Accounting Standards Update 2016-13, also known as the Current Expected Credit Loss (CECL) standard, was issued as the Financial Accounting Standards Board's (FASB’s) answer to the 2007−2009 financial crisis.
Its objective is early recognition of expected credit losses, allowing banks to proactively react to actual and expected future changes in the credit environment. CECL is one of the few accounting standards that has caused tremendous controversy and speculation regarding its impact on allowance and earnings, and the potential unintended consequences on lending and credit markets. This issue became increasingly relevant amid the COVID-19 pandemic, as credit markets saw expected credit losses mount.
CECL is designed to be more responsive to changes in the credit environment. In addition, adopting CECL should result in a clear alignment between credit risk management and accounting for credit losses. Most notably, CECL incorporates forecasts into the loss estimate and requires measurement on a collective or pool basis when there are similar risk characteristics. The allowance can exhibit material sensitivity to changes in the credit environment, leading to credit losses, especially when a pool contains substantial concentration in product, industry, geography, or borrower risk concentration. These dynamics are not as easily observed or anticipated when assessing an individual borrower or transaction, as was the case under incurred loss. In this paper, we explore what concentration risk is, the consequences of CECL on concentration risk in banking, and an approach to quantify and allocate that risk.
Additionally, COVID-19 had a profound impact on economic activity. This event represented the first materially deteriorated credit environment since CECL adoption. After the first credit defaults occurred, many banks and other financial institutions experienced pronounced increases in allowance — as forward-looking models that feed into CECL reacted to the expected credit environment deterioration. Nevertheless, the rapid economic decline resulting from the pandemic was astonishing. It became evident that, even under the incurred loss approach, allowances increased almost as soon as with CECL, as the probability threshold for the credit loss event had already been met in Q1 2020. The unprecedented circumstances prompted the Federal Reserve and other agencies and regulatory bodies to reinforce the system. This provided organizations with flexibility in implementation, allowed for delayed adoption, and permitted increased levels of judgment in assessing how the environment influenced the allowance. Despite these measures, the deterioration in the credit environment ultimately resulted in a reduction in earnings and capital.
Background
What is concentration risk? Concentration risk refers to the potential hazards faced by a portfolio or business when there is an excessive focus on a specific asset, sector, borrower, or geographical location, encapsulating the essence of concentration risk meaning. Understanding the concentration risk definition is crucial for navigating the potential adverse impacts stemming from an overly concentrated allocation within a portfolio.
To illustrate concentration risk manifestation, consider two otherwise identical segments: same product type, maturities, industry, loan-to-value (LTV), debt service coverage ratio (DSCR), risk ratings, and geography. Assume Pool 1 has 100 loans, each with a $10,000 amortized cost, while Pool 2 has 97 loans with a $10,000 amortized cost and 3 loans with a $100,000 amortized cost. If the credit loss model driven by industry, LTV, and DSCR produce the same expected default rate of 6%, and LGD of 100%, the expected credit loss rate will be 6%. When calculating the allowance, we need to multiply the loss rate by the pool’s amortized cost. For the first pool, we multiply $1 million by 6% and arrive at $60,000 of allowance. For the second, we multiply $1.27 million by 6% to arrive at $76,200. Now assume that three loans default and are subsequently charged off. Any three loans charged off in Pool 1 will result in a $30,000 loss and will be fully covered by the reserved allowance. However, if even one loan out of the three largest loans in Pool 2 defaults, the reserve will not be sufficient.
The simplistic example above demonstrates that balance (or borrower name) and concentration need to be considered when calculating the allowance. However, several factors and probable events can affect this:
- What if all three of the largest loans in Pool 2 mature within the next month?
- What if they are fully collateralized?
- What if the entire portfolio is in oil and located off the Gulf Coast and climate change becomes more pronounced, which may significantly raise the production costs, while the availability of renewable energy reduces the oil demand?
- What if these pools are concentrated in passenger airlines and cruise lines?
When a single (or small number of) probable event(s) or factors can drive material losses, concentration should be explicitly incorporated into the credit loss calculation.
The degree to which allowance fluctuates is directly related to the degree to which a portfolio is concentrated or diversified. A highly concentrated portfolio is more likely to exhibit concentrated losses when compared with a well-diversified portfolio where segments are exposed to relatively uncorrelated factors. Institutions should recognize that variation in concentration over time often causes estimated credit losses associated with the institution’s existing portfolio to differ from historical loss experience. Given that loss allowance reflects anticipated credit losses within a bank’s portfolio, and that loss is uncertain, segments with higher risk concentration are more likely to experience a high-loss event and require a concentration-loss adjustment. For example, the risk concentration of an all-oil portfolio will look very different from one diversified across oil, agriculture, auto, airlines, and banking, as the price of oil fluctuates over time and the credit quality of the oil segment changes accordingly. This became evident through the observed cross-sectional variation in reactions to COVID-19 across industries.
