Why Did Bank Stocks Crash During COVID-19?
Summary
NBER Working Paper No. 28559, "Why Did Bank Stocks Crash During COVID-19?", by Viral V. Acharya, Robert F. Engle III, Maximilian Jager and Sascha Steffen, dated March 2021 and revised September 2023. The abstract states that a two-sided credit-line channel of drawdowns and repayments explains the drop and partial recovery in bank stock prices during the pandemic. The introduction reports that a one-standard-deviation increase in liquidity risk decreased bank stock returns by about 8.4 percentage points between January 1, 2020 and March 3, 2020. It separates a funding channel from a capital channel using gross and net drawdown measures and reports that banks with high gross drawdowns reduced lending. The paper closes with a variable table defining industry exposure measures such as presence share, teamwork share and physical proximity.
Summary drafted by a model from the document's text below and checked by script against that text before publication. It is a navigation aid, not a reading of what the document proves. Where AI is used
Full text
NBER WORKING PAPER SERIES
WHY DID BANK STOCKS CRASH DURING COVID-19?
Viral V. Acharya
Robert F. Engle III
Maximilian Jager
Sascha Steffen
Working Paper 28559
http://www.nber.org/papers/w28559
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
March 2021, Revised September 2023
We thank Jennie Bai, Tobias Berg, Allen Berger, Christa Bouwman, Gabriel Chodorow-Reich,
Olivier Darmouni, Darrell Duffie, Ruediger Fahlenbrach, Anna Kovner, Kevin Raghet, Rafael
Repullo, Phil Strahan, Daniel Streitz, René Stulz, Anjan Thakor, Josef Zechner and participants at
the 2020 Federal Reserve Stress Testing Conference and seminar participants at the Annual
Columbia SIPA/BPI Bank Regulation Research Conference, Banco de Portugal, Bank of
England, SFS Cavalcade 2022, CAF, EFA 2021, Federal Reserve Bank of Cleveland, NYU Stern
Finance, RIDGE Workshop on Financial Stability, University of Southern Denmark, University
of Durham, Villanova Webinars in Financial Intermediation, the Volatility and Risk Institute,
World Bank, WU Vienna, for comments and suggestions and Sophie-Dorothee Rothermund and
Christian Schmidt for excellent research assistance. Robert Engle would like to thank NSF
2018923, Norges Bank project “Financial Approach to Climate Risk” and Interamerican
Development Bank Contract #C- RG-T3555-P001 for research support to the Volatility and Risk
Institute of NYU Stern. The views expressed herein are those of the authors and do not
necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2021 by Viral V. Acharya, Robert F. Engle III, Maximilian Jager, and Sascha Steffen. All
rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without
explicit permission provided that full credit, including © notice, is given to the source.
Why Did Bank Stocks Crash During COVID-19?
Viral V. Acharya, Robert F. Engle III, Maximilian Jager, and Sascha Steffen
NBER Working Paper No. 28559
March 2021, Revised September 2023
JEL No. G01,G21
ABSTRACT
A two-sided "credit-line channel" – relating to drawdowns and repayments – explains the severe
drop and partial subsequent recovery in bank stock prices during the COVID-19 pandemic. Banks
with greater exposure to undrawn credit lines saw larger stock price declines but performed better
before the pandemic and after the policy interventions. Despite deposit inflows, high drawdowns
led to reduced bank lending, suggestive of capital encumbrance upon drawdowns. Repayments of
credit lines unencumbered capital which explains the stock price recovery starting Q2 2020. Bank
provision of credit lines resembles writing deep out-of-the-money put options on aggregate risk,
and we propose how to incorporate this feature into bank capital stress tests.
