Staff StudiesFederal Deposit Insurance Corporation 

Staff Studies 

Construction and Evaluation of 

an NSFR Proxy Using Publicly 

Available Data 

www.fdic.gov/cfr • @FDICgov • #FDICCFR • #FDICResearch 

January 2022 

Construction and Evaluation of an NSFR Proxy Using Publicly Available Data 

Robert M. Miller, Daniel Nguyen, and Derek Thieme* 

Abstract 

We create a proxy for the Net Stable Funding Ratio (NSFR) using publicly available data from the FDIC’s Research Information 

System (RIS), in effect creating an offsite risk indicator that reflects both asset and funding liquidity. We construct the proxy 

for all banks for which we have data, including smaller banks that would not be subject to the NSFR, to allow for in-depth 

analysis of the NSFR as a risk metric. Banks that either failed before 2012 or participated in the FDIC’s Debt Guarantee 

Program (DGP) had lower proxy NSFRs on average than banks that did not, suggesting that the NSFR correlates with risk as 

intended. Our proxy NSFR allows us to examine the drivers for NSFR trends before the 2008 financial crisis and in various 

economic periods after the crisis, including during the COVID-19 pandemic. Our proxy shows that the share of domestic 

deposits in total funding for the banking industry increased immediately after the crisis, leading to a rise in aggregate net 

stable funding. In 2013, the net stable funding measures for larger banks started to diverge from those of smaller banks, 

as larger banks started managing their liquidity and funding stability in a manner distinct from that of other banks. This 

phenomenon is most pronounced for banks that are part of a banking organization with $250 billion or more in total 

consolidated assets, which are more likely to be subject to NSFR regulations. Finally, our proxy shows the effect on NSFR of 

the recent increases in deposits and cash balances during the pandemic. 

* Economists at the Federal Deposit Insurance Corporation (FDIC). The views expressed are those of the authors and do not necessarily reflect the official 

positions of the Federal Deposit Insurance Corporation or the United States. FDIC Staff Studies can be cited without additional permission. The authors thank 

George French, Senior Advisor for Economics and Regulatory Analysis, and Ryan Singer, Chief of the FDIC Regulatory Analysis Section, for their guidance 

and direction. The authors also thank Rosalind Bennett, Center for Financial Research Executive Program Director, and other reviewers at the FDIC for their 

editorial review and suggestions. 

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2 
I. Introduction 

During the financial crisis that began in 2008, a sharp drop in housing prices and losses on mortgages and securities 

backed by mortgages triggered a liquidity crisis. The liquidity crisis and the accompanying recession resulted in numerous 

bank failures.1 In response, the Basel Committee on Banking Supervision (BCBS) developed two regulatory standards 

designed to ensure that large and internationally active banks2 maintain adequate liquidity: the Liquidity Coverage Ratio 

(LCR) and the Net Stable Funding Ratio (NSFR).3 The LCR was designed to ensure that banks maintain enough high-quality 

liquid assets to withstand a liquidity stress event over a 30-day period. The NSFR was designed to address liquidity risk over a 

one-year time horizon by requiring banks to fund their activities with sufficiently stable sources of funding. 

In 2016, U.S. federal banking agencies4 published a notice of proposed rulemaking (NPR) that outlined and requested 

public comment on a proposed NSFR standard.5 In 2019, the agencies issued a final tailoring rule that established 

categories of large banking organizations6 and proposed gradations of NSFR requirements across the categories.7 Since 

NSFR regulations had not yet been finalized when the final tailoring rule was issued, the tailoring rule did not subject any 

institutions to an NSFR requirement but instead specified the NSFR requirements that would apply to institutions in the 

different categories once a final NSFR rulemaking became effective. In 2021, the agencies finalized an NSFR rulemaking 

that applies an NSFR standard to large banking organizations subject to Category I, II, III, and IV capital standards and their 

depository institution subsidiaries (final NSFR rule).8 

While the vast majority of depository institutions in the United States are not subject to any NSFR requirement, the NSFR 

is of independent interest as a liquidity and stable funding risk measure. Researchers have analyzed various versions of the 

NSFR and its efficacy as a risk measure for large banks. This paper builds on that literature in several ways. It introduces an 

NSFR proxy based on financial data reported by depository institutions on the FFIEC9 Consolidated Reports of Condition and 

Income (Call Reports). The NSFR proxy introduced in this paper is similar to Call Report-based NSFR proxies developed by 

other studies, including Hong et al. (2014) and DeYoung and Jang (2016). Compared with these earlier studies, the proxy used 

in this paper is based on more recent U.S. banking regulations as revised by the final NSFR rule and uses more recent data. 

It also expands on prior Call Report-based NSFR proxies to account for the maturity and encumbrance of assets and allow 

for comparisons of bank stable funding positions across periods of time when Call Report requirements changed. This paper 

uses the NSFR proxy to examine the relative liquidity profiles of failed banks and Debt Guarantee Program (DGP) participants 

from 2008 to 2012, to discuss how the liquidity risk profile of the banking industry changed after the 2008 financial crisis and 

to show how the stable funding profile of the banking industry evolved amid the pandemic. 

Our NSFR proxy is in effect an offsite stable funding risk measure that rolls up considerable asset and liability data from 

quarterly Call Reports. We construct the proxy for insured banks because of superior data availability throughout the period 

of analysis relative to holding companies, and we view our insured bank results to be of independent interest.10 We calculate 

and analyze the performance of this offsite risk measure for all banks for which we have data, not only banks subject to the 

1

 Federal Deposit Insurance Corporation (FDIC), 2017, Crisis and Response: An FDIC History, 2008–2013, Washington, DC: FDIC, p. xvi. 

2

 For purposes of constructing and analyzing the NSFR proxy, this paper uses the term “bank” to refer to an FDIC-insured depository institution. However, the 

paper also uses the term “bank” to refer to any financial institution that is regulated by a U.S. federal banking agency or international equivalent.

3

 See “Basel III: International Framework for Liquidity Risk Measurement, Standards and Monitoring,” December 2010, at https://www.bis.org/publ/bcbs188.pdf. 

4

 The Federal Reserve, the Office of the Comptroller of the Currency, and the Federal Deposit Insurance Corporation.

5

 See 81 Fed. Reg. at 35124. 

6

 This paper uses the term “banking organizations” to refer to bank holding companies, foreign banking organizations, intermediate holding companies, 

savings and loan holding companies, and depository institutions, along with affiliates and subsidiaries of these organizations.

7

 See 84 Fed. Reg. at 59230. 

8

 See 86 Fed. Reg. at 9120. 

9

 Federal Financial Institutions Examination Council. For more information about the FFIEC, including its purpose and its members, see https://www.ffiec.gov/. 

10 The final NSFR rule applies both at the consolidated holding company level and at the level of large insured bank subsidiaries. 

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NSFR, which provides a larger dataset for analysis and a useful benchmark ratio for discussing changes in risk profiles across 

different bank size groups. It is important to clarify, however, that most banks with total assets below $250 billion are not 

subject to the NSFR, and no plans exist to subject these banks to the NSFR. Neither the NSFR nor any proxy of it is used by the 

FDIC to assess the liquidity risk profile of small banks. 

Our proxy NSFR can be a useful data tool for future research related to banking, funding stability, and default risk. 

Publicly available Call Report data can be used to quickly calculate the proxy NSFR and its components, including proxies for 

available and required stable funding. In Appendix C of this study, we decompose our proxy NSFR. This decomposition can be 

used to estimate how changes in individual assets and liabilities contribute to overall changes in funding stability. 

While we calculate proxy NSFRs for every bank in our sample, we present our findings using an aggregated weighted 

average of the proxy NSFRs for banks within each of three author-defined categories: banks part of a banking organization 

with less than $10 billion in total assets as of fourth quarter 2020 (small banks), banks part of a banking organization with $10 

billion to $250 billion in total assets as of fourth quarter 2020 (midsize banks), and banks part of a banking organization with 

more than $250 billion in total assets as of fourth quarter 2020 (large banks).11 

II. Prior Research 

This paper builds upon prior academic research on the utility of the NSFR as a risk metric. 

King (2013) used Bankscope data to estimate the NSFR, based on the BCBS (2010) definition, of a representative bank 

in 15 countries. He found that for banks with estimated NSFRs below 1.0, the strategies required to increase the NSFR to 1.0 

(that is, holding more liquid assets, using more stable funding sources, or both) would reduce net interest margins 70 to 88 

basis points on average. 

Hong et al. (2014) constructed approximate measures of the LCR and the NSFR using Call Report data based on both 

the BCBS (2010) and BCBS (2014) definitions and found a negative relationship between the NSFR and bank failure and an 

insignificant relationship between the LCR and bank failure. Importantly, they find that idiosyncratic liquidity risk measures 

such as the NSFR and LCR were less effective than systemic risk measures in predicting bank failures in 2009 and 2010. 

Bologna (2015) demonstrated empirically that the loan-to-deposit (LTD) ratio is a useful indicator of the probability of 

bank failure, and reasoned that the NSFR is an improvement over the LTD because the NSFR differentiates between stable 

and less-stable deposits. 

Kang et al. (2017) examined data on South Korean banks and found that the NSFR performs better than the LTD ratio in 

ensuring the stability of bank funding because the NSFR recognizes that liquidity depends on the maturity and type of assets 

and liabilities. 

Wei et al. (2017) found that NSFR regulations overcome the principal-agent problem between bank managers and 

stakeholders, leading to a socially efficient level of debt financing. Further, the authors found that the NSFR also reduces bank 

use of short-term funding and increases both ex ante profits and ex ante probabilities of survival for banks. 

In a study on banks in Luxembourg, Giordana and Schumacher (2017) found that a higher NSFR decreases the average 

probability of default. The authors found that the effect of the NSFR on bank profits depends more on a bank’s funding 

structure than on its asset composition. 

11 To determine the category of each bank, we calculate the four-quarter average of total assets, corrected for mergers, of the bank’s global top tier parent 

institution, as reported by the National Information Center (https://www.ffiec.gov/NPW). If a bank ceased to exist before fourth quarter 2020, we use the total 

assets of the global top tier parent institution for that bank’s final four quarters.

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DeYoung and Jang (2016) constructed an approximate NSFR based on the BCBS (2014) definition for U.S. commercial 

banks using Call Report data. The authors found that, on average, banks actively managed their liquidity from 1992 through 

2012 consistent with a traditional loan-to-core deposits ratio and the NSFR, which had not yet been introduced. They found 

that systemically important banks often managed their liquidity to lower standards than other banks and that these banks 

operated with liquidity levels lower than those recommended by the Basel III standards. 

Hoerova et al. (2018) constructed approximate measures of the NSFR and LCR using data from the European Central 

Bank’s (ECB) Individual Balance Sheet Items database. They found that full compliance with these measures as defined by 

BCBS (2014) would have reduced bank reliance on liquidity assistance during the global financial crisis of 2008 to 2009 and 

during the European sovereign debt crisis of 2011 to 2012, but would not have altogether eliminated the need for emergency 

liquidity assistance. They also found that liquidity regulations like the NSFR and LCR have modest effects on macroeconomic 

output relative to capital regulations. 

Our paper builds upon the Call Report proxy framework introduced by Hong et al. (2014) and DeYoung and Jang (2016) 

in several ways. First, we consistently track Call Report line items across time and report form types using the FDIC’s Research 

Information System (RIS) data warehouse, which includes Call Report data and derived variables. Prior NSFR proxies based 

on Call Report data use Call Report line items defined at a point a time, which may not appear in all periods or form types.12 

RIS also accounts for changes in regulations, such as an increase in the deposit insurance limit.13 Second, our methodology 

estimates the NSFR based on the updated definitions in the final NSFR rule in the United States rather than the NSFR as 

defined in BCBS (2010) and BCBS (2014). Finally, our methodology uses maturity and encumbrance data available on the Call 

Report to apportion balance sheet items to NSFR-relevant periods of maturity and encumbrance.14 These apportionments 

allow our proxy measure to capture an important dimension of stable funding risk and provide a vehicle to map changes in a 

bank’s mean encumbrance (or maturity) to changes in its NSFR, as measured by the proxy. 

Using our updated proxy for the NSFR, we extend the exercise conducted by Giordana and Schumacher (2017) that 

shows the relationship between NSFR and bank stress events. And we describe how changes in our NSFR proxy since the 

2008 financial crisis appear to reflect the changing stable funding risk profile of the banking industry. Our research adds to the 

evidence that the NSFR as a risk metric is measuring what it is intended to measure. 

III. How the NSFR Works 

All versions of the NSFR rely on two key concepts. The first is available stable funding (ASF), which is intended to measure 

the portion of a banking organization’s funding that is stable. For example, a bank funded by FDIC-insured retail transactional 

deposits and long-term borrowing would have high ASF. At the other extreme, an institution funded with overnight 

repurchase agreements would have low ASF. 

The other key concept is required stable funding (RSF), which determines the amount of stable funding a banking 

organization needs based on its asset mix. A bank whose assets consist of unencumbered, liquid investments, for example, 

might be able to operate safely with lower levels of stable funding since it could more easily sell its assets for cash if other 

sources of funding were not available. Conversely, a bank with a large proportion of long-maturity, illiquid assets would 

require more stable funding sources since it could not easily convert its assets into cash if other funding becomes unavailable 

or prohibitively expensive. 

12 For example, cash and interest-bearing balances due from depository institutions are listed as line item RCON0071 in FFIEC Form 051 and as RCFD0071 on 

FFIEC Forms 031 and 041. Both are described by the RIS variable CHBALI. 

13 For example, RIS variable NTRTMLGJ tracks time deposits over the deposit insurance limit, whether that limit is $100,000 (1980 to 2008) or $250,000 (2008 

to present). 

14 In constructing their NSFR proxies, Hong et al. (2014) and DeYoung and Jang (2016) assume all securities (and loans) are unencumbered. 

5 
-

Once these ASF and RSF amounts have been determined—in a manner we discuss shortly—the NSFR standard is that 

the ASF should be equal to or greater than a prescribed fraction of the RSF. As originally published by the BCBS, the standard 

would be that ASF should be equal to or greater than 100 percent of RSF. Defining the NSFR as the ratio of ASF to RSF, the 

minimum requirement would be an NSFR of one (or, expressed in different units, 100 percent). The final NSFR rule requires 

an NSFR of at least 100 percent for institutions subject to the Category I and II capital standards, those subject to Category 

III capital standards that have at least $75 billion in average weighted short-term wholesale funding, and the depository 

institution subsidiaries of these institutions with at least $10 billion in consolidated assets. Other institutions subject to 

Category III standards, along with their depository institution subsidiaries with at least $10 billion in consolidated assets, are 

required to maintain an NSFR of 85 percent. Institutions subject to Category IV standards with at least $50 billion in average 

weighted short-term wholesale funding are required to maintain an NSFR of at least 70 percent, while other institutions 

subject to the Category IV standards are not subject to an NSFR requirement. No depository institution subsidiaries of 

institutions subject to Category IV standards are subject to an NSFR requirement.15 

To implement the NSFR, the concepts of ASF and RSF need to be operationalized. The ASF and RSF are generally 

weighted sums of balance sheet items, where the weights are based on certain liquidity characteristics of the balance sheet 

item. These characteristics include asset maturity and encumbrance, the marketability of assets, the sources and maturity of 

funding, and the size and type of off-balance sheet commitments. 

A. Available Stable Funding 

The ASF is calculated by applying ASF factors to each funding source. Higher ASF factors represent greater stability of 

that funding source (for example, longer-term borrowing) and thus increase the NSFR, while lower ASF factors (for example, 

shorter-term borrowing and repurchase agreements) represent relatively unstable funding sources and hence decrease the 

NSFR. Table 1 shows the ASF factor weights that the final NSFR rule assigns to various funding sources.16 

Table 1 

The Final NSFR Rule Assigns Higher Available Stable Funding (ASF) Factors to Longer Term Liabilities and 

Deposits That Are Fully Insured by the FDIC 

ASF Factor Description 

100% • Regulatory capital elements 

• Liabilities that mature one year or more from the calculation date 

95% • Affiliated sweep deposits 

• FDIC-insured retail deposits 

90% 

• Retail deposits not fully insured or insured under non-FDIC regimes 

• Fully insured brokered depositsa 

• Brokered depositsa with a remaining maturity of one year or more 

• Partially insured sweep deposits 

50% 

• Loans from nonfinancial sector entities that mature in less than one year 

• Loans from financial sector entities that mature in six months or more but less than one year 

• Securities issued with a remaining maturity of six months or more but less than one year 

• Operational deposits 

• Brokered depositsa with remaining maturity of six months or more but less than one year 

• Brokered depositsa

 that are not fully insured 

• All other liabilities with remaining maturity of six months or more but less than one year 

• Nondeposit retail funding 

15 See 86 Fed. Reg. at 9128–9131. 

16 For complete details, see 86 Fed. Reg. at 9141–9152. 

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ASF Factor Description 

0% 

• Trade date payables 

• Brokered depositsa

 with remaining maturity of less than six months 

• Loans from financial sector entities that mature in less than six months 

• Securities issued by a covered company with a remaining maturity of less than six months 

• Total derivatives assets minus total derivatives liabilities, if negative 

• All other liabilities with remaining maturity of less than six months, or that have open maturity 

Source: 86 Fed. Reg. at 9120. 

a

 The final Net Stable Funding Ratio rule groups brokered deposits into sweep deposits, reciprocal deposits, and all other brokered 

deposits. We use the term brokered deposits when referring to all other brokered deposits. 