This event marked the first materially deteriorated credit environment since the adoption of CECL. Although there were indications of a pullback, the impact was partially offset by Federal Reserve action and Federal and State stimulus; however, the speed of banks' reactions remained uncertain. Notably, CECL was a principles-based standard, similar to incurred loss, and the translation of economic forecasts into credit loss allowance under CECL varied among firms. As organizations managed portfolios judged by market metrics—allowance, earnings, and volatility—it was likely that banks avoided or more aggressively priced deteriorated segments compared to previous downturns, contributing to a more stable financial system. For those withstanding the short-term impacts, the widening spreads hinted at the medium- to long-term challenge of mounting credit losses. The occurrence of the first credit defaults underscored unanticipated downstream impacts, such as weak credits affected by supply chain disruptions from the coronavirus1. This raised questions about the implications for credit portfolios and the ability of banks to navigate through tumultuous times. Drawing on the experience of the Great Recession provided a partial understanding of what to expect from first-quarter 2020 earnings, reported under CECL by some SEC filers2 in mid-April 2020. While the Great Recession served as an important benchmark for actual losses, it did not capture the balance sheet's reactions to changing loss expectations. During that time, banks reported allowance under the less reactive incurred model, inherently backward-looking. CECL adoption aimed to enable expected changes in the credit environment to immediately impact reserves, enhancing the "cushion" for future credit losses. If the CECL model worked as intended, many banks and financial institutions experienced significant increases in allowance, as forward-looking models feeding into CECL reacted to the expected deterioration in the credit environment. This change ultimately led to a reduction in earnings before the manifestation of credit events, which might have seemed startling at first glance.
With this setting as the backdrop, we articulate concentration risk in banking as the impact of common risk factors that can result in substantial losses to a segment of a credit portfolio. Concentration risk can be generated from common factors: for instance, the degree to which an oil and gas portfolio has exposure to oil prices. Alternatively, concentration risk can manifest through concentrated exposure to a corporate counterparty such as, say, JP Morgan Chase, or a municipality such as the City of Detroit.
We organize the remainder of this paper as follows:
- Section 2 explores the manifestation of concentration risks under the CECL model in comparison to the incurred loss model.
- Section 3 describes an approach to quantify concentration risk in the allowance process.
- Section 4 walks through a series of case studies that demonstrate the dynamics of the concentration measure when applied to various portfolios.
- Section 5 explores implications for expected credit loss disclosures and credit portfolio management.
- Section 6 concludes.
The manifestation of concentration risks under CECL in comparison to incurred loss
A material difference between allowance under incurred loss, which is inherently a historical measure, and CECL, which is intended to be forward-looking (FL), is the reactive nature of allowance to the expected changes in the credit environment. In the context of this discussion, this trait is relevant to the extent to which concentration risk manifests.
To get a sense of the difference between historical measures, frequently leveraging measures associated with Through-the-Cycle (TTC), and FL measures, Figure 1 compares Through-the-Cycle and Point-in-Time measures across time, mapping out the average one-year default probability for the overlapping sample of U.S. Moody’s-rated firms and firms with Moody’s Analytics EDF™ (Expected Default Frequency) credit measure. While there is a range of approaches to measuring a TTC credit measure, Moody’s rating coupled with the idealized default rate is one common approach. Similarly, the EDF measure is one commonly used FL measure. The dramatic increase in the average one-year FL probability of default (PD) in the Tech-Telecom downturn and the Great Recession is not nearly as pronounced under the TTC measure. Figure 1 plots the average annualized PDs across all U.S. publicly listed firms with Moody’s rating. The forward-looking PD (blue line) for each firm represents the EDF measure calculated by Moody’s Analytics CreditEdge™ model. The TTC PD (blue line) for each firm is calculated based on the Moody’s rating for the firm. The rating is converted to TTC PD according to the historical average EDF measure and rating mapping. When compared with actual corporate default rates, depicted in Figure 2, it is clear that the FL measures indeed capture the credit environment. Consequently, allowance calculation based on historical TTC measures unadjusted for the current and future expected economic environment would not be as reactive and dynamic compared to the FL-based calculations.