Viral V. Acharya Maximilian Jager
Stern School of Business Frankfurt School of Finance & Management
New York University Adickesallee 32-34
44 West 4th Street, Suite 9-65 60322 Frankfurt am Main
New York, NY 10012 Germany
and CEPR m.jager@fs.de
and also NBER
vacharya@stern.nyu.edu Sascha Steffen
Frankfurt School of Finance & Management
Robert F. Engle III Adickesallee 32-34
Department of Finance, Stern School of Business 60322 Frankfurt
New York University, Salomon Center Germany
44 West 4th Street, Suite 9-160 s.steffen@fs.de
New York, NY 10012-1126
and NBER
rengle@stern.nyu.edu
1. Introduction
Since the global financial crisis (GFC) of 2008--09, banks have greatly expanded their liquidity
provision through credit lines to the United States (U.S.) non-financial sector. Panel A of Figure
1 shows that bank credit lines for the U.S. publicly listed firms increased from 0.7% of GDP in
2009 to 5.7% of GDP in 2019 leading to a substantial build-up of drawdown risk on bank
balance-sheets. This risk materialized in March 2020 amid the outbreak of the COVID-19
pandemic and subsequent government-imposed lockdowns. Firms’ cash flows dropped, in
some cases by as much as 100%, while operating and financial leverage remained sticky,
causing bond markets to freeze. As a consequence, U.S. firms with pre-arranged credit lines
from banks drew down their undrawn facilities with a far greater intensity than in past
recessions (Panel B of Figure 1), specifically the prospective fallen angels or BBB-rated and
junk-rated firms (Panel C of Figure 1).
[Figure 1 about here]
Recent data show that firms benefited from having such access to pre-arranged credit lines
during the pandemic when capital market funding froze (e.g., Acharya and Steffen, 2020a;
Chodorow-Reich et al., 2022; Greenwald et al., 2023).1 On the flip side, however, banks faced
unprecedented aggregate risk in the form of a correlated demand for credit-line drawdowns; an
important but not well-appreciated consequence is that banks’ share prices crashed and
persistently underperformed those of non-financial firms as well as non-bank financial firms
(Panel D of Figure 1).
In this paper, we investigate causes and consequences of this crash of bank stocks during
the COVID-19 pandemic and highlight a central role played by banks’ credit-line business.
1
Within three weeks, public firms drew down more than USD 300bn, with drawdowns particularly concentrated
among riskier BBB-rated and non-investment-grade firms. For instance, Ford Motor Company drew down its
credit lines in March 2020, withdrawing USD 15.4bn. With USD 20bn in cash, credit lines significantly impacted
its liquidity. Originally, Ford paid 15bps for undrawn credits and 125bps for drawn credits. However, after a
downgrade to non-investment grade, these fees increased substantially by 67% and 40% respectively. Li et al.
(2020) show – using FDIC’s Call Report data which includes drawdowns by private firms – that total drawdowns
amounted to more than USD 500bn.
1
Specifically, we ask what are the possible transmission channels through which the drawdowns
affected bank stock returns and ultimately banks’ intermediation functions for the real
economy? What was the role of credit line repayments for the recovery of bank stock prices in
the second quarter of 2020 following the stark decline in 2020Q1? Which aspects of these
channels during the COVID-19 episode are different compared to prior stress episodes such as
the GFC? Lastly, we ask how bank regulation can incorporate the relevant channels of
transmission from bank credit lines to financial fragility to safeguard against the attendant risks
in future?
At the core of our analysis is a new and comprehensive measure of the balance-sheet
liquidity risk of banks defined as undrawn commitments plus wholesale finance minus cash or
cash equivalents (all relative to assets). Our null hypothesis is that investors price liquidity risk
according to their expectations regarding the possible credit line drawdowns during crises.
However, these expectations might naturally deviate from realized drawdowns in times of
stress. At the beginning of the COVID-19 pandemic, capital markets froze increasing rollover
risk for all, but particularly for riskier, firms. Firms responded by drawing down credit lines
with significantly higher intensity and magnitude compared to the global financial crisis (GFC)
2007-2008. For example, the average drawdown rate in Q1 2020 was 37% and in Q4 2008 29%.
The cross-section of stock-price declines of banks as a function of their ex-ante exposure to
drawdown risk (during COVID) can therefore be intrepreted as reflecting the difference
between expected and realized drawdown risks.