Table 1 shows that the final NSFR rule typically treats funding sources as highly stable if they are provided by retail 

customers or counterparties, covered by deposit insurance, mature in more than one year, or have some combination of 

those features. The rule assigns lower ASF weights to shorter-term, uninsured funding sources. Funding from financial sector 

entities is typically assigned a lower ASF weight than funding from other types of customers and counterparties.17 Funding 

that matures in less than six months is treated as less stable than funding that matures over a longer period. 

B. Required Stable Funding 

The RSF is calculated by applying RSF factors to each class of assets to represent the relative liquidity of those assets. A 

higher RSF factor represents more illiquid assets (for example, long-term loans or complex structured investment products). 

Because the RSF is in the denominator of the NSFR, a higher RSF factor will, all else equal, decrease the NSFR. A lower 

RSF factor represents higher liquidity of assets (for example, cash or government securities) and, all else equal, increases 

the NSFR. Table 2 shows the RSF factor weights that the final NSFR rule assigns to various categories of assets. Assets 

are assigned higher RSF weights if they mature in more than one year, if the assets are past due by 90 days or more or in 

nonaccrual status, and if they are encumbered (that is, pledged as collateral), since those characteristics make an asset more 

difficult to sell.18 

Table 2 

The Final NSFR Rule Assigns Lower Required Stable Funding (RSF) Factors to Shorter-Maturity Assets and Assets 

That Are Unencumbered and Fully Performing 

RSF Factor Description 

0% 

• Currency and coin, including cash items in the process of collection 

• Reserve Bank balances or claims on Reserve Banks that mature in less than six months 

• Claims on foreign central banks that mature in less than six months 

• Trade date receivables 

• Any other level 1 liquid asseta 

• Loans to financial sector entities that mature within six months and are secured by rehypothecatable 

level 1 liquid assets 

5% • Undrawn amount of committed credit and liquidity facilities provided to customers and 

counterparties 

15% 

• Level 2A liquid assetsb 

• Loans to financial sector entities that mature within six months and are not secured by 

rehypothecatable level 1 liquid assets 

17 “Financial sector entities” are defined in the LCR final rule as “regulated financial companies, investment companies, non-regulated funds, pension funds, 

investment advisers, or a consolidated subsidiary of the foregoing.” See 79 Fed. Reg. at 6145. 

18 See 86 Fed. Reg. at 9152–9189. 

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RSF Factor Description 

50% 

• Level 2B liquid assetsc 

• Loans to financial sector entities that mature in six months or more but less than one year 

• Operational deposits held at financial sector entities 

• Assets with RSF weights of 0 percent to 15 percent encumbered for six months or more but less than 

one year 

• Lending to nonfinancial sector entities that matures in less than one year 

• All other assets that mature in less than one year 

65% 

• Retail mortgages that mature in one year or more, are assigned a risk weight of no greater than 50 

percent, and are not 90 days or more past due or in nonaccrual status 

• Loans to nonfinancial sector entities that mature in one year or more and are assigned a risk weight of 

no greater than 20 percent 

85% 

• Retail mortgages that mature in one year or more, are assigned a risk weight of greater than 50 

percent, and are not 90 days or more past due or in nonaccrual status 

• Loans to nonfinancial sector entities that mature in one year or more and are assigned a risk weight of 

greater than 20 percent 

• Publicly traded common equity shares that are not high-quality liquid assets 

• Securities, other than publicly traded common equity shares, that mature in one year or more, and are 

not high quality liquid assets 

• Commodities for which an active exchange-traded derivatives market exists 

• The fair value of a bank’s contribution to a central counterparty’s mutualized loss sharing agreement 

• The fair value of initial margin provided by the bank for derivatives transactionsd 

100% 

• Loans to financial sector entities that mature in one year or more 

• Any other unencumbered and performing asset not described above 

• Assets 90 days or more past due or in nonaccrual status 

• Assets encumbered for one year or more 

• Total derivatives assets minus total derivatives liabilities, if positive 

• 5 percent of total derivatives liabilities 

Source: FDIC. 

Note: Assets can receive an RSF weight of 0 percent, 5 percent, 10 percent, or 15 percent only if they are unencumbered or encumbered 

for less than six months, and if they are not past due by 90 days or more or in nonaccrual status. Assets can receive an RSF weight of 50 

percent, 65 percent, or 85 percent only if they are unencumbered or encumbered for less than one year and are not past due by 90 days 

or more or in nonaccrual status. Assets encumbered for less than six months are treated as unencumbered to calculate the Net Stable 

Funding Ratio (NSFR) and Research Information System-NSFR. Assets encumbered for more than six months but less than one year 

receive an RSF weight that is the greater of 50 percent or the weight they would receive if they were not encumbered. 

a

 Level 1 liquid assets are defined in the Liquidity Coverage Ratio (LCR) final rule to include Federal Reserve Bank balances, foreign 

withdrawable reserves, U.S. government securities, certain sovereign and multilateral organization securities, and certain foreign 

sovereign debt securities. See 79 FR at 61455–61457. 

b

 Level 2A liquid assets are defined in the LCR final rule to include U.S. government-sponsored enterprise (GSE) securities and certain 

sovereign and multilateral organization securities. See 79 FR at 61457–61459. 

c

 Level 2B liquid assets are defined in the LCR final rule to include corporate debt securities and publicly traded shares of common 

stock. See 79 FR at 61459 – 61462. A 2019 rule amended the LCR rule to include investment-grade municipal obligations in Level 2B 

liquid assets. See 84 FR at 25975. 

d

 Initial margin is assigned an RSF factor that is the greater of 85 percent or the RSF factor assigned to the asset comprising the initial 

margin. 

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RSF factors are associated with the amount of time until the asset matures, the quality of the asset (i.e., whether 

contractual payment obligations have been made), and customer or counterparty type. For example, covered institutions 

generally need to maintain less stable funding to support loans to financial sector entities since, as stated in the preamble 

to the final rule, choosing not to roll over a loan to a financial sector entity creates less business and reputational risk than 

choosing not to roll over a loan to a different type of customer or counterparty.19 Off-balance sheet commitments can also 

increase required stable funding. 

Like risk weights in capital calculations, the ASF and RSF factors may provide incentives to hold or use some assets 

and liabilities rather than others. For example, if a bank needed to increase its NSFR, management may have incentives to 

increase the relative amount of assets with low RSF factors and decrease the relative amount of assets with high RSF factors. 

IV. How the RIS-NSFR Proxy Works 

Calculating the NSFR of an institution requires asset and liability data that are disaggregated by counterparties, 

maturities, and encumbrances. Such data are not publicly available. Following the methodology introduced by Hong et al. 

(2014) and DeYoung and Jang (2016), we construct a weighted ratio of equity, liabilities, and assets based on public data. The 

ratio is intended as a proxy for the NSFR as it is defined in the final NSFR rule. 

The proxy uses quarterly Call Report data reported by FDIC-insured institutions (henceforth, banks), as processed and 

stored in the RIS data warehouse. RIS assigns Call Report line items to standardized variables so that the line items are 

comparable across time and Call Report form. For example, the RIS variable NTRTMLGJ always represents a bank’s “time 

deposits over the standard insurance limit held in domestic offices,” whether the underlying data are from Call Report form 

031, 041, or 051 and whether the deposit insurance limit is $100,000 or $250,000. Since the data in our calculations come from 

RIS, we will refer to our proxy as the RIS-NSFR.20 

We calculate RIS-NSFRs for all banks existing during the period 2002–2020 for which we have data.21 We also calculate 

aggregate RIS-NSFRs for certain subsets of banks by summing the ASFs and RSFs for all banks in a subset and finding the 

resulting quotient for that subset. The aggregate RIS-NSFR is algebraically identical to a weighted average RIS-NSFR of all 

banks in the subset, where the weights are individual bank ASFs. These calculations are meant to focus on the RIS-NSFR as a 

risk measure and not as a regulatory requirement. To reiterate an earlier point, the NSFR applies by regulation only to a small 

subset of insured banks and no plans exist to apply it to other banks. 

Any Call Report-based proxy of the NSFR will be imperfect because a significant amount of the information needed to 

calculate the NSFR defined in the final NSFR rule is not reported on the Call Report. Nonetheless, as described by Hong et al. 

(2014) and DeYoung and Jang (2016) and revisited here, Call Report proxy measures of ASFs and RSFs for many Call Report 

categories are feasible. Insofar as we can translate the NSFR factor categories delineated in the final NSFR rule to a Call Report 

line item, we assign an ASF or RSF factor to the RIS variable corresponding to the Call Report line item. 

A summary of the factor assignments is depicted in Table 3 and Table 4. A complete list of every RIS variable for which an 

ASF or RSF factor was assigned by the proxy is contained in Appendix A, along with a detailed discussion of the assumptions 

made in the calculations. 

The RIS-NSFR differs from the other NSFR proxy measures discussed in Section II in several respects. The RIS-NSFR is 

based on the final NSFR rule as defined by U.S. bank regulators in 2021. The proxy developed by King (2013) is based on the 

19 See 86 Fed. Reg. at 9153. 

20 A complete set of RIS data is available at https://www.fdic.gov/foia/ris/. A mapping of RIS variables to Call Report line items is available at 

https://www5.fdic.gov/dict/app/templates/Index.html#!/Main. 

21 FDIC-insured institutions that filed Thrift Financial Reports are excluded.

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Table 3 

The RIS-NSFR Assigns Available Stable Funding (ASF) Factor Weights to Funding Sources According to the 

Framework in the Final NSFR Rule 

ASF Factor Description of Call Report Line Items Used to Calculate RIS-NSFR 

100% • Equity capital 

• Other borrowings, including subordinated debt, with maturities more than one year 

95% 

• Domestic time deposits from retail customers,a excluding time deposits over the standard FDIC 

insurance limitb and brokered deposits 

• Halfc of domestic transaction deposits of individuals, partnerships, and corporations 

90% 

• Halfc of deposits held in domestic offices and all deposits held in foreign officesd of individuals, 

partnerships, and corporations 

• Savings deposits and money market deposit accounts, including those in foreign officesd 

• Domestic time deposits from retail customers over the standard FDIC insurance limitb 

• Foreign time depositsd from retail customers 

• Insured brokered deposits with maturities more than one year 

50% 

• Time deposits from non-retail customers a, including those in foreign officesd and including insured 

brokered deposits with maturities less than one year 

• Transaction accounts other than those of individuals, partnerships, and corporations, including those in 

foreign officesd 

• Half e of other borrowings with maturities less than one year 

0% • All other liabilities 

Source: FDIC. 

Note: RIS is the FDIC’s Research Information System. NSFR is the Net Stable Funding Ratio. Author assumptions used to develop certain 

elements of the RIS-NSFR are detailed in Appendix A. 

a

 Time deposits in domestic offices are assumed to have the same distribution of depositor type as nontransaction deposits in 

domestic offices. 

b

 The standard insurance limit was $100,000 before March 2010 and $250,000 afterward. The share of deposits over the insurance limit 

from retail customers is assumed to be identical to the share of all deposits from retail customers. 

c

 Apportioned equally to stable retail deposits (ASF factor of 95, assumed to be fully insured by the FDIC) and other retail deposits (ASF 

factor of 90, assumed to not be fully insured, or insured under non-FDIC deposit insurance regimes). 

d The distribution of foreign deposits among transaction accounts, savings accounts, money market deposit accounts, and time 

deposits is assumed to be the same as the distribution of domestic deposits among various account types. 

e Half of borrowings with maturities of one year or less are assumed to have maturities of less than six months, meaning they receive an 

ASF factor of zero. 

BCBS (2010) definition of the NSFR. Hong et al. (2014) developed one proxy based on the BCBS (2010) definition and another 

based on the BCBS (2014) definition. Both DeYoung and Jang (2016) and Hoerova et al. (2018) base their proxies on the BCBS 

(2014) definition. 

The data sources used to construct the proxies differ as well. Hong et al. (2014), DeYoung and Jang (2016), and the RISNSFR all use Call Report data to construct their NSFR proxies. King (2013) uses Bankscope data to construct his NSFR proxy, 

and Hoerova et al. (2018) use the ECB Individual Balance Sheet Items database to construct their proxy. 

Any attempt to create an NSFR proxy using tangential data sources such as the Call Report requires making a number 

of assumptions, including assumptions about the distribution of retail versus wholesale customers and counterparties, 

the maturity of certain assets and funding sources, and encumbrance of assets. Section F of Appendix A compares the 

assumptions used to construct the RIS-NSFR to the assumptions King (2013), Hong et al. (2014), DeYoung and Jang (2016), 

and Hoerova et al. (2018) used to construct their NSFR proxies. 

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Table 4 

The RIS-NSFR Assigns Required Stable Funding (RSF) Factor Weights to Assets According to the Framework in 

the Final NSFR Rule 

RSF Factor Description of Call Report Line Items Used to Calculate RIS-NSFR 

0% 

• Cash and balances due 

• One-third of unencumbereda

 and a portionb of encumberedc performingd federal funds sold and reverse 

repurchase agreementse 

• Unencumbereda and one-third of encumberedc performingd U.S. government agency and Treasury 

securities (not including mortgage-backed securities (MBS)) 

5% • Unused commitments and lines of credit 

15% 

• One-third of unencumbereda and a portionb of encumberedc performingd federal funds sold and reverse 

repurchase agreementse 

• Unencumbereda and one-third of encumberedb performingc loans to depository institutions with 

maturities of one year or less, net of unearned incomef and allowances for loans and lease losses (ALLL)g 

• Unencumbereda and one-third of encumberedc performingd MBS issued or guaranteed by U.S. 

government agencies or government-sponsored enterprises (GSEs) 

• Unencumbereda and one-third of encumberedc performingd loans with maturities of one year or less to 

nondepository institutions, domestic branches of foreign banks or agencies, and other loans, net of 

unearned incomef and ALLLg 

50% 

• One-third of unencumbereda and a portionb of encumberedc performingd federal funds sold and reverse 

repurchase agreementse 

• Unencumbereda and two-thirds of encumberedc performingd loans and leases with remaining 

maturities of one year or less,h other than those given a 15 percent RSF factor, net of unearned incomef 

and ALLLg 

• Unencumbereda and two-thirds of encumberedc performingd investment grade debt securities or equity 

securities that are part of a major equity market indexi 

• One-third of encumberedc performingd securities and loans that would receive an RSF weight of less 

than 50 percent if unencumbered and performing 

65% 

• Unencumbereda and two-thirds of encumberedc performingd closed-end loans secured by first liens on 

1–4 family properties with remaining maturities of more than one year,h net of unearned incomef and 

ALLLg 

• Unencumbereda and two-thirds of encumberedc performingd leases and agricultural loans, commercial 

and industrial loans, loans to U.S. municipalities, loans to foreign governments, commercial real estate 

loans, loans secured by farmland, and consumer loans with risk weights of 20 percent or lessj and 

remaining maturities of more than one year,h net of unearned incomef and ALLLg 

85% 

• Unencumbereda and two-thirds of encumberedc performingd open-end loans secured by 1–4 family 

properties, and closed-end loans secured by junior liens on 1–4 family properties, with remaining 

maturities more than one year,h net of unearned incomef and ALLLg 

• Unencumbereda and two-thirds of encumberedc performingd loans, securities, and trading assets with 

remaining maturities more than one year,h

 net of unearned incomef and ALLL,g other than those already 

listed 

• Unencumbereda and two-thirds of encumberedc performingd non-investment grade debt securities or 

equity securities not part of a major equity market indexi 

• Unencumbereda and two-thirds of encumberedc performingd private-label MBS 

100% 

• All other assets, including loans to financial institutions with maturities of more than one year,h 

nonperformingd assets, and assets encumbered for more than one year,h net of unearned incomef and 

ALLLg 

• Five percent of the value of derivatives with positive fair value minus derivatives with a negative fair 

value, if the difference is positive 

11 
Source: FDIC. 

Note: RIS is the FDIC’s Research Information System. NSFR is Net Stable Funding Ratio. Author assumptions used to develop certain 

elements of the RIS-NSFR are detailed in Appendix A. 

a

 The percent of a loan (or security) that is unencumbered is assumed to be equal to the fraction of total loans (or securities) that are 

unencumbered or unpledged. 

b See Appendix A for the percentages of encumbered federal funds sold and reverse repurchase agreements assigned to the RSF 0, RSF 

15, RSF 50, and RSF 100 categories. 

c Encumbered loans (or securities) are apportioned equally across the three encumbrance periods: less than six months, between six 

and twelve months, and more than twelve months. Assets are assumed to not be encumbered by more than their maturity (i.e., loans 

with maturities less than a year are not apportioned to an encumbrance of more than a year). We follow the encumbrances rules 

delineated in the final NSFR rule (See 86 FR at 9166). Those that are encumbered for more than one year are assigned an RSF factor of 

100, those that are encumbered for six months to one year are assigned an RSF factor that is the greater of 50 or the factor they would 

be assigned if they were not encumbered, and those encumbered for less than six months are assigned the same RSF factor they would 

be assigned if they were not encumbered. 

d Performing loans are those not past due by 90 days or more or in nonaccrual status. The Call Report contains only aggregated 

debt securities 90 days or more past due and in nonaccrual status, so we assume the percent of each debt security type that is 

nonperforming is equal to the fraction of total debt securities that are nonperforming. 

e Unencumbered, performing federal funds sold and reverse repurchase agreements are equally apportioned across RSF factor weights 

of 0, 15, and 50, representing maturities of six months or less, six to twelve months, and more than one year, respectively. 

f We assume the fraction of unearned income in each loan category is equal to the fraction of total unearned income in total loans. 

g

 We assume the fraction of allowances for loan and leases losses (ALLL) in each loan category is equal to the fraction of total allowances 

for loan and leases losses in total nonperforming loans. 

h

 We assume the fraction of loans in each category that mature in one year or less is equal to the fraction of total loans that mature in 

one year or less. 

i

 We assume a 1:1 split between investment grade and non-investment grade debt securities and between equity securities that are and 

are not part of a major equity market index. 

j

 We assume the fraction of each asset with a risk weight of 20 percent or less is equal to the fraction of total balance sheet assets with a 

risk weight of 20 percent or less. 

