops a method to back-allocate to individual positions the market risk capital requirement that a bank must satisfy under the revised standardized approach proposed by the Basel Committee. Our method assesses the contribution of single positions or sub-portfolios to the overall capital charge. One important feature of our method is that it provides insight on which positions, sub-portfolios, and risk factors drive the capital charge and which help mitigate it. A negative contribution indicates that a marginal increase in the position would lead to a decrease in the capital charge, and vice versa.
Lorenzo Boldrini, Tiago Pinheiro
In this paper, we show a practical application to forecasting capital requirements for real portfolios of participating whole life and annuity business, carried out in a joint research project between Moody's Analytics and New York Life Insurance Company.
Financial institutions are seeking ways to gain a better understanding of their credit portfolios' risk dynamics, allowing them to foresee and to prepare for potential increases in capital requirements resulting from economic shocks.
Aubrey Clayton, Xuan Liang
This paper presents a novel modeling approach that allows for better management of the interplay between supply and demand dynamics for regulatory capital, combining an economic framework with regulatory capital and new loss recognition rules. The framework is particularly relevant in understanding the extent to which IFRS 9 can lead to more aggressive provisioning, which feeds into earnings volatility. Our approach provides guidance on how organizations can better manage their capital buffer, considering investment concentration, its impact on earnings volatility, and the relationship with regulatory capital requirements. Imperative to portfolio management, the framework recognizes the likelihood of a capital shortfall being significantly impacted by portfolio asset class, geography, industry, and name concentration, as extreme fluctuations in capital supply and demand occur more often for institutions holding more concentrated portfolios. Finally, we discuss integrated investment and strategic decision measures that account for the full spectrum of economic risks and interactions with regulatory and accounting rules, as well as instruments' contribution to earnings volatility and capital surplus dynamics.
Banks commonly use Risk Contribution, or contribution to portfolio Unexpected Loss (i.e., standard deviation), as a risk allocation method. While the method has some very desirable properties, it can also produce seemingly counterintuitive dynamics, whereby high interest income-producing assets are associated with higher risk, all else being equal. This dynamic manifests from the higher interest income assets possessing higher value, leading to higher standard deviation in absolute terms. In reality, financial institutions often use interest income to offset losses, and thus, associate higher interest with lower risk. This paper introduces a new, income-adjusted form of Risk Contribution-based capital allocation, designed so that interest income offsets losses. The measure demonstrates improved properties for exposures with particularly high coupons.
Amnon Levy, Managing Director of Portfolio Research at Moody's Analytics, discusses a novel modeling approach that allows organizations to better manage the supply and demand dynamics for regulatory capital. The approach marries an economic capital (EC) framework with (RegC) and loss accounting rules.