We next turn to the question of the manifestation of concentration risk in banking across segments under FL credit measures. Figure 3 depicts the average FL measures segmented across financial institutions and non-financial institutions. The figure highlights that concentration risks manifest more observably under FL measures when, for example, Tech and Telecom companies deteriorated in credit quality during the early 2000s. It is worth observing that if we use these FL measures, the portfolio would have steered aggressively away from non-financial institutions during the Tech-Telecom downturn, and away from Financials at the onslaught of the Great Recession; clearly not in the same way that a TTC measure would have the organization steer. The next section explores these concepts more formally. Figure 3 plots the average annualized PDs across all U.S. Financial and Non-financial publicly listed firms with a Moody’s rating. PDs are calculated using Moody’s Analytics CreditEdge model.
What’s more, acknowledging the wide cross-sectional variation across credit market segments, banks and other credit market participants can experience wildly different earnings impacts due to evolving market conditions. Banks must incorporate their forward-looking indicators into CECL models, and have an incentive to increase required rates on assets that attract a higher level of allowance under the CECL lifetime expected credit loss models. Occasionally, this might lead to the withdrawal from deteriorating lending segments entirely.
The bottom line: CECL measures result in allowance being more reactive to the credit environment — by design — and concentration risks will be more pronounced under CECL. This said, by their forward-looking nature, the measures lend themselves to more robust credit portfolio management.
The next section explores a framework that quantifies concentration dynamics that can be applied in the allowance process.
An approach to quantify concentration risk during the allowance process
Traditionally, concentration risk is often determined heuristically, using managerial overlays added to existing metrics. Although this approach is usually intuitive, it involves a degree of hand-waving that may not be entirely satisfactory. In contrast, we suggest a quantitative approach that serves as a starting point for assessing concentration risk.
Our approach measures concentration as a contribution to portfolio losses, recognizing that pockets of concentration are more likely to contribute to material losses. To illustrate the approach, we define credit earnings as interest income net of changes in loss allowance due to credit migration and resulting changes in expected credit losses and default loss. To quantify concentration risk, we model possible combinations of scenarios and possible gains and losses, recognizing concentration, correlation, terms and conditions, and so forth. Referencing the discussion above, we account for the likelihood that oil prices and other correlated factors might drive material losses in the portfolio.
More generally, we must predict not only the average earnings but the entire earnings’ distribution, as depicted in Figure 6, which maps out possible realizations over a horizon of say, one year. Over the year, a good credit scenario might result in 10% earnings, while a baseline or downturn scenario might be 5% or -4%. In general, the more concentrated the portfolio, the more extreme the difference between positive and downturn scenarios. In fact, this difference should be close to zero for a fully diversified portfolio with no common risk factors.
Figure 6 provides a stylized example of the distribution of credit earnings of a portfolio of amortized cost term loans. The process of allocating concentration add-on for each segment involves estimating each segment’s average loss when the portfolio suffers an event. This measure is called the segment tail-risk contribution. We set the allowance of the segment to the segment tail-risk contribution.
There are several aspects to modeling loss allowance concentration add-on worth discussing. The model must differentiate across portfolio segments, such as Tech/Telecom, Energy, and Finance, to name a few prominent sectors that have suffered in various ways over the years. For retail and commercial real estate (CRE), metropolitan statistical analysis (MSA), and product/property type, differentiation is imperative. It is also important to account for name concentration for commercial and industrial (C&I), municipal, and other asset classes typically held at concentrated levels. A slew of other factors, such as terms and conditions and maturity (in particular for CECL), are particularly relevant for this exercise, articulated in Figure 7.
Finally, the extreme nature of the event, such as 1-in-10 or -20 years, must be considered. This is organization-specific, related to qualitative judgment, and can be tied to an institution’s risk appetite; the more averse the institution is to risk, the lower the event probability, resulting in a higher concentration add-on. This situation may also depend upon the credit environment.
Next, we explore framework dynamics using a series of case studies.
Dynamics in concentration: case studies
We begin with a sample of U.S. corporate loan portfolios and explore our concentration measure, based on tail-risk contribution. In Figure 8, we compare the percentage of portfolio holding in each sector with the percentage of portfolio tail-loss attributed to each sector. While larger exposures contribute more to tail loss, the contribution is not proportional to holding, due to the difference in other factors that impact concentration risk, such as PD and maturity. For instance, resulting in large portfolio tail losses attributed to these sectors.
Figure 8 plots the holding amount of each sector as the percentage of total portfolio holding and the tail-risk contribution of each sector as a percentage of total portfolio tail-loss. It shows only the 10 sectors with the highest amount of holdings in the portfolio.