A. RIS-NSFR Validation Exercises 

As a check of the ability of RIS-NSFR to track the NSFR, we compared the RIS-NSFRs of the insured depository institution 

(IDI) subsidiaries of a few large bank holding companies (BHCs) to NSFR estimates for these BHCs. We find a strong positive 

correlation between the two measures. An anonymized summary is contained in Appendix B. 

As the Call Report changes, some line items on the report are added or removed. To ensure our methodology is 

consistent over time, we check that the balance sheet items used in RIS-NSFR constitute the whole of assets reported by the 

banks. For every quarter since 2002, our methodology accounts for more than 96 percent of assets (or liabilities plus equity) 

reported by banks. Detailed discussions are provided in Sections B.6 and C.9 of Appendix A. 

V. Results Part 1: Correlation With Bank Stress Events 

The proxy RIS-NSFR appears to be a useful indicator of the ability of institutions to withstand liquidity stress. For this 

exercise, we calculated RIS-NSFRs for all non-thrift banks and examined differences in RIS-NSFRs across groups of banks 

that did or did not experience a liquidity stress event. Our bank universe does not include the large investment banks that 

experienced liquidity crises in 2008 or any thrift institutions that failed during the crisis. Nonetheless, the banks in our sample 

experienced sufficient stress such that we can determine if our proxy shows meaningful differences between the liquidity 

profiles of banks that did or did not fail and banks that did or did not participate in the DGP during the 2008 liquidity crisis. 

While this paper calculates and analyzes RIS-NSFRs for small banks, U.S. banking regulators have not required small 

banks to meet the NSFR standard and are not considering doing so. As noted, the FDIC does not use either the NSFR or 

12 
 

any proxy of it to assess the liquidity risk profile of small banks. We analyze small banks to examine the performance of the 

RIS-NSFR measure as a risk indicator and to discuss the recent liquidity positions and trends for small and midsize banks 

compared to those for large banks generally subject to the NSFR standard. 

A. Low RIS-NSFR Associated With Higher Risk of Failure 

If the NSFR is a good indicator of the ability of financial institutions to withstand a liquidity stress event, then institutions 

with lower RIS-NSFRs would have been more likely to fail or experience liquidity stress during the financial crisis. The 

RIS-NSFR is in effect a summary risk measure that captures the effects of loan concentrations, liquid asset holdings, and 

dependence on volatile funds. As such, it would be somewhat surprising if historical data did not show an association 

between low RIS-NSFR and bank failure. 

In Figure 1, we show the median and lowest fifth percentile RIS-NSFRs for non-thrift banking institutions for each quarter 

from 2006 to 2012, distinguishing between banks that did or did not fail before 2012.22 Leading up to and during the financial 

crisis, the median and lowest fifth percentile of RIS-NSFRs of institutions that failed before 2012 were consistently lower than 

those of institutions that did not fail before 2012, suggesting that banks with lower RIS-NSFRs had a higher risk of failing 

during and immediately following the financial crisis.23 

Figure 1 

Banks That Failed Before 2012 Had Lower RIS-NSFRs Than Banks That Did 

Not Fail 

RIS-NSFR 

1.6 

1.4 

1.2 

1.0 

0.8 

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 Dec-16 Dec-19 

Did Not Fail (50th Percentile) Did Not Fail (5th Percentile) 

Failed (50th Percentile) Failed (5th Percentile) 

Source: FDIC Research Information System (RIS). 

Note: NSFR is Net Stable Funding Ratio. Shading denotes recessions (Dec 2007–Jun 2009 and 

Feb 2020–Apr 2020). 

In interpreting Figure 1, one must keep in mind that institutions can fail for many reasons. Insufficient stable funding is 

one possible reason for failure, but the data do not distinguish between institutions that failed because of funding instability 

and those that failed for other reasons. However, the levels of median and fifth percentile RIS-NSFRs for institutions 

22 For this analysis, banks were considered to have failed if they are on the FDIC’s Failed Bank List retrieved August 3, 2018, https://www.fdic.gov/resources/ 

resolutions/bank-failures/failed-bank-list/. 

23 To support our results, we run a two-sample t-test on the RIS-NSFRs before 2012, comparing the distribution of RIS-NSFRs for institutions that failed before 

2012 to the distribution of RIS-NSFRs for institutions that did not fail before 2012. We reject the null hypothesis that variances of the two distributions are 

equal (p_F < .0001) and the null hypothesis that the two means are equal (t = 8.30). 

13 
that eventually failed suggest that a low RIS-NSFR indicates a higher risk of failure.24 Insufficient stable funding can be a 

contributing factor to bank failure or a consequence of other proximate causes for bank failure. 

B. Institutions That Participated in the DGP Had Lower RIS-NSFRs 

Given that not all failures during and immediately after the financial crisis resulted from insufficient stable funding, 

the value of the RIS-NSFR as an indicator of potential liquidity stress can be further tested by comparing the RIS-NSFRs of 

institutions that participated in the DGP to those of institutions that opted out. We consider institutions to have participated 

in the DGP if they are not included in the list of institutions that opted out of the program or if they were not subsidiaries of 

bank holding companies that opted out of the program.25 While not all DGP participants were facing an emergency liquidity 

situation, we used DGP participation as a proxy for banks that experienced liquidity stress between October 2008 and 

December 2010, the period during which the DGP operated. 

Figure 2 

Banks That Participated in the DGP Had Lower RIS-NSFRs Than Banks That 

Opted Out of the DGP 

RIS-NSFR 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 Dec-16 Dec-19 

Non-Participants (50th Percentile) Non-Participants (5th Percentile) 

DGP Participants (50th Percentile) DGP Participants (5th Percentile) 

Source: FDIC Research Information System (RIS). 

Note: NSFR is Net Stable Funding Ratio. DGP is Debt Guarantee Program. Shading denotes 

recessions (Dec 2007–Jun 2009 and Feb 2020–Apr 2020). 

To show the relationship between DGP participation and NSFR, we chart the median RIS-NSFR for non-thrift banking 

institutions between 2006 and 2012, distinguishing between institutions that did or did not participate in the DGP (Figure 2). 

As expected, banks that participated in the DGP had a lower median RIS-NSFR leading up to and during the financial crisis 

24 While the RIS-NSFRs in Figure 1 are all above 1.0 in the years leading up to the crisis, the reader should note that RIS-NSFR probably overestimates the NSFR 

due to the authors’ conservative assumptions, as detailed in Appendix A. If RIS-NSFR overestimates NSFR by 13 percentage points, as shown in Appendix B for the 

largest banks, then Figure 1 would indicate that NSFRs for the lowest fifth percentile of failed banks were below 1.0 during and after the 2008 financial crisis.

25 See Federal Deposit Insurance Corporation, “Temporary Liquidity Guarantee Program Opt-Out Lists,” https://www.fdic.gov/regulations/resources/tlgp/ 

optout.html. Any institution that existed from March 2008 through December 2010 and was not included in the list of entities that opted out of the DGP was 

considered to have participated in the program for the purposes of this analysis. Not all of these institutions necessarily issued debt guaranteed through the 

DGP. 

14 
than banks that opted out of the program.26 The RIS-NSFRs of both groups of institutions improved after the financial crisis 

and following the end of the program in 2011, likely from the economic and financial recovery.27 

We can also see in Figure 2 that the fifth percentile of banks that participated in the DGP had RIS-NSFRs well below 1.0 

preceding and immediately following the 2008 financial crisis. Meanwhile, the fifth percentile of banks that opted out of the 

program had RIS-NSFRS greater than 1.0 over that period. The RIS-NSFRs of DGP participants improved beginning in 2009, 

while the RIS-NSFRs of institutions that opted out remained fairly stable and then declined between 2013 and 2019. 

VI. Results Part 2: RIS-NSFR Trends and Drivers 

In this section, we describe how our NSFR proxy can be used to track the stable funding positions of banks. We calculate 

aggregate RIS-NSFRs for the three groups of banks described in Section I: small, midsize, and large.28 Since we assign banks 

to these groups using assets at a point in time, the set of banks in each group remains fixed during our analysis period.29 We 

examine historical trends in the aggregate RIS-NSFRs and describe the underlying balance sheet changes.30 

Figure 3 below shows aggregate RIS-NSFRs since 2002 for the three groups of banks.31 An overview of the figure reveals 

several stylized facts: 

1. Before the 2008 financial crisis, aggregate RIS-NSFRs were correlated with bank size and behaved similarly 

across the three size groups; 

2. After bottoming out during the recession that accompanied the 2008 financial crisis, aggregate RIS-NSFRs 

increased between 2010 and 2013 for all three size groups; 

3. Between 2013 and the end of 2019, the trends of aggregate RIS-NSFRs among the three size groups 

diverged; and 

4. Aggregate RIS-NSFRs increased rapidly for all three size groups in 2020, a trend contemporaneous with the 

pandemic. 

A. RIS-NSFRs Before the 2008 Financial Crisis 

Figure 3 shows that before the 2008 financial crisis aggregate RIS-NSFRs were lower for large banks, suggesting a general 

correlation between net stable funding and asset size. In addition, the aggregate RIS-NSFR curves for the three groups 

generally moved in parallel. These parallel movements suggest that banks across all size groups shared similar strategies for 

managing stable funding risk in the years leading up to the crisis. 

26 To support our results, we run a two-sample t-test on the RIS-NSFRs before 2012, comparing the distribution of RIS-NSFRs for institutions that participated 

in the DGP to the distribution of RIS-NSFRs for institutions that did not participate. We reject the null hypothesis that variances of the two distributions are 

equal (p_F < .0001) and the null hypothesis that the two means are equal (t = -4.00). 

27 To support our results, we run a two-sample t-test on the RIS-NSFRs after 2012, comparing the distribution of RIS-NSFRs for institutions that participated 

in the DGP to the distribution of RIS-NSFRs for institutions that did not participate. We reject the null hypothesis that variances of the two distributions are 

equal (p_F < .0001) but cannot reject the null hypothesis that the two means are equal (t = 1.11, p_t = 0.2663). 

28 As noted in Section I, our aggregate RIS-NSFR is a weighted average of the RIS-NSFRs in a group. 

29 See discussion at the end of Section I, with corresponding footnote 11, for size grouping details. 

30 A detailed methodology of the decomposition of a change in RIS-NSFR is presented in Appendix C. Note that since the aggregate RIS-NSFRs are weighted 

averages of RIS-NSFRs for individual banks, changes in the aggregate RIS-NSFRs over time could reflect changes in the composition of banks in a group, 

rather than the distributions of liquidity and stability of assets and liabilities within that group. As a robustness check, the authors compared the aggregate 

RIS-NSFRs charted in Figure 3 to median RIS-NSFRs for each of the three size groups. The trends of the two sets of lines were similar, with two notable 

exceptions: first, the median RIS-NSFR for large banks dropped 12 percentage points between 2001 and 2007, compared to just 3 percentage points for the 

aggregate RIS-NSFR for large banks; second, the median RIS-NSFR for midsize banks fell 15 percentage points between 2010 and 2018, compared to almost 

no change in the aggregate RIS-NSFR in the same time span. 

31 Aggregate RIS-NSFR is calculated as the sum of total ASF for a group (for example, large banks) divided by the sum of total RSF for the group. 

15 
Figure 3 

RIS-NSFRs of Large Banks Increased Considerably Since 2009 

RIS-NSFR

1.6

1.5

1.4

1.3

1.2

1.1

1.0

0.9

0.8

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 Dec-16 Dec-19

Large Midsize Small

Source: FDIC Research Information System (RIS). 

Note: NSFR is Net Stable Funding Ratio. Shading denotes recessions (Dec 2007–Jun 2009 

and Feb 2020–Apr 2020). 

B. RIS-NSFRs Immediately After the 2008 Financial Crisis

Figure 3 also shows how the funding and asset portfolios of banks changed after the 2008 financial crisis. Aggregate 

RIS-NSFRs for all groups increased between second quarter 2009 and first quarter 2013; the aggregate RIS-NSFR increased 

approximately 7 percentage points for the smallest banks, 10 percentage points for midsize banks, and 19 percentage points 

for the largest banks.32 An arithmetic decomposition of these increases reveals that portfolio concentrations of liabilities 

that had been assigned ASF factors of 90 and 95 percent (primarily retail deposits) accounted for at least 50 percent of the 

changes in aggregate RIS-NSFRs of all three size categories over the period immediately following the recession. In addition, 

RSF declined relative to total, unweighted assets for all three size categories, contributing to rising NSFRs over this period.33 

In the rest of this subsection, we delve into the specific assets and liabilities that make up these RSF and ASF categories and 

examine how these assets and liabilities changed from second quarter 2009 to first quarter 2013. 

1. Changes in Funding Composition 

Figure 4 shows changes in various bank funding sources between June 2009 and March 2013. For each funding source, 

the changes are broken out into three bar graphs representing the three size groups. The funding sources are then presented 

in order of declining ASF factor. The set of charts confirms the first decomposition observation described above: in the four 

years immediately following the end of the recession in 2009, banks in all three size categories increased their portfolio 

concentrations in liabilities with high ASF factors, particularly deposits. These concentrations contrast with the portfolio 

reductions of liabilities with ASF factors at or below 50 percent. All else equal, the reshuffling of bank portfolios from liabilities 

with low ASF factors to liabilities with high ASF factors would cause an increase in RIS-NSFR. The pattern can be observed 

32 As summarized in Table C.1 in Appendix C. 

33 See the Asset Composition Effect of Table C.1 in Appendix C. 

16 
most prominently in Figure 4 for large banks. Over the four years, large banks increased their deposits as a portion of assets 

by 8.6 percentage points and reduced short-term and other liabilities by 7.3 percent. The aggregate effect of the shifting of 

funding sources from sources with low ASF factors to sources with high ASF factors increased the aggregate RIS-NSFR of large 

banks by 11 percentage points between June 2009 and March 2013, as calculated by our decomposition exercise.34 

Figure 4 

Deposits Increased as a Percent of Assets for All Size Groups 

Change in Funding Source as a Percent of Total Assets 

June 2009–March 2013 

10 

8.6 8 

6 

4 3.6 3.7 

2 

0.0 0 

-2 

-1.3 -1.3 -4 -2.2 

-3.9 -6 

-8 -6.4 

Equity and Long-Term Deposits Short-Term Liabilities 

Liabilities (ASF 50% to 95%) (ASF 0% to 50%) 

(ASF 100%) 

Small Midsize Large 

Source: FDIC Research Information System. 

Note: ASF is available stable funding. 

0.1 

-0.1 -0.9 

Other Liabilities 

(ASF 0%) 

Since the growth of deposits is so prominent in Figure 4, we investigate this category further by separating the growth 

of deposits into retail and non-retail deposits, presented in Figure 5. Figure 5 shows that the increase in deposits as a share 

of assets was driven by retail deposits for all three size groups. Stable retail deposits, or fully insured transaction deposits of 

retail customers, receive ASF weights of 95 percent, while other retail deposits receive ASF weights of 90 percent. This trend 

in deposit growth would contribute to rising RIS-NSFRs for the banking industry in general, all else equal. Non-retail deposits, 

which increased for small and midsize banks and decreased for large banks, receive ASF weights of 50 percent. Figure 5 shows 

that retail deposits, which receive high ASF factors, increased more than non-retail deposits, which receive lower ASF factors, 

for all size groups. This observation supports the discussion above that aggregate RIS-NSFR growth between 2009 and 2013 

was driven in part by the reshuffling of liabilities from low ASF factor categories to high ASF factor categories. 

34 See the Funding Composition Effect of Table C.1 in Appendix C. 

17 
Figure 5 

Retail Deposits Increased for All Bank Size Groups 

Change in Deposit Type as a Percent of Total Assets 

June 2009–March 2013 

14 

12 11.4 

10 

8 

6 

4 3.3 

2.5 

2 1.2 

0.4 

0 

-2 

-4 -2.8 

Retail Deposits Non-Retail Deposits 

(ASF 90% to 95%) (ASF 50%) 

Small Midsize Large 

Source: FDIC Research Information System. 

Note: ASF is available stable funding. 