Consistent with this hypothesis, we find that our measure of the liquidity risk of banks
helps understand the decline of bank stock prices, especially during the first phase of the
pandemic from January 1, 2020 until March 3, 2020, i.e., before decisive monetary and fiscal
support measures were introduced.2 A one-standard-deviation increase in liquidity risk
2
See in particular Kovner and Martin (2020) on the range of special facilities set up by the Federal Reserve (Fed)
to provide liquidity to a range of fixed-income markets.
2
decreased bank stock returns by about 8.4 percentage points during this period, or 12.5% of the
unconditional mean return. A possible concern is that liquidity risk through the provision of
credit lines is correlated with bank portfolio composition, as banks facing larger drawdowns
may be engaged with riskier borrowers who are more vulnerable to financial and economic
crises, and specifically to the onset of COVID-19 pandemic. We provide a variety of tests to
isolate the effect of credit-line exposure on bank stock returns using different measures for bank
exposure to COVID-19 affected industries. Our results on bank stock returns being affected by
balance-sheet liquidity risk appear virtually unaffected by these measures of bank portfolio risk
and provide a consistent interpretation that balance-sheet liquidity risk is a key driver of bank
stock returns at the beginning of the pandemic – independent of the effect of bank portfolio
exposures to COVID-affected industries.
We then show that this cross-sectional explanatory power of balance-sheet liquidity risk
for bank stock returns is highly episodic in nature. Using separate cross-sectional regressions
during the months of January 2020, February 2020 and during the March 1, 2020 to March 23,
2020 period, we show that liquidity risk explains stock returns, particularly during the latter
period, when firms’ liquidity demand through credit-line drawdowns sharply increased and
became highly correlated. The effect disappeared in Q2 2020, i.e., after the decisive monetary
and fiscal interventions, but briefly re-surfaced amid the second wave of the pandemic and
associated lockdowns in Q3 2020 (the effect is, however, much smaller compared to March
2020).3
We analyze two channels through which this sensitivity of bank stock prices to undrawn
credit lines can arise: (1) funding liquidity to source new loans can become a binding constraint
for banks if deposit funding does not keep pace with credit line drawdowns (the “funding
3
The Fed intervened in the repo market on March 12, 2020, stabilizing the OIS-spread, a measure for liquidity
conditions in financial markets. However, these actions did not halt the drop in bank stock prices, implying
liquidity was not a binding constraint for banks at the pandemic's onset.
3
channel”);4 and, (2) the drawdown of credit lines can “lock up”, i.e., encumber, scarce bank
capital against drawn facilities and impair intermediation by preventing banks from making
possibly more profitable loans (the “capital channel”).5 To distinguish between these channels,
we construct two proxies: (1) Gross Drawdowns as the change in credit line drawdowns
(relative to total assets); and (2) Net Drawdowns as the change in drawdowns minus the change
in deposit funding (also relative to total assets). Gross and net drawdowns are not highly
correlated but net drawdowns are highly correlated with changes in deposits. Keeping net
drawdowns constant, our gross drawdown metric distinguishes the credit line drawdowns'
impact on banks due to capital channel, rather than the funding channel. Our analysis shows
that bank stock returns during the COVID onset are sensitive to gross drawdowns but not
significantly to net drawdowns. Banks with higher capital (buffers) experience less negative
impact on stock returns during gross drawdowns. In essence, banks' balance-sheet liquidity risk
influences stock returns, as credit line drawdowns encumber bank capital away from more
lucrative intermediation opportunities.
Next, we investigate this mechanism directly by testing whether banks with more
balance-sheet liquidity risk reduced their lending during the COVID-19 pandemic by a greater
degree relative to other banks. If banks’ capital constraints matter, then we expect lending to be
particularly sensitive to gross (but not to net) drawdowns. To control for demand effects, e.g.,
because of lower investments by riskier firms in a period characterized by high uncertainty or
because riskier borrowers have already drawn down existing lines of credit, we employ a
Khwaja and Mian (2008) estimator, investigating the change in lending of banks to the same
borrower before and after the outbreak of the pandemic. We find that banks with high gross
4
This was the case during the GFC as shown by Acharya and Mora (2015).