Figure 9 presents segment loss allowance concentration add-on, according to segment tail-risk contribution as a percentage of segment holding. The green bars represent the loss allowance calculated under CECL, which can be thought of as roughly lifetime PD times the loss-given default (LGD) associated with each segment. The blue bars show the concentration add-ons.
We see that a few sectors have significantly higher concentration adjustments than others. The Banking sector is one concentration risk example. It turns out that the borrowers in this sector are highly correlated with the systematic risk factors that drive portfolio loss and thus introduce relatively more concentration risk. In contrast, the borrowers in the Food and Utilities sectors have a low correlation with portfolio systematic credit risk factors and thus add little concentration risk to the portfolio. Another example is the Hotel sector. In this particular portfolio, the Hotel sector’s exposures are concentrated in a relatively small number of borrowers compared to other sectors. Consequently, the concentration add-on for this sector is also relatively high, even though the basic loss allowance level for the sector is already the highest across all sectors as a consequence of COVID-19. Figure 9 shows only the 10 sectors with the highest amount of holdings in the portfolio.
Implications for credit loss disclosures and for credit portfolio management
If an institution had perfect foresight at origination, there would be no uncertainty with credit loss expectations. In reality, economic forecasts are never perfect, and available usable data to support future expected credit loss calculations is often far from granular enough to provide transparency into portfolio concentration risk. Concentration risk allowance adjustments may either not be statistically calculated or overlooked. As institutions align their accounting with credit risk management (and ultimately adjusting their allowance levels to achieve accurate and timely coverage for credit losses), investors and readers of financial statements seek transparency and understanding of original lifetime credit loss estimate and how it plays out over time. More specifically, they want to understand significant drivers of changes in the lifetime estimate compared to Day 1. Dependent on the portfolio segment and economic cycle, concentration may cause material changes and require disclosure considerations to help tell the story.
Entities may want to consider supplementing their required amortized cost vintage disclosure with information about concentration risk and factors that could influence this risk to become more pronounced. This information, together with statistical measurements, boosts investors’ confidence despite expected volatility in CECL allowance.
Conclusions and market trends
Institutions and the market must navigate CECL and explore the implications of allowance dynamics and necessary disclosures. The forward-looking measures will have concentration risks that manifest in a more pronounced way — by design — and these measures have a wide range of applications related to credit portfolio management. This paper provides a natural quantitative approach for incorporating concentration in the allowance process.
Recent years marked by economic and market volatility have made banks increasingly attuned to dynamics under CECL, looking for more granular, more robust, forward-looking measures to manage their credit portfolios (e.g., Saporta, 2019).
As a final note, it is worth observing that the concepts and approach introduced in this paper apply to IFRS 9 as well, and are relevant in the context of some trending European regulations. The ECB Guide to the Internal Capital Adequacy Assessment Process (ICAAP), for example, requires institutions to consider forward-looking capital adequacy assessments.3
Ultimately, CECL measures can be used to inform and improve lending standards. For organizations that actively monitor cross-sectional dynamics and manage credit portfolio diversification, these measures can facilitate steering portfolios to minimize volatility in expected credit losses. Therefore, CECL adoption has the potential of mitigating a credit crisis, such as seen during the Great Recession, partially caused by continued investment in segments whose credit has deteriorated.
Footnotes:
1 Valeritas Holdings, a medical company, filed for Chapter 11 on February 10, 2020 citing supply chain disruptions linked to the coronavirus (COVID-19).
2 CECL is effective for the public business entities that are SEC filers and not Small Reporting Companies for fiscal years beginning after December 15, 2019. Furthermore, SEC issued an order on March 25, 2020, under which public companies unable to meet filing deadlines due to COVID-19-related circumstances will have an additional 45 days to submit certain disclosure reports (e.g., Forms 10-K, 10-Q, 20-F) that would otherwise have been due between March 1 and July 1, 2020. As a result, the relief can be applied to March 31, 2020 Forms 10-K and 10-Q. https://www.sec.gov/rules/exorders/2020/34-88465.pdf
3 For further discussion, see, Levy and Xu (2019).
References:
Levy, Amnon and Pierre Xu, “The Role of Banks in Illiquid Credit Markets, and the Disruption and Evolution of Credit Portfolio Management.” Risk Books: Credit Risk Measurement and Management: Disruption and Evolution, 2019.
Saporta, Victoria, “Written Auditor Reporting: Update and Main Thematic Findings.” Letter to Chief Financial Officers of Selected Deposit Takers, Bank of England Prudential Regulation Authority, URL: https://bit.ly/2TdAYyx, 2019.
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