2. Changes in Asset Composition 

Changes in NSFRs can occur not only due to changes in the composition of bank funding sources but also due to 

changes in asset composition. A decomposition of the changes reveals that about 43, 44, and 73 percent of the changes 

in aggregate RIS-NSFRs of large, midsize, and small banks, respectively, between June 2009 and March 2013 were due to 

changes in RSF.35 Cash balances, which carry an RSF factor of zero, increased as a percentage of assets for all three size 

groups. This increase in cash would increase the aggregate RIS-NSFRs of all three size groups, all else equal. Figure 6 shows 

holdings of interest-bearing cash balances for the three groups of banks, as percentages of their total assets, from June 2009 

through December 2020. Since interest-bearing cash balances have an RSF factor of 0 percent, an increase in these holdings 

by banks would decrease their RSFs and correspondingly increase their NSFRs, all else equal. A comparison of the aggregate 

RIS-NSFR line graphs in Figure 3 and the interest-bearing cash balances line graphs in Figure 6 suggests that the increase in 

interest-bearing cash balances is largely responsible for the rise in aggregate RIS-NSFRs for all three size groups between June 

2009 and March 2013. 

35 As presented in Table C.1 in Appendix C. 

18 
Figure 6 

Interest-Bearing Cash Balances Represent a Relatively High Share of Assets for 

Large Banks 

Interest-Bearing Cash Balances 

Percent of Total Assets 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 Dec-16 Dec-19 

Large Midsize Small 

Source: FDIC Research Information System. 

Note: Shading denotes recessions (Dec 2007–Jun 2009 and Feb 2020–Apr 2020). 

Banks also changed their holdings of assets with high RSF factors in the years immediately following the 2008 financial 

crisis. Nonperforming assets—that is, loans or debt securities for which payments are past due by 90 days or more—peaked 

shortly after the end of the recession for all three size groups and decreased considerably as the economy improved, 

contributing to rising aggregate RIS-NSFRs for all three size categories (Figure 7). 

Assets past due by 90 days or more or in nonaccrual status receive a 100 percent RSF factor, which lowers NSFRs for 

institutions with a high percentage of nonperforming assets relative to total assets. This effect could cause NSFRs to improve 

industrywide when the economy is strong and to decline industrywide when the economy weakens. For the banking industry 

overall, the ratio of nonperforming assets to total assets peaked at 3.1 percent in March 2010 and declined to 0.5 percent by 

December 2020. While the drop appears large, it amounts to a difference of only 2.6 percent of total assets, making its effect 

on total RSF and therefore on RIS-NSFR fairly minimal. The RSF weight of 100 percent for past-due and nonaccrual assets may 

or may not be significantly higher than the RSF weight the asset would receive if it were performing.36 

To summarize this section, we return to Figure 3. The rise in aggregate RIS-NSFRs since the 2008 financial crisis is 

presented in varying degrees for all three groups of banks. Aggregate RIS-NSFRs increased for all three size groups between 

June 2009 and March 2013 as a result of increasing retail deposits, increasing cash balances, and declining nonperforming 

assets among the banking industry in general. 

The next subsection explores the reasons for the divergent RIS-NSFR trends between March 2013 and December 2019. 

36 As of December 31, 2020, the largest category of assets is RSF85, which comprises 22.4 percent of total assets, followed by RSF0 at 21.5 percent. Even if most 

of the assets in RSF85 became nonperforming, the resulting RIS-NSFR increase would be less than 4 percentage points. 

19 
Figure 7 

Nonperforming Assets Declined for All Size Groups From 2010 to 2019 

Nonperforming Assets 

Percent of Total Assets 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 Dec-16 Dec-19 

Large Midsize Small 

Source: FDIC Research Information System. 

Note: Shading denotes recessions (Dec 2007–Jun 2009 and Feb 2020–Apr 2020). 

C. The 2013-2019 Divergence of RIS-NSFR 

The next stylized fact gleaned from Figure 3 is that, between 2013 and 2019, the trend of the aggregate RIS-NSFR for large 

banks diverged from the trend of the aggregate RIS-NSFRs for small and midsize banks. Between March 2013 and December 

2019, the aggregate RIS-NSFR of large banks increased from 1.12 to 1.28. Over the same period, the aggregate RIS-NSFR of 

midsize banks decreased slightly, from 1.18 to 1.16. Meanwhile the aggregate RIS-NSFR of small banks decreased from 1.38 to 

1.21.37 This divergence stands in stark contrast to the pattern before the 2008 financial crisis during which the aggregate RISNSFRs for all three groups generally moved in lockstep. 

The disparity in aggregate RIS-NSFR trends between large banks and other banks suggests that after the crisis large 

banks started managing their liquidity and funding stability in a manner distinct from that of other banks. This change may 

have occurred in response to lessons learned during the crisis or large banks’ anticipation of and compliance with LCR and 

NSFR requirements. 

In the following subsections, we explore the trends in individual balance sheet items that drove changes in RIS-NSFR 

between 2013 and 2019. 

1. Changes in Asset Composition 

Changes in the composition of a bank’s asset portfolio would lead to changes in its NSFR (and RIS-NSFR). Holding more 

liquid assets—that is, assets that can be converted into cash relatively quickly even in a period of market stress—would lead 

to an increase in NSFR. Figure 8 shows how asset composition changed for the three size groups between 2013 and 2019. 

37 Summarized in Table C.2 in Appendix C. 

20 
Figure 8 

Changes in Asset Composition Varied Between Small, Midsize, and Large Banks 

Change in Asset Type as a Percent of Total Assets 

March 2013–December 2019 

10 

8.9 

8 

6 

4 

2.9 

2 1.8 1.9 

0.6 0.0 0 

-2 -0.3 

-1.1 -1.9 -1.7 -2.0 -4 

-6 

-8 -6.5 

Cash Securities Trading Assets Loans and Leases 

(RSF 0%) (RSF 0% to 85%) (0% to 85%) (RSF 0% to 100%) 

Small Midsize Large 

Source: FDIC Research Information System. 

Note: RSF is required stable funding. 

-0.5 -0.5 

-1.5 

Other Assets 

(RSF 100%) 

All three size groups decreased the percentage of total assets that they held in cash—that is, interest-bearing or 

noninterest-bearing balances held at other depository institutions, Federal Reserve banks, or other central banks—from 

March 2013 to December 2019. Cash receives an RSF weight of 0 percent, so these decreases in cash holdings would decrease 

the NSFR (and aggregate RIS-NSFR) for the industry, all else equal. 

Between March 2013 and December 2019, small banks significantly decreased their holdings of securities as a percent of 

assets, while midsize and large banks modestly increased their securities holdings. Certain securities, such as U.S. Treasury 

securities and U.S. government agency debt, receive low RSF weights, while others such as equity securities, private-label 

mortgage-backed securities (MBS), and corporate debt securities below investment grade receive high RSF weights. 

Loans and leases increased significantly for small banks and increased modestly for midsize and large banks. Loans 

generally receive high RSF weights, although short-term loans to financial sector entities receive low RSF weights. 

Trading assets, which receive the same RSF weight as the asset would receive if it were held for a purpose other than 

trading, declined for large banks and changed minimally for small and midsize banks. The effect of the decline in trading 

assets on the aggregate RIS-NSFR of large banks is difficult to infer since trading assets potentially include assets with low RSF 

weights such as securities issued by the U.S. Department of Treasury or a U.S. government agency and assets with high RSF 

weights such as high-yield bonds. 

The slight decline in “other assets” for each of the size groups would increase the aggregate RIS-NSFR of the industry, all 

else equal, since this category includes illiquid assets with high RSF weights such as bank premises, other real estate owned, 

and intangible assets. While the decrease in cash would generally decrease RIS-NSFRs for the banking industry in general, 

the effect on RIS-NSFRs of the changes in securities, loans, and trading assets depicted in Figure 8 is not clear. So, the overall 

effect of changes in asset composition on the aggregate RIS-NSFR of the banking industry is difficult to infer from Figure 8. 

21 
2. Changes in Securities Composition 

Although Figure 8 shows the disparate changes in securities as a percent of assets across the three size groups, the 

resulting effects on aggregate RIS-NSFR are not immediately apparent because RSF factors for securities span a large range 

(0 to 85 percent). To further investigate the effects of securities holdings on aggregate RIS-NSFR, Figure 9 shows how different 

types of securities changed as a percent of assets for each of the size groups. 

Figure 9 

Low-RSF Securities Increased for Large Banks and Decreased for Small Banks 

Change in Security Type as a Percent of Total Assets 

March 2013–December 2019 

4 

2.6 2.8 

2 

0.8 0.2 0.1 0 0.0 0.0 0.0 

-2 -0.5 -0.8 

-1.4 -1.3 -1.4

-4 -2.5 -2.6 

U.S. Treasury / MBS: Issued or Other Debt Securities Other Securities MBS: Private Label 

Government Guaranteed by a (RSF 50% to 85%) (RSF 50% to 85%) (RSF 85%) 

Agency Debt U.S. Government 

(RSF 0%) Agency or GSE 

(RSF 15%) 

Small Midsize Large 

Source: FDIC Research Information System. 

Note: RSF is required stable funding. MBS is mortgage-backed securities. GSE is government-sponsored enterprise. The RSF weights 

in the column labels apply to securities that are performing and not encumbered for more than one year. 

Large banks increased their relative holdings of securities with RSF weights of 0 percent, such as U.S. Treasury securities 

and U.S. government agency debt, and increased their relative holdings of MBS issued or guaranteed by a U.S. government 

agency or government-sponsored enterprise (GSE), which have RSF weights of 15 percent. They also decreased their relative 

holdings of private-label MBS and other debt securities, which have relatively high RSF weights of 50 to 85 percent, and 

“Other Securities,” which also have RSF factors of 50 to 85 percent and include assets such as asset-backed securities other 

than MBS, structured financial products, and equity securities. The increase in low-RSF securities and the decrease in highRSF securities would increase the aggregate RIS-NSFR of large banks, all else equal. 

Midsize banks increased their relative holdings of MBS issued or guaranteed by a U.S. government agency or GSE and 

decreased their relative holdings of U.S. Treasury and U.S. government agency debt, while their holdings of other types of 

securities changed modestly. Although holdings of securities overall as a percent of assets changed only modestly for midsize 

banks, the change in the composition of their securities portfolios shown in Figure 9 would likely increase the NSFRs and 

aggregate RIS-NSFR of midsize banks, all else equal, since the slight increase in securities as a percent of assets was driven by 

securities with low RSF weights. 

Small banks decreased their relative holdings of securities that receive low RSF weights, including U.S. Treasury and U.S. 

government agency debt and MBS issued or guaranteed by a U.S. government agency or GSE. Small banks also decreased 

their relative holdings of other debt securities, which receive high RSF weights. However, the decline in low-RSF securities 

was greater than the decline in high-RSF securities, which would decrease their aggregate RIS-NSFR, all else equal. These 

differences in changes in the composition of the securities portfolios of large, midsize, and small banks help to explain the 

divergence in their aggregate RIS-NSFRs from 2013 to 2019. 

22 
3. Changes in Loan Composition 

Small, midsize, and large banks all increased their relative holdings of loans and leases. The RSF weights of loans depend 

on collateral, borrower type, maturity, and risk weight. Figure 10 shows changes in loan types as a percent of assets for each 

size group. 

Figure 10 

Loans Secured by Real Estate Increased for Small Banks and Decreased for Large Banks 

Change in Loan Type as a Percent of Total Assets 

March 2013–December 2019 

8 

6.3 6 

4 

1.9 2.2 2 1.1 1.2 1.4 1.0 

0.6 0.5 0.4 0.3 0.4 0 0.0 0.1 

0.0 -2 -0.5 -0.2 

-4 

-3.0 

Secured by Commercial and Consumer Agricultural Financial Other Loans 

Real Estate Industrial (RSF 50% to 85%) (RSF 50% to 85%) Institutions and Leases 

(RSF 50% to 85%) (RSF 50% to 85%) (RSF 15% to 100%) (RSF 50% to 85%) 

Small Midsize Large 

Source: FDIC Research Information System. 

Note: RSF is required stable funding. The RSF weights in the column labels apply to securities that are performing and not encumbered 

for more than one year. 

Figure 10 shows that loans secured by real estate, which receive RSF weights of 50 to 85 percent, increased for small 

banks and decreased for large banks. Commercial and industrial loans, which also receive RSF weights of 50 to 85 percent, 

increased for small and large banks and decreased slightly for midsize banks. Loans to financial institutions increased for all 

size groups. Loans to financial institutions can receive RSF weights of 15 percent if they mature in less than six months. They 

receive RSF weights of 100 percent if they mature in more than one year, making loans to financial institutions the only type 

of unencumbered, performing loan eligible for an RSF weight of less than 50 percent or greater than 85 percent. The maturity 

of these types of loans is not reported separately on the Call Report. However, considering that some portion of these 

loans likely meets the criteria for a 15 percent RSF weight and a 100 percent RSF weight, the increase in loans to financial 

institutions for all banks would have an ambiguous effect on their aggregate RIS-NSFRs. 

The effects of the trends shown in Figure 10 on aggregate RIS-NSFRs are difficult to infer since loans can receive a wide range 

of RSF weights. To investigate further, we examine the components of loans secured by real estate which increased the most of 

all loan types for small banks and decreased the most of all loan types for large banks. Loans secured by first liens on 

1–4 family residential properties, which receive a maximum RSF factor of 65 if they are performing and unencumbered, 

increased modestly for small and midsize banks and decreased modestly for large banks (Figure 11). Other loans secured by 1–4 

family residential properties, which includes home equity lines of credit and retail mortgages secured by junior liens, declined 

for all size groups but decreased more for midsize and large banks than for small banks. Loans secured by commercial real 

estate—defined in our model as nonfarm nonresidential properties, multifamily (five or more families) residential properties, 

or properties intended for acquisition, development, or construction—increased the most of all loans secured by real estate as 

a percent of assets for small and midsize banks and increased slightly for large banks. Changes in loans secured by farmland 

were modest for each of the three size groups. The changes shown in Figure 11 would decrease the aggregate RIS-NSFR of small 

23 
banks and increase the aggregate RIS-NSFR of large banks, all else equal. The increase in loans secured by real estate for small 

banks was driven by loans secured by commercial real estate, which can receive RSF weights of up to 85 percent. The decrease in 

loans secured by real estate for large banks was driven by loans secured by residential real estate other than first lien mortgages, 

which includes open-end loans secured by residential real estate and loans secured by junior liens on 1–4 family residential 

properties. These loans can also receive RSF weights of up to 85 percent. The trends shown in Figure 11 would slightly decrease 

the aggregate RIS-NSFR of midsize banks, all else equal, since their increase in loans secured by real estate was driven by loans 

secured by commercial real estate. The changes in the composition of loans secured by real estate also help to explain the 

divergent performance of aggregate RIS-NSFRs for small, midsize, and large banks from 2013 to 2019. 

Figure 11 

Commercial Real Estate Drove the Increase in Loans Secured by Real Estate for Small Banks 

Change in Loan Category as a Percent of Total Assets 

March 2013–December 2019 

6 

5.0 

4 3.5 

2 

1.1 0.7 0.7 0.3 0 

0.0 0.0 

-0.7 -0.6 -2 

-2.6 -2.6 -4 

1–4 Family Residential 1–4 Family Residential Commercial Farmland 

Properties; First Liens Properties; Other Real Estate (RSF 50% to 85%) 

(RSF 50% to 65%) (RSF 50% to 85%) (RSF 50% to 85%) 

Small Midsize Large 

Source: FDIC Research Information System. 

Note: RSF is required stable funding. The RSF weights in the column labels apply to securities that are performing and not 

encumbered for more than one year. 

The decline in loans secured by real estate as a percent of assets for large banks does not imply that real estate lending 

is declining among these institutions. Rather, it indicates that the volume of such loans has grown at a slower pace than the 

growth of total assets for those institutions. The volume of loans secured by real estate increased by 6.7 percent for large 

banks between March 2013 and December 2019, while total assets for those institutions increased by 24.2 percent, leading to 

a decrease in loans secured by real estate as a percent of total assets. 

D. RIS-NSFR During the COVID-19 Pandemic 

As Figure 3 shows, the post-crisis aggregate RIS-NSFRs for all three bank size groups sharply increased in the last four 

quarters of our data, representing the four quarters ending on March 31, June 30, September 30, and December 31, 2020. 

Between December 2019 and December 2020, the aggregate RIS-NSFR of small, midsize, and large banks increased by 6, 11, 

and 19 percentage points, respectively. The gains over these four quarters nearly match the gains over the four years following 

the recession that ended in June 2009.38 These aggregate RIS-NSFR gains were contemporaneous with the outbreak of 

COVID-19 in the United States and around the world.39 Because of the pandemic, economic activity slowed as global air travel 

38 As seen in Tables C1 and C3 in Appendix C. 

39 On January 21, 2020, the Centers for Disease Control and Prevention confirmed the first case of COVID-19 in the United States. See the Centers for 

Disease Control and Prevention, “First Travel-Related Case of 2019 Novel Coronavirus Detected in United States,” news release, January 21, 2020, 

https://www.cdc.gov/media/releases/2020/p0121-novel-coronavirus-travel-case.html. 

24 
was restricted,40 public health emergencies were declared,41 and stay-at-home orders were issued.42 The absence of usual 

vehicles for spending, combined with programs enacted by the federal government to alleviate financial hardship resulting 

from the pandemic, contributed to an increase in bank deposits, which increased by nearly $3 trillion dollars between 

December 2019 and December 2020. Deposits increased more for large banks than for midsize or small banks (Figure 12). 

Figure 12 

Domestic Deposits Increased for All Bank Size Groups in 2020 

Domestic Deposits 

($ Trillions) 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 

Large Midsize Small 

Source: FDIC Research Information System. 