5
The theoretical literature argues that a key function of bank capital is to absorb risk, i.e., more capital facilitates
bank lending. Bhattacharya and Thakor (1993), Repullo (2004), von Thadden (2004), and Coval and Thakor
(2005), among others, argue that capital increases risk-bearing capacity. Allen and Santomero (1998) and Allen
and Gale (2004) show that banks with less capital might have to dispose of illiquid assets at a cost when facing an
adverse shock, which may affect their ability to lend ex ante.
4
drawdowns (but not net drawdowns) actively reduce existing term-loan exposures relative to
banks with low gross drawdowns. Moreover, banks with high gross drawdowns reduce new
loan originations compared to banks with low gross drawdowns, for both credit lines and term
loans. That is, holding the effect of deposit inflows constant, banks that incur a greater impact
on equity capital through large credit line drawdowns reduce lending more than other banks.
Overall, aggregate drawdowns at banks appear to have important spillovers for credit provision
to the real economy via the bank capital channel.
Bank stock prices lagged notably behind non-financial firms in the post-intervention
period. To elucidate this discrepancy, we introduce the two-sided "credit-line channel." Central
to this are the dual options credit lines offer firms: the ability to draw and the choice to repay
(or withhold repayment). Recognizing the significance of the repayment option is pivotal in
understanding banks' stock performance during the post-intervention period. In Q2 and Q3
2020, as capital market issuances resumed, top-rated firms began exercising their repayment
option (see, e.g., Chodorow-Reich et al., 2022). We construct a measure of credit-line
repayments using a matched sample of banks and firms with data from FDIC Call Reports,
Refinitiv Dealscan and Capital IQ. To distinguish between liquidity and capital effects of
repayments, we formulate two variables. First, we measure the total liquidity returning to banks'
balance sheets using the ratio of the repaid amount to the committed amount of a credit line. As
a second measure, we employ the difference in the revenue (from fees and interest rate) between
the drawn credit line and potential alternative investments of similar risk profiles.6
Our findings verify that both factors influenced the partial recovery of bank stock returns
in 2020Q2. Repayments benefit stock returns due to the liquidity they provide. Yet, banks favor
repayments from credit lines with lower (opportunity cost-adjusted) fees. Essentially, banks
6
For an accurate comparison, we use as alternative a corporate bond index matching the credit line borrower's risk
and regulatory capital cost (through risk-weights) as a proxy. Since capital costs of loans are rating-specific for
banks, this measure captures the capital channel of credit line repayments. Suppose banks A and B charge
borrowers the same interest, but bank A's borrower ties up more capital. We theorize that bank A gains more from
credit line repayment, freeing up more capital, leading to a greater positive impact on its stock return than bank B.
5
and their investors seek compensation for their opportunity cost of encumbered capital and
drawdown risk. The more capital is tied up by a drawdown, the more revenue a credit line must
generate to satisfy investors. We therefore conclude that the capital channel is pivotal in
understanding the two-sided nature of the impact of credit lines on stock returns, through
drawdowns as well as repayments.
A natural question to ask is whether drawdown risk of banks materialized and was priced
in other crisis periods, such as during the Dotcom bubble burst or the GFC, and whether
investors in banks get compensated with higher stock returns outside of crisis periods for
bearing this aggregate risk. To answer these questions, we regress quarterly bank stock returns
on credit line commitments over the 1995Q1 to 2021Q1 period on a sample of high- and low-
commitment banks matched on bank health (capitalization, NPL-to-loan ratios), size (assets)
and business model (loan-to-assets), controlling for the five Fama-French factors. We find that
high commitments – and therefore (ex-post) aggregate drawdown risk – adversely impacts bank
stock returns during all three crisis periods, with the impact during Covid approximately 2.5
times more potent than during the Dotcom and GFC periods. We also find that investors are
compensated for aggregate drawdown risk outside crises. Put differently, evidence does not
support a total oversight or mispricing of this risk by bank stock investors. Instead, our findings
align with the idea that investors reassess the implications of unexpected credit line drawdowns
during states with significantly high aggregate risk.7
The finding that bank stock investors seem to bear the aggregate risk of credit line
drawdowns prompts us to study credit line pricing by banks. While credit line spreads and fees
can reflect idiosyncratic drawdown risk, as shown by Berg et al. (2016, 2017) and Acharya et
al. (2013), they might not adequately reflect the aggregate nature of the risk. Our data reveals
7
Compare, for example, English et al. (2018), who show how investors reassess banks’ stock returns sensitivity
to interest rate risk in the light of unexpected interest rate changes. Diep et al. (2021) document that investors try
to price systematic prepayment risk in mortgage-backed securities (MBS). Similarly, we expect investors to adjust
the pricing of banks’ stocks in response to any signals/information about aggregate drawdown risk.