Note: Shading denotes recessions (Dec 2007–Jun 2009 and Feb 2020–Apr 2020). 

Dec-1 6 Dec-19 

As shown in Figure 12, domestic deposits increased in 2020 for all three categories of banks, although most of the total 

increase was at large banks. Since domestic retail deposits carry ASF factors of 90 to 100 percent, the increase in domestic 

deposits would carry over to the RIS-NSFR. 

Since the RIS-NSFR is a ratio, an increase in domestic deposits would not raise the RIS-NSFR if banks used those 

additional deposits to accumulate assets with high RSF factors. However, a comparison of the 2020 increases in Figure 6 and 

Figure 8 suggests that much of the influx of domestic deposits has been held as interest-bearing cash balances, which have 

an RSF factor of 0 percent. Cash increased considerably as a percent of assets for all three size groups between December 

2019 and December 2020; from 10.8 percent of assets to 17.1 percent of assets for large banks, from 5.2 percent of assets to 

11.0 percent of assets for midsize banks, and from 7.1 percent of assets to 10.9 percent of assets for small banks. 

Nonperforming assets also increased during the pandemic. As Figure 7 shows, nonperforming assets increased as 

a percent of assets for all three bank size categories in 2020. These increases would decrease RIS-NSFRs, all else equal. 

However, these small increases were overshadowed by the large contemporaneous jumps in cash, deposits, and other RISNSFR-increasing balance sheet items. 

40 See “Proclamation on Suspension of Entry as Immigrants and Nonimmigrants of Persons Who Pose a Risk of Transmitting 2019 Novel Coronavirus,” 

January 31, 2020, https://trumpwhitehouse.archives.gov/presidential-actions/proclamation-suspension-entry-immigrants-nonimmigrants-persons-poserisk-transmitting-2019-novel-coronavirus/. 

41 See Presidential Proclamation 9994, “Declaring a National Emergency Concerning the Novel Coronavirus Disease (COVID–19) Outbreak,” March 13, 2020, 

https://www.govinfo.gov/content/pkg/DCPD-202000156/pdf/DCPD-202000156.pdf. 

42 On March 19, 2020, California became the first state to issue a stay-at-home order. See State of California Executive Order N-33-20, March 19, 2020, 

https://www.gov.ca.gov/wp-content/uploads/2020/03/3.19.20-attested-EO-N-33-20-COVID-19-HEALTH-ORDER.pdf. 

25 
VII. Conclusion 

The 2008 financial crisis and resulting recession were exacerbated by a lack of stable funding. This study has shown that 

low NSFRs, as measured by the RIS-NSFR, correlate with bank failures or use of liquidity assistance programs during the crisis. 

The aggregate RIS-NSFR for large banks increased considerably between 2009 and 2020. Large banks had a lower 

aggregate RIS-NSFR than small and midsize banks in the years leading up to the crisis, and their aggregate RIS-NSFR 

increased more than those of small and midsize banks after the crisis. The rise in the post-recession aggregate RIS-NSFR for 

large banks occurred primarily because of a combination of increases in retail deposits as a percent of assets and increases 

in low-RSF assets such as cash, U.S. Treasury and U.S. government agency securities, and MBS issued or guaranteed by a U.S. 

government agency or GSE. Then, as deposits and cash balances increased during the pandemic, the aggregate RIS-NSFR of 

large banks increased sharply in 2020. 

The aggregate RIS-NSFR for small banks (those that are part of a banking organization with total assets under $10 billion) 

increased between June 2009 and March 2013 but decreased between 2013 and 2019 as these institutions decreased their 

holdings of low-RSF securities as a percent of assets and increased their holdings of loans secured by commercial real estate. 

The aggregate RIS-NSFR for midsize banks also declined slightly from 2013 to 2019 as these banks increased commercial real 

estate loans as a percent of total assets. These declines were reversed in 2020 for small and midsize banks as a result of an 

influx of retail deposits during the pandemic and significant increases in cash as a percent of total assets. 

As of December 2020, large banks had a higher aggregate RIS-NSFR than small and midsize banks. Since these banks 

held more than 60 percent of industry assets as of that date, the industry, on the whole, was more resilient to liquidity stress 

events according to this measure. Virtually all institutions subject to the LCR or NSFR rule—whether directly at the entity level 

or indirectly through application at the parent bank holding company—are large banks, and these regulatory standards likely 

affected large bank decisions regarding their funding sources and asset portfolios. Most small and midsize banks are not 

subject to the LCR or NSFR. 

While different regulatory requirements for these institutions likely explain some of the difference between the aggregate 

RIS-NSFRs of these banks and larger banks, several factors besides regulation may have influenced the portfolio composition 

of large institutions relative to midsize and small institutions, including the post-crisis interest-rate environment, the 

large injection of reserves into the banking system associated with quantitative easing, and the generally more stringent 

supervision of large banks in the aftermath of the crisis. Identifying the contributions of each factor to changes in the 

industry’s risk profile is beyond the scope of this paper, but it is nonetheless reasonable to hypothesize that the LCR final rule 

and the final NSFR rule would have influenced covered banks’ portfolio choices with regard to liquidity and stable funding. 

Based on our results, we are confident in asserting that changes in the aggregate RIS-NSFR reflect changes in the stable 

funding risk profile of banks. In broad terms, liquidity and stable funding standards were intended to reduce risks to covered 

banks and the financial system as a whole, and our results support the contention that they have done so. 

26 
REFERENCES 

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Liquidity Requirements.” 16. 

Basel Committee on Banking Supervision. 2010. “Basel III: International Framework for Liquidity Risk Measurement, 

Standards, and Monitoring.” 

Basel Committee on Banking Supervision. 2014. “Basel III: the Net Stable Funding Ratio.” 

Bologna, Pierluigi. 2015. “Structural Funding and Bank Failures: Does Basel 3 Net Stable Funding Ratio Target the Right 

Problem?” Journal of Financial Services Research 37: 81–113. 

Cohen, Benjamin H., and Michela Scatigna. 2016. “Banks and Capital Requirements: Channels of Adjustment.” Journal of 

Banking and Finance (443): S56–S69. 

DeYoung, Robert, and Karen Jang. 2016. “Do Banks Actively Manage their Liquidity?” Journal of Banking & Finance 66: 143– 

161. 

Federal Deposit Insurance Corporation (FDIC). 2017. “Crisis and Response: An FDIC History, 2008-2013.” Washington, DC, xvi. 

Giordana, Gaston, and Ingmar Schumacher. 2017. “An Empirical Study on the Impact of Basel III Standards on Banks’ Default 

Risk: The Case of Luxembourg.” Journal of Risk and Financial Management 10 (8). 

Heynderickx, Wouter, Jessica Cariboni, and Petracco Giudici. 2016. “Drivers Behind the Changes in European Banks’ Capital 

Ratios: A Descriptive Analysis.” JRC Working Papers in Economics and Finance (1). 

Hoerova, Marie, Caterina Mendicino, Kalin Nikolov, Glenn Schepens, and Skander Van den Heuvel. 2018. “Benefits and Costs 

of Liquidity Regulation.” European Central Bank Working Paper Series 2169 1–67. 

Hong, Han, Jing-Zhi Huang, and Deming Wu. 2014. “The Information Content of Basel III Liquidity Risk Measures.” Journal of 

Financial Stability 91–111. 

Kang, Tae Soo, and Hyun Yeol Shin. 2017. “Loan-to-Deposit Ratio vs. Basel III Net Stable Funding Ratio: Case in Korean Banks.” 

Korea and the World Economy 18 (3): 281–297. 

King, Michael R. 2013. “The Basel III Net Stable Funding Ratio and Bank Net Interest Margins.” Journal of Banking and Finance 

4144–4156. 

Wei, Xu, Yaxian Gong, and Ho-Mou Wu. 2017. “The Impacts of Net Stable Funding Ratio Requirements on Banks’ Choices of 

Debt Maturity.” Journal of Banking and Finance 82: 229–243. 

27 
28 

 ̃a ̃A a × ASFa RIS-NSFR = 

 ̃r ̃R r ×RSFr 

Appendix A. RIS-NSFR Construction 

A. Introduction 

The RIS-NSFR (and NSFR) is defined by the following equation: 

where A = {100,95,90,50,0} is the set of ASF factor weights, R = {100,85,65,50,15,5,0} is the set of RSF factor weights, ASFa is the 

sum of the RIS variables with corresponding factor a, and RSFr is the sum of the RIS variables with corresponding factor r. 

When ASFa and RSFr are measured at the individual bank level, the resulting value is an individual bank’s RIS-NSFR. When ASFa 

and RSFr are measured as sum values for a set of banks, the resulting value is the aggregate RIS-NSFR for that set. 

As discussed in Section IV, RIS-NSFR is intended to estimate the NSFR as it was defined in the final NSFR rule, using data 

from the FDIC’s RIS data warehouse. Challenges in implementing this approach include 

1. Insufficient data on the maturity and encumbrance characteristics of assets and liabilities (for example, 

having data on maturities less than one year, but not on whether the maturities are from zero to six months 

or six months to one year) 

2. Discontinuities in data series because of changes to bank reporting requirements 

3. Double-counting when certain reported items are included within others 

To overcome these challenges, our model uses proxies from available data when exact data on NSFR components do 

not exist in RIS. Where RIS does not provide granular data on various liquidity characteristics within an NSFR component (for 

example, securities with various maturities) the model apportions component-level data to subcomponents equally. In the 

rest of this appendix, we discuss these proxies and apportionments for each NSFR category. 

B. ASF Factors Framework43 

ASF is the weighted average of all of a bank’s funding sources, including equity, deposits, and borrowings. These weights, 

called ASF factors, range from 0 (least stable funding) to 100 (most stable funding). 

1. ASF100 Factors 

The final NSFR rule assigns a 100 percent ASF factor (ASF100) to regulatory capital elements and long-term liabilities. The 

RIS data warehouse does not contain complete data for all banks on these items before 2015 because of disparate reporting 

requirements. As a proxy for regulatory capital elements, our model uses total equity capital on a consolidated basis (EQTOT). 

To proxy for long-term liabilities, we use the sum of other borrowings (OTHBOT) and subordinated notes and debentures 

(SUBND). 

Included in these last two variables are some liabilities with maturities of less than one year and thus are assigned ASF 

factors of less than 100 percent. We remove these short-term liabilities from the 100 percent ASF category by subtracting 

reported values for subordinated notes and debentures with a remaining maturity of one year or less (SUBND1L) and Federal 

Home Loan Bank advances and other borrowings with remaining maturity of one year or less (OTHBFH1L+OTHBOT1L). 

43 See 81 Fed. Reg. at 35135–35139. 
29 

2. ASF95 Factors 

The final NSFR rule assigns a 95 percent ASF factor (ASF95) to stable retail deposits, which are not identified separately 

on the Call Report or in RIS.44 To proxy for stable retail deposits, our model uses insured transaction deposits of individuals, 

partnerships, and corporations including certified and official checks held in domestic offices (TRNIPCOC). However, since 

these values include some retail deposits that are uninsured and are therefore not defined as “stable” retail deposits in the 

final NSFR rule, we include half the value of these variables in ASF95 and half in ASF90. This split should not change RIS-NSFR 

significantly, however, since the net effect of mischaracterizing an ASF90 deposit under ASF95 is only 5 percent of the value of 

the deposit. 

Our model also considers as stable retail deposits a proportion of insured time deposits held in domestic offices 

(NTRTIME - NTRTMLGJ). The proportion is equal to the fraction of retail nontransaction deposits (NTRIPC) in total 

nontransactional deposits (NTR) and represents our estimate of the fraction of time deposits from retail customers. 

The final NSFR rule treats long-term brokered deposits differently from other long-term liabilities, assigning them 

to ASF90. To account for this treatment, we subtract total brokered deposits with maturities longer than one year (BRO– 

BROINS1Y–BROLG1YR) from ASF95 and assign them to other categories. If brokered deposits with maturities longer than 

one year contain deposits with a 95 percent ASF factor, this adjustment may underestimate the value of ASF95 and hence 

underestimate the RIS-NSFR. Brokered deposits that mature in one year or less, and that are fully insured and from retail 

customers, are included within TRNIPCOC and NTRTIME and are therefore assigned an ASF factor of 95 percent in our model. 

3. ASF90 Factors 

The final NSFR rule assigns a 90 percent ASF factor (ASF90) to other retail deposits and fully insured affiliate, reciprocal, 

and certain longer-term retail brokered deposits. None of these items are identified on the Call Report or in RIS. To proxy for 

other retail deposits, our model uses the sum of the value of savings deposits, including money market deposit accounts 

(MMDA) held in domestic offices (NTRSMMDA), all other savings deposits (excluding MMDA) held in domestic offices 

(NTRSOTH), total time deposits above the FDIC deposit insurance limit (NTRMLGJ), and a percentage of total foreign deposits 

(DEPFOR) equal to the share of NTRSMMDA, NTRSOTH, and NTRMLGJ in total domestic deposits (DEPDOM). These amounts 

are then multiplied by the fraction (NTRIPC/NTR) to represent our estimate of the fraction of these deposits from retail 

customers. The amounts of DEPFOR are also reduced by 50 percent to reflect the consolidation rules under 12 CFR 329.109. 

Half the value of TRNIPCOC is included in ASF90 to account for uninsured retail funding. 

To proxy for fully insured affiliate, reciprocal, and certain longer-term retail brokered deposits, our model uses total 

insured brokered deposits with maturities longer than one year (BROINS – BROINS1Y), which is subtracted from ASF95. 

Insured brokered deposits from retail customers that mature in one year or less are included as components of NTRSMMDA, 

NTRSOTH, and NTRMLGJ and therefore receive an ASF factor of 90 in our model. 

4. ASF50 Factors 

The final NSFR rule assigns a 50 percent ASF factor (ASF50) generally to types of funding that mature between six months 

and one year, including 

• Unsecured wholesale funding provided by, and secured funding transactions with, a counterparty that is 

not a financial sector entity or central bank and with remaining maturity of less than one year 

• Unsecured wholesale funding provided by, and secured funding transactions with, a financial sector entity 

or central bank with remaining maturity of six months or more but less than one year 

44 As defined by the LCR rule. See 79 Fed. Reg. at 61480.
30 

• Securities issued by a covered company with remaining maturity of six months or more but less than one 

year 

• Operational deposits 

• Other retail brokered deposits 

• All other liabilities with remaining maturity of six months or more but less than one year 

To proxy for those funding sources, our model uses the sum of all funding that matures in less than one year 

(subordinated debt (SUBND1L) and other borrowing (OTHBFH1L+OTHBOT1L)). However, since RIS does not distinguish 

between borrowings with remaining maturities of less than six months and borrowings with remaining maturities between 

six months and a year, the RIS-NSFR assigns half this value to ASF50 (assumed to mature in six months to one year) and the 

remainder to ASF0 (assumed to mature in less than six months). 

To proxy for operational deposits, our model uses the sum of transactional deposits held in domestic offices of the U.S. 

government (TRNUSGOV), foreign governments (TRNFG), states and political subdivisions in the United States (TRNMUNI), 

commercial banks and other depository institutions in the United States (TRNCBO), and commercial banks and other 

depository institutions in foreign countries (TRNFC). To account for similar deposits held in foreign offices, we apportion a 

share of total deposits in foreign offices (DEPFOR) equal to the share of (TRNUSGOV+TRNFG+TRNMUNI+TRNCBO+TRNFC) in 

total domestic deposits (DEPDOM) to ASF50. 

As discussed in the ASF95 and ASF90 sections above, a portion, equal to NTRIPC/NTR, of NTRTIME, NTRSMMDA, and 

NTRSOTH are assumed to be from retail customers and given appropriate ASF weights of 95 or 90. The balance of those 

variables are assumed to be from non-retail customers and included in ASF50. 

Finally, in addition to the short-term liabilities described above, the final NSFR rule assigns a 50 percent ASF factor 

to uninsured brokered deposits of any maturity. To account for these liabilities, we add longer-term uninsured brokered 

deposits (BRONINS – BROLG1YR) to ASF50. BROLG1YR is subtracted from BRONINS because uninsured brokered deposits 

from non-retail customers are included as components of transaction accounts and nontransaction accounts of non-retail 

customers, and are therefore included in the variables listed above and receive an ASF factor of 50 in our model. 

5. ASF0 Factors 

The final NSFR rule assigns a 0 percent ASF factor (ASF0) to 

• Trade date payables 

• Certain short-term retail brokered deposits 

• Certain short-term funding from financial sector entities 

• Any other NSFR liability that matures in less than six months 

• Derivatives liability amount 

We include in ASF0 half of SUBND1L+OTHBFH1L+ OTHBOT1L to estimate securities issued and other borrowing with 

remaining maturities of less than six months. We also include federal funds purchased and securities sold under agreements 

to repurchase on a consolidated basis to estimate short-term funding from financial sector entities (FREPP). 

A bank’s NSFR derivatives liability amount is the greater of zero and the difference between the bank’s derivatives liability 

amount and derivatives assets amount. We proxy the derivatives asset amount with the reported amount of derivatives with 

a positive fair value (TRREVAL) and the derivatives liability amount with the reported value of derivatives with a negative fair 

value (TRLREVAL). 
31 

To estimate any other NSFR liability that matures in less than six months, we use trading liabilities (TRADEL) and other 

liabilities (OL). 

6. Validation of ASF Coverage 

To ensure that the ASF factors in the RIS-NSFR include all necessary elements across time, we summed all RIS 

components that were assigned each ASF factor and compared that sum to the reported total liabilities and equity capital 

line item (LIABEQ). For every quarterly Call Report since 2002, we accounted for more than 97 percent of industry LIABEQ, with 

an average coverage rate of more than 99.3 percent. 

C. RSF Factor Framework45 

RSF is the weighted average of all of a bank’s assets, with higher weights assigned to assets that have longer maturity, are 

encumbered, are not fully performing, or are otherwise less readily converted into cash. RSF factors range from 0 (most liquid) 

to 100 (least liquid). 

1. RSF0 Factors 

The final NSFR rule assigns a 0 percent RSF factor (RSF0) to unencumbered currency and coin, cash items in the process 

of collection, Federal Reserve Bank balances, claims on a Federal Reserve Bank or foreign central bank that mature in less 

than six months, and trade date receivables. As a proxy for these variables, our model uses cash and cash balances (CHBALI 

+CHBALNI) that correspond to Call Report Schedule RC-A and align with the definitions used in the final NSFR rule. 

In addition, the final NSFR rule assigns a 0 percent RSF factor to unencumbered Level 1 liquid assets, which includes 

securities issued or guaranteed by the U.S. Department of Treasury and certain liquid and readily marketable securities. To 

proxy for those assets, our model uses values reported on Call Report Schedule RC-B that meet these definitions along with 

the corresponding trading assets on Schedule RC-D (listed in Table 4). The corresponding RIS variables include U.S. Treasury 

securities (SCUSTHA+SCUSTAF+TRSCUSTR) and U.S. government agency obligations (SCAOTHA+SCAOTAF+TRSCUSAG).46 Our 

model also assigns a 0 percent RSF factor to the value of federal funds sold (FFSOLD). The Call Report instructions state that 

assets can only be reported as federal funds sold if they mature within one business day, so we consider these items to be 

equivalent to cash, trade date receivables, and Level 1 liquid assets from a liquidity standpoint. 

Finally, the final NSFR rule assigns to RSF0 the amount of unencumbered secured lending transactions with a financial 

sector entity or a subsidiary thereof that mature within six months and are secured by rehyphothecatable Level 1 liquid 

assets. To proxy for those assets, our model uses one-third of the value of securities purchased under a resale agreement 

(REPOSOLD). RIS does not identify maturity data of REPOSOLD, so we assume that one-third of the value of REPOSOLD 

represents assets with remaining maturities less than six months and that are secured by rehypothecatable Level 1 liquid 

assets, one-third have remaining maturities less than six months and are secured by collateral other than rehypothecatable 

Level 1 liquid assets, and one-third have remaining maturities of more than six months but less than one year. 

2. RSF5 Factors 

The final NSFR rule assigns a 5 percent RSF factor (RSF5) to the credit and liquidity facilities that a covered company 

provides to its customers and counterparties. To proxy for these values, we use off-balance sheet commitments reported 

on Call Report Schedule RC-L including unused commitments for commercial real estate loans secured by real estate 

45 See 81 Fed. Reg. at 35141–35147. 

46 SCAOTAF and SCAOTHA are not populated variables for any banks between the June 2018 Call Report and the December 2020 Call Report. However, the 

effect of this discontinuity on the results of our model is likely minimal since the combined values of SCAOTAF and SCAOTHA account for a maximum of 0.2 

percent of assets and an average of 0.13 percent of assets among all banks between March 2002 and March 2018. 
32 

(UCCOMRES); commercial real estate loans not secured by real estate (UCCOMREU); credit card lines (UCCRCD); revolving, 

open-end lines of credit secured by 1–4 family residential properties (UCLOC); securities underwriting (UCSC); and all other 

purposes (UCOTHER). Also included in this category of off-balance sheet commitments are financial standby letters of credit 

and foreign office guarantees (LOCFSB), performance standby letters of credit and foreign office guarantees (LOCPSB), and 

commercial letters of credit (LOCCOM). 

3. RSF10 Factors 

Although the 2016 NPR assigned a 10 percent RSF factor to certain assets, the final NSFR rule does not assign a 10 

percent RSF factor to any assets. 

4. RSF15 Factors 

The final NSFR rule assigns a 15 percent RSF factor (RSF15) to level 2A liquid assets as set forth in section 20(b) of the LCR 

final rule without considering the requirements in section 22 or the level 2 cap in section 21.47 This category includes certain 

liquid and readily marketable obligations issued or guaranteed by a U.S. GSE and certain obligations issued or guaranteed by 

a sovereign entity or a multilateral development bank.48 As a proxy for these assets, our model uses the following RIS variables 

in RSF15 as indicated. 

RSF15 includes one-third of the value of securities purchased with a repurchase agreement (REPOSOLD), representing the 

portion of securities purchased with a repurchase agreement that mature in less than six months but are secured by collateral 

other than rehypothecatable Level 1 liquid assets. The remaining two-thirds of REPOSOLD is assigned equally between RSF0 

and RSF50.

49 

RSF15 also includes other loans to financial sector entities with maturities of less than one year, including loans and 

acceptances to U.S. branches and agencies of foreign banks (LNDEPAUS), loans to depository institutions or commercial 

banks (LNDEPCOM), loans to other depository institutions (LNDEPUS), loans to foreign branches of other U.S. banks 

(LNDEPFUS), loans to other banks in foreign countries (LNDEPFOT), and loans to nondepository financial institutions 

(LNOTHER). Because these RIS variables also contain assets with maturities longer than a year, we account for the ambiguity 

by multiplying each variable by the share of loans and leases with maturities less than one year (LNLSLES1) in total loans and 

leases (LNLS) and apportioning half of that value to RSF5050 and the other half to RSF15.

51 Note that this allocation method 

assigns equal weight to each factor because the authors do not have a priori beliefs about the distribution of maturities for 

these assets. 

RSF15 also contains various securities such as U.S. government agency and corporation obligations excluding MBS 

(SCASPNHA+SCASPNAF),52 residential MBS issued by GSEs (SCFMNHA+SCFMNAF), residential collateralized mortgage 

obligations (CMOs) and real estate mortgage investment conduits (REMICs) issued or guaranteed by a U.S. government 

agency or GSE, or collateralized by loans or securities issued or guaranteed by a U.S. government agency or GSE 

(SCCOLHA+SCCOLAF+TRSCMB), commercial MBS issued or guaranteed by GSEs (SCCPTGHA+SCCPTGAF+TRSCCMBG), 

47 See 79 Fed. Reg. at 61529–61530. 

48 See 86 Fed. Reg. at 9158–9159. 

49 See discussion of RSF0. 

50 Representing maturities of 6 to 12 months. 

51 Representing maturities of less than 6 months. 

52 Some of the securities includes in SCASPNHA and SCASPNAF are likely U.S. government agency or corporation bonds that carry a guarantee of interest and 

principal payment that is backed by the full faith and credit of the U.S. Treasury, and therefore should be assigned an ASF factor of 0 rather than 15. However, 

the effect of this possible mischaracterization of assets on the results of our model is likely minimal since the combined values of SCASPNAF and SCAFPNHA 

amount to a maximum of 3.58 percent of assets, and an average of 1.85 percent of assets, from March 2002 through December 2020. 
33 

residential MBS guaranteed by GNMA (SCGNMHA+SCGNMAF+TRSCPAS), and other CMOs and REMICs guaranteed by GSEs 

(SCCMOGHA+SCCMOGAF). 

We also assign to RSF15 a portion of other traded assets (TRLNOTH) equal to half of the proportion of loans and leases 

with less than one year of remaining maturity (LNLSLES1) in total loans and leases (LNLS). 

5. RSF50 Factors 

The final NSFR rule assigns a 50 percent RSF factor to unencumbered Level 2B liquid assets, secured lending transactions 

with a financial sector entity or a subsidiary thereof or a central bank that mature in six months of more but less than one 

year, operational deposits held at financial sector entities, secured lending transactions and unsecured wholesale lending to 

counterparties that are not financial sector entities and are not central banks and that mature in less than one year, lending 

to retail customers and counterparties that matures in less than one year, and all other assets that mature in less than one 

year.53 To proxy for these RSF50 assets, our model includes the following RIS variables: 

One-third of the value of REPOSOLD is assigned to RSF50 to account for repurchase agreements sold with maturities of 

between six months and one year.54 

RSF50 contains all loans and leases with remaining maturity less than a year that are not already assigned a lower 

factor weight. As a proxy for this variable, we use the value of loans and leases with less than one year of remaining maturity 

(LNLSLES1), except those that were assigned to RSF15. 

Level 2B liquid assets include equity securities that are part of a major index and investment-grade corporate and 

municipal obligations. Municipal obligations are included in the RIS variables SCMUNIHA, SCMUNIAF, and TRSCMUNI. RIS 

does not distinguish between municipal obligations that do and do not meet the criteria to be “investment grade.” As such, 

we assume half the value of these accounts represents investment-grade municipal obligations and assign them to RSF50.

55 

We assume half the values of domestic debt securities (SCODOTAF+SCODOTHA) and foreign debt securities 

(SCFORDAF+SCFORDHA) represent the investment-grade portion of these securities and assign those to RSF50.

56 

We assign to RSF50 a portion of other traded assets (TRLNOTH) equal to half of the proportion of loans and leases with 

less than one year of remaining maturity (LNLSLES1) in total loans and leases (LNLS).57 

Finally, we assume half of the value of equity securities (SCEQMV+SCEQFV) represent securities that are part of a major 

stock market index and assign those to RSF50.

58 

6. RSF65 Factors 

The final NSFR rule assigns a 65 percent RSF factor (RSF65) to retail mortgages that mature in one year or more and are 

assigned a risk weight of no greater than 50 percent.59 As a proxy for this value, our model includes closed-end loans secured 

53 See 86 Fed. Reg. at 9159–9162. 

54 See discussion in RSF0. 

55 The other half is assigned to RSF100. 

56 The other half is assigned to RSF85. 

57 By assigning a portion of TRLNOTH to RSF15, we are assuming that all loans included in TRLNOTH are loans to financial sector entities. TRLNOTH potentially

includes some loan types that are not eligible for a 15 percent RSF factor, including loans to foreign governments and loans to states and political subdivisions 

in the United States, so this methodology understates RSF and, therefore, overstates the NSFR, to the extent that our model assigns a 15 percent RSF factor to 

assets that should be assigned a higher RSF factor. Call Report forms 031 and 041 require institutions to report loans secured by real estate held for trading, 

consumer loans held for trading, and commercial and industrial loans held for trading on form RC-D; all other loans held for trading are included in TRLNOTH. 

We expect that this assumption has a minimal effect on the quality of our model because loans to financial institutions make up the majority of loans reported

on Schedule RC-C that could potentially be included in TRLNOTH, so they likely make up the majority of loans held for trading that could be included in 

TRLNOTH. Between March 2002 and December 2020, loans to financial institutions represented a minimum of 80.8 percent of all loans that are potentially

included in TRLNOTH and an average of 86.0 percent of such loans. Further, TRLNOTH comprises a very small percentage of total assets between March 2002 

and December 2020: the ratio of TRNLOTH to total assets had a maximum value of 0.15 percent, and an average value of 0.08 percent, over this period. 

58 The other half is assigned to RSF85. 

59 See 86 Fed. Reg. at 9162. 
34 

by 1–4 family residential properties held in trading accounts (TRLNRR1) and all other closed-end loans secured by first liens 

on 1–4 family residential properties (LNRERSFC). Following our method to apportion loans and leases to different degrees of 

maturity,60 we remove from RSF65 a share of LNRERSFC equal to the share of LNLSLES1 in LNLS. 

The final NSFR rule also assigns to RSF65 secured lending transactions, unsecured wholesale lending, and lending to 

retail customers and counterparties that mature in one year or more and are assigned a risk weight of no more than 20 

percent. As a proxy for this value, we include municipal loans (LNMUNI), loans to foreign governments (LNFG), loans to 

farmers (LNAG), loans to consumers (LNCON), loans secured by farmland (LNREAG), commercial real estate loans (LNRECNOC 

+ LNRENFC + LNREMULC + LNRENOWC + LNRENOTC), and commercial and industrial loans (LNCI). To account for the 

risk weight criterion, we apportion these variables using the share of total balance sheet assets assigned a risk weight of 

0 percent, 2 percent, 4 percent, or 20 percent, as reported on Call Report Schedule RC-R.61 To account for the maturity 

criterion, we remove from RSF65 a share of LNMUNI + LNFG + LNAG + LNCON + LNAG + LNRECNOC + LNRENFC+ LNREMULC + 

LNRENOWC + LNRENOTC + LNCI equal to the share of LNLSLES1 in LNLS. 

7. RSF85 Factors 

The final NSFR rule assigns an 85 percent RSF factor to retail mortgages that mature in one year or more and are 

assigned a risk weight of greater than 50 percent.62 To proxy for this value, we include in RSF85 other loans secured by 1–4 

family residential properties including open-end loans and loans secured by junior liens on 1–4 family residential properties 

(LNRELOCC+TRLNRLC+LNRERS2C+TRLNRR2). Following our method to apportion loans and leases to different degrees of 

maturity,63 we remove a share of these loans equal to the share of LNLSLES1 in LNLS. 

RSF85 also contains the balance of municipal loans (LNMUNI), loans to foreign governments (LNFG), loans to farmers 

(LNAG), loans to consumers (LNCON), loans secured by farmland (LNREAG), commercial real estate loans (LNRECNOC + 

LNRENFC+LNREMULC+LNRENOWC+LNRENOTC), and commercial and industrial loans (LNCI) not assigned to RSF65 or RSF50 

based on risk-weights and/or maturities. Following our method to apportion loans and leases to different degrees of maturity, 

we remove a share of these loans equal to the share of LNLSLES1 in LNLS.64 

As discussed in the section on RSF50, we also include in RSF85 half the values of domestic debt securities 

(SCODOTAF+SCODOTHA) and foreign debt securities (SCFORDAF+SCFORDHA) to represent the portion of these securities 

that does not meet the criteria to be considered investment grade. Similarly, RSF85 contains half the value of equity securities 

(SCEQMV+SCEQFV) representing securities that are not part of a major stock market index, with the remaining half assigned 

to RSF50. 

RSF85 also contains asset-backed securities and structured financial products, private-label residential and commercial 

MBS, and CMOs and REMICs not issued by or guaranteed by a U.S. government agency or GSE and not collateralized by 

securities or loans issued or guaranteed by a U.S. government agency or GSE: SCABSAF+SCABSHA+SCCMOPAF+SCCMOPHA+ 

SCCPTPAF+SCCPTPHA+SCODPCAF+SCODPCHA+SCOPICAF+SCOPICHA+SCOPIOAF+SCOPIOHA+SCSFPAF+ SCSFPHA+ 

TRSCCMBP+TRSCOTDB+TRSCOTMB. 

60 See discussion on RSF15 and RSF50. 

61 The remaining value is included in RSF0. 

62 See 86 Fed. Reg. at 9163. 

63 See discussion on RSF15 and RSF50. 

64 See discussion on RSF15 and RSF50. 
35 

8. RSF100 Factors 

The final NSFR rule assigns all other assets to the RSF100 category.65 This category includes loans to financial institutions 

that mature in one year or more. To proxy for these loans, we use the value of loans to nondepository financial institutions 

and other loans (LNOTHER), other loans in trading accounts (TRLNOTH), loans and acceptances to U.S. branches and 

agencies of foreign banks (LNDEPAUS), loans to domestic commercial banks (LNDEPCOM), loans to other depository 

institutions (LNDEPUS), loans to foreign branches of U.S. banks (LNDEPFUS), and loans to other banks in foreign countries 

(LNDEPFOT). Following our method to apportion loans and leases to different degrees of maturity,66 we remove a share of 

these loans equal to the share of LNLSLES1 in LNLS. 

RSF100 also includes municipal obligations that do not meet the criteria to be considered investment grade 

(SCMUNIHA+SCMUNIAF+TRSCMUNI). Our model assigns half the value of these accounts to RSF100.

67 

Our model also assigns to RSF100 all remaining assets such as premises and fixed assets (BKPREM), other real estate 

owned (ORE), investments in subsidiaries (INVSUB), investments in real estate ventures (INVSUORE), and other assets (OA). 

Finally, the final NSFR rule contains a complex treatment for derivatives, including for initial margin and variation 

margin provided to and received from derivatives counterparties,68 which are not reported on the Call Report and therefore 

do not correspond to any RIS variables. The rule requires covered companies to calculate their net derivatives liability or 

asset amount by subtracting total derivatives liabilities from total derivatives assets, and assigning the amount to RSF100 

if the amount is positive or to ASF0 if the amount is negative.69 The net derivatives asset or liability calculation takes into 

account variation margin, so we cannot calculate the exact amount using RIS variables. However, we proxy for this value by 

subtracting reported derivatives with a negative fair value (TRLREVAL) from the reported amount of derivatives with a positive 

fair value (TRREVAL). The final NSFR rule also includes in RSF100 an amount equal to “5 percent of the sum of the covered 

company’s gross derivatives liabilities, when calculated as if no variation margin had been exchanged and no settlement 

payments had been made based on changes in the values of the derivative transactions.”70 As a proxy for this amount, our 

model includes 5 percent of the total value of derivatives with a negative fair value held for trading (TRLREVAL) in RSF100. 

9. Validation of RSF Coverage 

To check the coverage of our RSF factors across Call Report form type and time, we summed all RIS components 

discussed in this section, other than off-balance sheet commitments (unused loan commitments and letters of credit), and 

compared that sum to the reported total assets line item (ASSET). For every quarterly Call Report since 2002, we accounted 

for more than 96 percent of industry ASSET, with an average coverage rate of more than 99.8 percent. 

D. Additional Adjustments 

Our methodology reflects our interpretation of the final NSFR rule’s treatment of unencumbered, performing assets. 

However, we also account for the final NSFR rule’s treatment of encumbered and/or nonperforming assets. In addition, we 

account for unearned income on loans and some data discrepancies. 

65 See 86 Fed. Reg. at 9165. 

66 See discussion on RSF15 and RSF50. 

67 See discussion on RSF50. 

68 See 86 Fed. Reg. at 9171–9183. 

69 See 86 Fed. Reg. at 9172–9173. 

70 See 86 Fed. Reg. at 9182. 
36 

1. Past-Due and Nonaccrual Assets 

The final NSFR rule assigns a 100 percent RSF factor to loans and securities 90 days or more past-due or in non-accrual 

status (i.e., nonperforming). RIS contains information on the amount of each loan type that is nonperforming. For example, 

RIS contains data on the volume of agricultural loans past due by 90 days or more (P9AG) or in nonaccrual status (NAAG). 

The amount of all loan types 90 days or more past due or in nonaccrual status are reported on the Call Report, enabling our 

model to apportion the total amount of past-due and nonaccrual loans between RSF categories with greater precision. For 

each RSF category, our model subtracts the nonperforming portion of loans and assigns them to RSF100. 

However, RIS does not contain granular data on nonperforming securities by type. Therefore, our model apportions the 

total amounts of securities 90 days or more past due (P9SCDEBT) and nonaccruing securities (NASCDEBT) proportional to 

the share of securities in each RSF category to total securities. The shares of securities that are nonperforming in each RSF 

category are then reassigned to RSF100. 

2. Pledged and Encumbered Securities 

The final NSFR rule provides that loans and securities pledged as collateral or otherwise encumbered be assigned to 

different RSF categories based on the length of the encumbrance. While RIS reports the amount of pledged loans (LNPLEDGE) 

and securities (SCPLEDGE), RIS has no data on the amount to time for which the loan or security is pledged as collateral, the 

distribution of such pledged assets, or other encumbrances. 

To incorporate pledged assets into our model, we first apportion the total amount of pledged loans/securities according 

to the share of performing loans/securities in each RSF category. For example, if 37.2 percent of a bank’s performing loans are 

in RSF85, then the model apportions 37.2 percent of pledged loans to RSF85. Next, our model assigns one-third of the share 

of pledged assets in each RSF category to each of the three pledge durations: (1) zero to six months, (2) six months to a year, 

and (3) more than one year.71 Assets pledged for more than one year are assigned to RSF100. Assets pledged for less than six 

months are treated as unencumbered and remain in their original RSF category. Assets pledged for between six months and 

one year are assigned to the greater of RSF50 and the category to which they would be assigned if they were unencumbered. 

3. Unearned Income 

Banks report the total amount of any unearned income (UNINC) included in the values of the various types of loans and 

leases reported on Call Report Schedule RC-C. This amount is subtracted from the aggregate reported value of all loan and 

lease types reported on Call Report Schedule RC-C to derive the balance sheet value of total loans and leases held for sale or 

investment, as reported on Call Report Schedule RC. However, this amount is reported as a total across all loans and leases 

and not by loan and lease category. To account for the unearned income in each loan category, we apportion the unearned 

income across all performing loans by each loan category’s share in total performing loans. These apportioned unearned 

income amounts are subtracted from each performing loan category. Since an institution’s total assets are calculated based 

on the amount of loans and leases after subtracting unearned income, unearned income is not included in any RSF category. 

4. Allowance for Loan and Lease Losses 

Banks report on Call Report Schedule RC their allowance for losses on loans and leases (LNLSRES). This amount is reported 

as a total amount across all loans, rather than by loan category. To account for these allowances for losses, we apportion LNLSRES 

across all nonperforming loans by their share in total nonperforming loans. These apportioned amounts are subtracted from 

each nonperforming loan category. Total reported assets are reduced by the amount of the allowance for loan and lease losses to 

calculate total assets for each institution. Since this amount is not included in total assets, it is not included in any RSF category. 

71 The authors have no a priori beliefs on the distribution of pledged assets and thus split the share evenly across the three durations. 
37 

E. Legacy Variables 

In numerous cases, the information required on Call Reports has changed. For most cases, RIS attempts to use the same 

RIS variable to represent both the current Call Report line item and the legacy line item. In some cases, however, RIS does 

not continue reporting that line item. In such instances, our model uses the legacy variables up to the point where the data 

changed and then the new variables from that point forward. For example, banks have reported their amount of derivatives 

with a positive fair value (TRREVAL) on a consolidated basis only since 2008. To track this variable for periods before 2008, 

we use bank holdings of derivatives in domestic (TRREVALD) and foreign (TRREVALF) offices. While this treatment may create 

data discontinuities, it allows us to track similar assets across the entire sample period. The coverage of RSF categories is 

close to 100 percent of assets for the entire sample period, supporting our belief that the effect of these discontinuities on the 

estimated NSFR calculations is small. 

F. Comparison of RIS-NSFR to Other NSFR Proxies 

As discussed in Section IV, the RIS-NSFR differs from the NSFR proxies developed by King (2013), Hong et al. (2014), 

DeYoung and Jang (2016), and Hoerova et al. (2018) in several respects, including the database used to develop the proxy 

(King [2013] used Bankscope data, Hoerova et al. [2018] used the ECB’s Individual Balance Sheet Items database, and Hong 

et al. [2014] and DeYoung and Jang [2016] used FFIEC Call Report data, which is the same data source on which we base the 

RIS-NSFR) and the definition of the NSFR used to develop the proxy (King [2013] used the BCBS [2010] definition, Hong et al. 

[2014] used both the BCBS [2010] and the BCBS [2014] definitions, DeYoung and Jang [2016] and Hoerova et al. [2018] used 

the BCBS [2014] definition, and the RIS-NSFR uses the NSFR as defined in the final NSFR rule). No public data source contains 

the information that a researcher would need to perfectly proxy the NSFR, so assumptions need to be made about certain 

characteristics of publicly available balance sheet items including the distribution of retail versus wholesale customers, the 

maturities of certain assets and liabilities, and the encumbrance of assets. Below we discuss key differences between the 

assumptions used to develop the RIS-NSFR and assumptions used to develop other NSFR proxies. 

1. Distinguishing “Stable” Deposits From “Less Stable” Deposits 

The final NSFR rule in the United States as well as BCBS (2010) and BCBS (2014) assign higher ASF factors to “stable” 

deposits than to “less stable” deposits. “Stable” deposits are defined as those that are covered by an effective deposit 

insurance scheme or a public guarantee that provides equivalent protection, the depositors have an established relationship 

with the bank, and/or the deposits are held in transactional accounts. Public data sources do not provide sufficiently granular 

data on deposits to allow researchers to unambiguously distinguish between stable and less stable deposits, so researchers 

must make assumptions to determine which deposits should and should not be considered stable for the purposes of 

generating an NSFR proxy. 

As described in section B of Appendix A, to develop the RIS-NSFR, we assume that half of transaction accounts of 

individuals, partnerships, and corporations (RIS variable TRNIPCOC) are fully covered by deposit insurance and therefore 

meet the definition of stable retail deposits. Implicit in this approach is the assumption that all deposits in this category 

belong to customers with an established relationship with the bank. 

King (2013) assumes that 70 percent of all deposits meet the criteria of stable deposits. 

Hong et al. (2014) assume that 20, 50, or 80 percent of transaction accounts of individuals, partnerships, and corporations 

belong to customers with an established relationship with the bank in their pessimistic, baseline, and optimistic scenarios, 

respectively. They estimate the portion of that value covered by deposit insurance by multiplying it by the ratio of total 

deposits to total assets. 
38 

DeYoung and Jang (2016) assume that 100 percent of transaction accounts of individuals, partnerships, and 

corporations, as well as 100 percent of the value of non-MMDA savings deposits and non-brokered time deposits with values 

under $250,000 that mature in less than one year, meet the definition of stable deposits. 

Hoerova et al. (2018) assume that all household deposits, other than those that mature overnight, meet the definition of 

stable deposits. 

2. Estimating the Maturity of Funding Sources 

The final NSFR rule treats borrowing that matures in six months to one year as more stable than borrowing that matures 

in less than six months. The Call Report distinguishes between borrowing that matures in less than one year and borrowing 

that matures in more than one year, but not between borrowing that does and does not mature in six months or more. 

As described in section B of Appendix A, in constructing the RIS-NSFR we assume that half of all borrowing that matures 

in less than one year matures in less than six months, and the other half matures in six months to one year. 

King (2013) assumes that half of all borrowing matures in less than one year, and the other half matures in more than one 

year. King creates his NSFR proxy based on the BCBS (2010) definition, which assigns a 50 percent ASF factor to all funding 

that matures in less than one year and does not treat funding that matures in less than six months differently from funding 

that matures in six months to one year. Here, the difference between the NSFR proxy developed by King (2013) differs from the 

RIS-NSFR because the two proxies are based on different definitions of the NSFR. 

Hong et al. (2014), DeYoung and Jang (2016), and Hoerova et al. (2018) assign a 50 percent ASF factor to borrowing that 

matures in less than one year and assign a 100 percent ASF factor to borrowing that matures in more than one year. They 

make no assumptions about the distribution of maturities among funding sources that mature in less than one year. Like 

King (2013), these researchers did not estimate the portion of funding that matures in less than six months. However, unlike 

King (2013), these researchers used data sources that identified funding that matures in less than one year, while King (2013) 

estimated the portion of borrowing that matures in less than one year. Further, these researchers based their proxies on the 

BCBS (2014) definition, which assigns an ASF factor of 0 percent to liabilities that mature in less than six months and an ASF 

factor of 50 percent to liabilities that mature in six months to one year, whereas King (2013) bases his proxy on the BCBS 

(2010) definition which does not make such a distinction. 

3. Treatment of Time Deposits 

We did not consider the maturity of deposits in developing the RIS-NSFR, because the final NSFR rule does not make 

a distinction between time deposits and other deposits. Under the final NSFR rule, deposits (including time deposits) that 

belong to retail customers receive a 95 or 90 percent ASF factor depending on whether they are fully covered by deposit 

insurance, and those that belong to wholesale customers receive a 50 percent ASF factor. 

King (2013) does not distinguish between time deposits and other deposits in developing his NSFR proxy. He assigns a 90 

percent ASF factor to 70 percent of all deposits (assumed to meet the definition of “stable” deposits) and an 80 percent ASF 

factor to the remaining 30 percent of deposits. 

Hong et al. (2014) assign a 100 percent ASF factor to all time deposits that mature in more than one year. Time deposits 

that mature in less than one year receive an ASF factor of 95 percent if they are “small,” meaning fully covered by deposit 

insurance, and they receive an ASF factor of 50 otherwise. 

DeYoung and Jang (2016), like Hong et al. (2014), assign a 100 percent ASF factor to all time deposits that mature in more 

than one year. Non-brokered time deposits that mature in less than one year and are fully covered by deposit insurance 

receive a 95 percent ASF factor, while those not fully covered by deposit insurance receive a 90 percent ASF factor. Brokered 

time deposits that mature in less than one year receive a 50 percent ASF factor. 
39 

Hoerova et al. (2018) assign a 100 percent ASF factor to time deposits that mature in more than one year. They assign a 95 

percent ASF factor to time deposits from households that mature in less than one year, and a 90 percent ASF factor to other 

time deposits that mature in less than one year. 

4. Estimating Encumbrance of Assets and the Duration of Encumbrance 

As described in section D of Appendix A, we estimate the portion of each loan and security type encumbered by 

assuming that the amount of each loan and security type encumbered is equal to total pledged loans (securities) multiplied 

by the share of total loans (securities) represented by each category of loans (securities). We then assume, for each asset type, 

that exactly one-third of the assets that are assumed to be encumbered are encumbered for more than one year, another 

third is encumbered for six months to one year, and the final third is encumbered for less than six months. 

King (2013), Hong et al. (2014), DeYoung and Jang (2016), and Hoerova et al. (2018) make no adjustments to their NSFR 

proxy calculations to account for encumbered assets. 

5. Treatment of Nonperforming Assets 

As discussed in section D of Appendix A, we subtract the nonperforming (past due by 90 days or more or in nonaccrual 

status) portion of each loan type from the RSF category to which the loan would be assigned were it performing, and assign 

a 100 percent RSF factor to the nonperforming portion. The Call Report contains data only on the aggregate volume of 

securities that are nonperforming, so we apportion nonperforming securities to each RSF category based on the share of total 

securities in each RSF category. We subtract the volume of securities assumed to be nonperforming in each RSF category 

from the RSF category to which the securities would be assigned were they performing, and assign a 100 percent RSF factor to 

the nonperforming securities. 

DeYoung and Jang (2016) follow the same approach we do with respect to loans— that is, they subtract the 

nonperforming portion of each loan type from the RSF category to which the loan type would be assigned were it performing, 

and they assign a 100 percent RSF factor to the nonperforming portion. They make no adjustments to securities to account 

for nonperforming assets. 

King (2013), Hong et al. (2014), and Hoerova et al. (2018) make no adjustments in their NSFR proxies to account for 

nonperforming assets. 

6. Allocation of Residential Mortgage Loans to the 65 Percent and 85 Percent RSF Categories 

The final NSFR rule assigns a 65 percent ASF factor to residential mortgages that mature in more than one year, are not 

encumbered or nonperforming, and qualify for a risk weight of no greater than 50 percent under risk-based capital rules 

issued by U.S. financial regulatory agencies. In addition, mortgages encumbered for more than one year are assigned an RSF 

factor of 100, those encumbered for six months to one year are assigned an RSF factor that is the greater of 50 or the factor 

they would be assigned if they were not encumbered, and those encumbered for less than six months are assigned the same 

RSF factor they would be assigned had they not been encumbered. 

As described in section C of Appendix A, to construct the RIS-NSFR in accordance with the final NSFR rule, we assign a 

65 percent RSF factor to closed-end loans secured by first liens on 1–4 family residential properties that are performing and 

that are apportioned by our methodology to mature in more than one year and be unencumbered (or encumbered for less 

than one year). All other performing and unencumbered (or encumbered for less than one year) residential mortgage loans, 

which include open-end loans secured by residential properties and closed-end loans secured by junior liens on 1–4 family 

residential properties, are assigned an 85 percent RSF factor in our model. 
40 

In slight contrast to the final NSFR rule, the BCBS (2010) and BCBS (2014) NSFR definitions assign a 65 percent RSF factor 

to unencumbered residential mortgages of any maturity that would qualify for a 35 percent or lower risk weight under the 

Basel II Standardized Approach for credit risk. 

King (2013) assigns a 65 percent RSF factor to all residential mortgage loans. 

Hong et al. (2014) do not distinguish between loan types or security types to develop their NSFR proxy. In their model, the 

RSF factors assigned to loans and securities are based exclusively on the risk weight the loans and securities receive under 

the capital rules issued by U.S. financial regulatory agencies. They assign a 65 percent RSF factor to all loans that receive a 

20 percent risk weight, whether or not those loans are residential mortgages or other types of loans. Loans with other risk 

weights are assigned to different RSF categories. 

DeYoung and Jang (2016) assign an 85 percent RSF factor to all loans secured by real estate, which would include 

residential mortgages. 

Hoeroava et al. (2018) assign a 65 percent RSF factor to all household mortgages that mature in more than one year. 

7. Assigning the Maturity of Loans 

As discussed in section C of Appendix A, we apportioned total loans and leases that mature in less than one year to each 

RSF category based on the share of total loans and leases that we assigned to each RSF category. We subtract loans and 

leases that are assumed to mature in less than one year from the RSF category to which the loan type would be assigned if it 

matured in more than one year and assign an RSF factor of 15 percent to that amount for loans to financial institutions, or 50 

percent for all other loan types. 

King (2013) assigns a 0 percent RSF factor to interbank loans that mature in less than one year and a 100 percent RSF 

factor to interbank loans that mature in more than one year. He assigns a 50 percent RSF factor to loans to corporate clients 

that mature in less than one year and a 65 percent RSF factor to loans to corporate clients that mature in more than one year. 

Finally, he assigns an 85 percent RSF factor to loans to retail clients that mature in less than one year and a 100 percent RSF 

factor to loans to retail clients that mature in more than one year. 

Hong et al. (2014) and DeYoung and Jang (2016) do not adjust their models to account for the maturity of loans, likely 

because the Call Report reports only relevant loan maturities at the aggregate level and not at the level of individual loan 

types. 

Hoerova et al. (2018) assign a 15 percent RSF factor to loans to financial institutions and a 50 percent RSF factor to 

government loans, regardless of maturity. They assign a 50 percent RSF factor to household loans, household mortgages, 

and loans to nonfinancial corporations that mature in less than one year. They assign a 65 percent RSF factor to all other 

household mortgages and an 85 percent RSF factor to all other loans to households and nonfinancial corporations. 

8. Estimating Derivatives Asset and Liability Amounts 

As discussed in section C of Appendix A, to construct the RIS-NSFR we subtract the value of derivatives with a negative 

fair value from the value of derivatives with a positive fair value, and assign an RSF factor of 100 percent to the difference if it 

is a positive value and an ASF factor of zero to the difference if it is a negative value. We also assign an RSF factor of 100 to 5 

percent of the value of derivatives with a negative fair value. 

King (2013), Hong et al. (2014), and Hoerova et al. (2018) do not include derivatives in their NSFR proxy calculations. 

DeYoung and Jang (2016) do not include derivatives in their calculation because the NSFR as defined by BCBS (2014) leaves 

the treatment of derivatives up to the national regulator that implements the NSFR standard. 
41 

Appendix B. Validation Check Using NSFR Data Reported by Banking 

Organizations 

Given that RIS variables do not align perfectly to the NSFR categories, we check whether the RIS-NSFR can be a useful 

proxy for the NSFR by comparing the RIS-NSFRs for IDIs of selected banking organizations to actual NSFRs. Specifically, we 

calculate the aggregate RIS-NSFR for the set of IDIs under each bank organization’s global top tier parent and compare our 

calculated aggregate RIS-NSFR to the NSFR reported by the banking organization to BCBS. The reported NSFR data are 

confidential and were provided to us by supervisory divisions within the FDIC. Our results show a strong, positive correlation 

between our NSFR proxy and actual NSFRs as calculated by banks. 

BCBS has been monitoring the impact of Basel II/III reforms on participating banks.72 As part of this ongoing monitoring 

exercise, participating banks have provided to BCBS workbooks that identify 44 liability and equity line items, 103 asset 

items, and 11 off-balance sheet line items that constitute the NSFR.73 Most line items were disaggregated by their remaining 

periods of encumbrance and remaining maturities. Each disaggregated line item is then assigned an ASF (or RSF) factor. 

For this check of our NSFR proxy, we used BCBS data provided by large U.S. bank holding companies for the quarter ending 

December 31, 2017.74 

For each BHC for which we had BCBS data, we combined the assets, liabilities, and other NSFR RIS variables for all of 

its IDI subsidiaries. Three BHCs were dropped from the analysis because their combined IDI assets constituted a share of 

the BHC’s assets that was too small, or too large, to allow for a useful comparison of the RIS-NSFR to the BCBS data. For the 

remaining BHCs, RIS-NSFRs for the combined IDI assets were calculated and compared to the NSFRs reported by the BHCs. 

The two measures track each other well. Our modified RIS-NSFR exhibits strong positive correlations with the NSFRs reported 

to BCBS, with a statistically significant Pearson correlation coefficient of +0.65 and Spearman correlation coefficient of +0.73.75 

The calculated RIS-NSFRs were generally higher than the reported BCBS NSFRs, with an average deviation of 0.13. 

These deviations could arise from several sources. One possible reason for the differences is that the final NSFR rule assigns 

only certain regulatory capital elements to ASF100, particularly tier 1 and tier 2 capital before applying capital adjustments 

or deductions. Data for these adjustments and deductions are missing from a large proportion of filers, so the RIS-NSFR 

methodology uses total equity capital (EQTOT), which is reported for 99.9 percent of firm-quarter observations in the data, 

to proxy for NSFR regulatory capital elements. If EQTOT overestimates these elements, the methodology overestimates ASF 

and thereby also overestimates NSFR. Additionally, RIS combines the transaction deposits of individuals, partnerships, and 

corporations into a single variable (TRNIPCOC). Our methodology allocates half of TRNIPCOC to ASF95 to account for stable 

retail deposits and half to ASF90 to account for other retail deposits. To the extent that liabilities in TRNIPCOC represent ASF 

factors with lower weights, such as operational deposits, our methodology overestimates ASF. Further, RIS reports some 

maturity data, such as the total amount of loans and leases with maturities less than one year (LNLSLES1). However, LNLSES1 

is aggregated across all loan types. To estimate the value of each loan type with a maturity less than one year, we use a 

constant fraction across all loan types. If this apportionment underestimates the remaining maturities of certain assets, our 

methodology overestimates NSFR. 

For most assets, our apportionment for maturities does not extend to maturities of less than six months. Since we are 

missing this level of granularity, we assume that half of the values of assets with remaining maturities of less than 12 months 

72 See “Basel III Monitoring Report,” October 2018, at https://www.bis.org/bcbs/publ/d449.htm. 

73 Data provided by banks in the monitoring workbooks are confidential. Templates for the workbooks, which show the line items tallied, can be found at 

https://www.bis.org/bcbs/qis/. 

74 BCBS data were available for only one quarter (ending December 31, 2017), so the comparison represents a snapshot in time. Additionally, rigorous 

calculation and reporting instructions have not been established for the BCBS reporting, so their comparability is limited. 

75 These results are stronger than the results reported by Hoerova et al. (2018) who report a correlation of 0.55 between their proxy and reported NSFRs for 

banks in the euro area between 2014 and 2016. 
42 

 

 

1 

NSFR1 RSF1 ASF1 × RSF0 (ASF1 × RSF0 × TA0 )/TA0 

 ̃  ̃ = 

NSFR0 ASF0 RSF1 × ASF0 (ASF 0 × RSF1 × TA1 )/TA1 

RSF0 

have remaining maturities less than six months. For those assets for which we do not know whether the remaining maturities 

are more or less than 12 months, we assume that two-thirds of the value have remaining maturities of less than 12 months, 

and within that fraction, half have maturities of less than six months. If this assumption underweights ASF liabilities or 

overweights RSF assets, our methodology underestimates NSFR. 

We use the total values of pledged loans and pledged securities to estimate the encumbrances of those respective 

assets. However, we also do not know the distribution of encumbrances across asset types or the distribution of periods 

of encumbrance within each asset. As we did with maturities, we estimate encumbrances by assuming all loan/securities 

types have the same fraction of encumbrances, and all encumbered loans/securities are equally encumbered across the 

three periods of encumbrances. If this apportionment underestimates the encumbrances of assets, our methodology 

overestimates NSFR. 

Finally, our methodology does not account for items not reported on the Call Report, such as variation margin with 

derivative transactions. 

Outside of our methodology, differences in the balance sheet composition of BHCs and their IDI subsidiaries would lead 

to differences between RIS-NSFR and BCBS NSFR. If, compared to their IDI counterparts, non-IDI subsidiaries of BHCs hold 

more long-term or higher risk-weighted assets, then BHCs would naturally have a higher NSFR. 

While our RIS-NSFRs might overestimate NSFRs in this sample, it is still useful as a comparison across banks. RISNSFR is strongly positively correlated with reported BCBS NSFRs. To the extent that banks differ in their asset and funding 

composition, as reported on the Call Reports, our methodology can measure that effect on NSFR. 

Our methodology can also track changes in a bank’s NSFR over time due to changes in asset composition. For example, 

since cash and interest bearing balances (CHBALI) is assigned a 0 percent RSF factor in both the RIS-NSFR and BCBS NSFR 

calculations, an increase in CHBALI from 5 percent to 10 percent of assets would raise both measures. Therefore, our 

methodology allows us to examine trends in RIS-NSFR and use these trends to make reliable predictions of regulatory NSFRs. 

Appendix C. Decomposition of Changes in the Net Stable Funding Ratio 

This section explains a method for decomposing the total change in the NSFR (or RIS-NSFR) from one period to the 

next to determine the main drivers of the change. We follow the methodology used in Cohen and Scatinga (2014), as cited in 

Heynderickx, et al. (2016). This methodology can be used to decompose the change in the RIS-NSFR for an individual bank or 

the change in the aggregate RIS-NSFR for a set of banks. In the latter case, the decomposition does not consider the effects of 

institutions entering or exiting the set of banks, since it uses only aggregated asset and liability data at the two decomposition 

endpoints.76 

To decompose the change in RIS-NSFR between two periods, we deconstruct the NSFR into its components. We denote 

the initial period of analysis with the superscript 0 and the ending period with the superscript 1. Total Assets is denoted as TA. 

We divide NSFR1 by NSFR0 and factor TA0/TA0 = 1 into the numerator and TA1/TA1 = 1 into the denominator: 

ASF

76 For example, the aggregate RIS-NSFR, and decomposition thereof, does not distinguish between a bank growing its asset size by raising new capital and 

the equivalent change in assets/liabilities that would occur if the bank acquired another institution. 
43 

 ̃NSFR  ̃NSFR F = = ASF1 ASF0 

ln ( RSF1 ) − ln ( RSF0 ) ln (NSFR1) − ln (NSFR0) 

 

 

 

1 0 1 0 

 ̃NSFR = F × ln − ln + F ( ( TA1 ) ( TA0 )) × (ln ( TA1 ) − ln ( TA1 )) 

Taking the natural logarithm of both sides and collecting terms, we get: 

Funding Composition Asset Composition 

Effect Affect 

ASF ASF RSF RSF

where F is a scaling factor given by: 

and: 

F′ = -F

The first term on the right-hand-side of the equation, the funding composition effect (FCE), represents that portion of 

the change in NSFR associated with an improvement in available stable funding relative to total liabilities and equity, for 

example, by increasing equity or holding longer-term and insured deposits.77 The second term, called the asset composition 

effect (ACE), represents that portion of the change in NSFR associated with changes in required stable funding relative to 

total assets, for example, holding shorter-term, better-performing, less-encumbered, or less-risky assets. An improving asset 

composition will result in a positive term. 

The funding composition effect and asset composition effect can be visualized by graphing the fractions ASF/TA and RSF/ 

TA, respectively, as shown in Figures C. 1 and C.2. 

Figure C.1 

Available Stable Funding Increased Relative to Total 

Assets for All Size Groups in 2020 

Available Stable Funding

Percent of Total Assets 

90 

85 

80 

75 

70 

65 

60 

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 Dec-16 Dec-19 

Large Midsize Small 

Source: FDIC Research Information System. 

Note: Shading denotes recessions (Dec 2007–Jun 2009 and 

Feb 2020–Apr 2020). 

Figure C.2 

Required Stable Funding Decreased Relative to Total 

Assets for All Size Groups in 2020 

Required Stable Funding

Percent of Total Assets 

75 

70 

65 

60 

55 

50 

Dec-01 Dec-04 Dec-07 Dec-10 Dec-13 Dec-16 Dec-19 

Large Midsize Small 

Source: FDIC Research Information System. 

Note: Shading denotes recessions (Dec 2007–Jun 2009 and 

Feb 2020–Apr 2020). 

To interpret Figures C.1 and C.2, note for example the movements of the large bank lines in 2020, the last year of the 

sample. During that period, the ASF for large banks increased slightly while the RSF for large banks dropped by more than 

15 percentage points. Both movements result in an increase in RIS-NSFR, and the decomposition arithmetic, delineated 

77 Follows from the accounting identity: Total Assets = Total Liabilities + Total Equity. 
44 

 RSF1 RSF0F° × (ln ( ) TA1 − ln ( ) TA0 ) H = 

RSF1 RSF0 ( TA1 − TA0 ) 

 ASF1 ASF0F × (ln ( ) TA1 − ln ( ) TA0 ) G = ASF1 ASF0 ( − ) TA1 TA0 

 

ASF1 ASF0 ASFa F × (ln ( ) − ln ( )) = G × °a ×  ̃

TA1 TA0 a ̃A TA

 

RSF1 RSF0 RSFr F° × (ln ( ) − ln ( )) = H × ˇ r × ˆ TA1 TA0 r ̃R TA 

in Table C.3, will show that the asset composition effect from the drop in RSF accounts for 18 percentage points of the 19 

percentage point gain in the RIS-NSFR from December 2019 to December 2020. 

We can further decompose the two factors into sub-factors for each RSF and ASF category. The FCE can be decomposed 

into individual ASF category effects by the formula: 

where a is the ASF factor for category a, ∆ (ASFa / TA) is the change from period 0 to period 1 in the ratio of unweighted 

amounts in category a to total assets, and 

Similarly: 

where r is the RSF factor for category r, ∆(RSFr / TA) is the change from period 0 to period 1 in the ratio of unweighted amounts 

in category r to total assets, and 

We present these second-level decompositions for three time periods discussed in Section VI. Table C.1 shows the 

decomposition for the four years following the end of the recession in June 2009. 
45 

Table C.1 

RIS-NSFRs for All Size Groups Increased From June 2009 to March 2013 as a Result of More Available Stable 

Funding and Less Required Stable Funding 

Category Large Midsize Small 

Available Stable 

Funding (ASF) 

100% -0.02 0.00 -0.02 

95% 0.00 0.00 0.04 

90% 0.13 0.06 0.00 

50% -0.01 0.00 0.00 

0% 0.00 0.00 0.00 

Funding Composition Effect (FCE) 0.11 0.06 0.02 

Required Stable 

100% 0.03 0.05 0.01 

85% 0.05 0.01 0.05 

65% 0.00 -0.02 -0.02 

50% 0.01 0.02 0.04 

15% 0.00 0.00 -0.01 

Funding (RSF) 5% 0.00 0.00 -0.01 

0% 0.00 0.00 0.00 

Negative Value Derivatives 0.00 0.00 0.00 

RSF Adjustments 0.01 -0.01 0.00 

Asset Composition Effect (ACE) 0.08 0.04 0.05 

Change in RIS-NSFR (FCE+ACE) 0.19 0.10 0.07 

Source: FDIC. 

Note: RIS is the FDIC’s Research Information System. NSFR is the Net Stable Funding Ratio. 
46 

Table C.2 shows the decomposition for the period of divergence between 2013 and 2019. 

Table C.2 

The Effect of Changes in Asset Composition on RIS-NSFRs From March 2013 to December 2019 Was Negative for 

Small and Midsize Banks but Positive for Large Banks 

Category Large Midsize Small 

Available Stable 

Funding (ASF) 

100% 0.00 -0.01 0.00 

95% 0.02 -0.04 -0.08 

90% 0.06 0.06 0.06 

50% 0.00 0.00 0.01 

0% 0.00 0.00 0.00 

Funding Composition Effect (FCE) 0.08 0.02 -0.01 

Required Stable 

100% 0.04 -0.01 -0.01 

85% 0.06 -0.02 -0.14 

65% -0.01 -0.01 -0.02 

50% 0.00 0.00 0.01 

15% -0.01 0.00 0.02 

Funding (RSF) 5% 0.00 0.00 0.00 

0% 0.00 0.00 0.00 

Negative Value Derivatives 0.00 0.00 0.00 

RSF Adjustments 0.00 0.01 0.00 

Asset Composition Effect (ACE) 0.08 -0.04 -0.16 

Change in RIS-NSFR (FCE+ACE) 0.16 -0.02 -0.17 

Source: FDIC. 

Note: RIS is the FDIC’s Research Information System. NSFR is the Net Stable Funding Ratio. 
47 

Table C.3 shows the decomposition for the year 2020. 

Table C.3 

Asset Composition Drove the Increase in RIS-NSFRs From December 2019 to December 2020 for All Size Groups 

Category Large Midsize Small 

Available Stable 

Funding (ASF) 

100% -0.05 -0.04 0.00 

95% 0.05 -0.01 -0.01 

90% 0.01 0.06 0.03 

50% -0.01 0.00 -0.01 

0% 0.00 0.00 0.00 

Funding Composition Effect (FCE) 0.01 0.01 0.01 

Required Stable 

100% 0.03 0.01 0.00 

85% 0.11 0.09 0.01 

65% 0.02 -0.02 0.01 

50% 0.01 0.01 0.01 

15% 0.00 0.00 0.00 

Funding (RSF) 5% 0.01 0.00 0.00 

0% 0.00 0.00 0.00 

Negative Value Derivatives 0.00 0.00 0.00 

RSF Adjustments 0.00 0.00 0.02 

Asset Composition Effect (ACE) 0.18 0.09 0.05 

Change in RIS-NSFR (FCE+ACE) 0.19 0.11 0.06 

Source: FDIC. 

Note: RIS is the FDIC’s Research Information System. NSFR is the Net Stable Funding Ratio. 

To summarize the results of the decomposition, Tables C.1, C.2, and C.3 show that the RIS-NSFR for large banks increased 

by 19, 16, and 19 percentage points in the three periods described above, for a total increase of 54 percentage points between 

June 2009 and December 2020. The funding composition effect accounted for an 11 percentage point increase between 

June 2009 and March 2013, an 8 percentage point increase between March 2013 and December 2019, and a 1 percentage 

point increase from December 2019 to December 2020, for a total of 20 percentage points, meaning that 37 percent of the 

54 percentage point increase for large banks between June 2009 and December 2020 was driven by ASF funding categories. 

Funding sources that are included in the ASF 90 and 95 percent categories, such as retail deposits, are responsible for 

much of these increases. Changes (i.e., decreases) in large bank holdings of assets in the 100 and 85 percent RSF categories 

are also responsible for much of the increase in RIS-NSFR for large banks over this period. Because the mechanics of the 

decomposition require multiplying by the corresponding factor, changes in the RSF category with a 0 percent factor (for 

example, Cash Balances) appear as 0 in the decomposition table. Thus, a positive change in each RSF line item should be 

interpreted as an increase in RIS-NSFR due to the reallocation of assets in that RSF category to the RSF 0 percent category. For 

example, Table C.3 shows that, in 2020, a reallocation of assets from 85 to 0 percent RSF resulted in an increase in RIS-NSFR of 

11 percentage points. 

Similar statements can be made for small and midsize banks.