6
that idiosyncratic drawdown risk is considered in commitment fees and spreads. However,
banks do not factor in aggregate drawdown risk when setting credit line prices, explaining their
equity capital reliance during the pandemic. In essence, credit line pricing does not seem to
fully signal aggregate drawdown risk. This is then consistent with investors having to adjust
their expectations regarding drawdowns during periods of aggregate risk, and in turn,
unexpected drawdowns in such times leading to an adverse bank response in bank stock prices.
How can policymakers proactively manage this aggregate drawdown risk? One approach
is to include credit line drawdown effects in bank capital stress tests, mandating banks to
support these exposures with more equity capital ex ante. We extend the concept of SRISK, a
market-data based estimation of capital shortfall under aggregate stress, in Acharya et al.
(2012), Acharya et al. (2016) and Brownlees and Engle (2017), to account explicitly for
contingent credit line drawdowns. Specifically, we propose two adjustments: (1) Factor in the
required equity capital when contingent liabilities become actual liabilities during stress
periods; and, (2) Reflect this liquidity risk's adverse effect on bank market value during stress
periods, as estimated in our prior regression analysis. These adjustments reveal an additional
capital deficit of over USD 366bn for the U.S. banking sector as of end-2019 in a stress scenario
of 40% correction to the S&P500 index and when subject to an 8% market-equity capital
requirement under stress, with the top 10 banks' shortfall being 1.7 times greater.
2. Related literature
Our paper relates to the literature highlighting the role of banks in liquidity provision. Kashyap
et al. (2002) and Gatev and Strahan (2006) propose a unique role for banks as liquidity
providers to both households and firms, given efficiency in risk management (via cash
holdings) and access to government backstops (which induces a flight to safety in deposits),
respectively. Ivashina and Scharfstein (2010) document evidence of an acceleration of credit-
line drawdowns as well as an increase in aggregate bank deposits during the 2007-2009 crisis.
During this crisis – in which the banking system itself was at the centre and several individual
7
banks faced significant deposit withdrawals – Acharya and Mora (2015) show that banks faced
a crisis as liquidity providers and could manage credit line drawdowns only because of (and
after) significant support from the government. During the COVID-19 pandemic, however,
which directly affected the corporate sector, Li et al. (2020) and Acharya and Steffen (2020b)
show that aggregate deposit inflows were sufficient to fund the increase in liquidity demand
from drawdowns. Chodorow-Reich et al. (2022) and Greenwald et al. (2023) document
important lending spillovers and show that particularly small firms experienced a drop in the
supply of bank credit when large firms drew down credit lines using F-14Q data. Kapan and
Minoiu (2021) provide similar results using Dealscan data.
None of these papers, however, explores the implications of banks as liquidity providers
for their stock returns when drawdowns – and eventual repayments – affect bank capital
availability for other intermediation functions.8 By examining both gross drawdowns and net
(of deposit inflows) drawdowns, we demonstrate that credit-line drawdowns reduce banks’
franchise value because of binding capital constraints.
There is a large corporate finance literature on the availability and pricing of credit lines
as well as credit line usage.9 In contrast to this literature, we take a bank-centric view and
investigate the implications of drawdown risks for banks with large exposures to committed
credit lines. Importantly, we show that – while idiosyncratic and systematic components of a
firm’s stock return volatility are incorporated by banks in the pricing of credit lines extended to
a firm – banks do not appear to adequately or fully price the drawdown risk for the banking
sector in the aggregate, i.e., in large stress episodes such as the GFC or the pandemic. Acharya
8
Others focus on stock price reactions of mainly non-financial firms to the COVID-19 pandemic, emphasizing the
importance of financial policies (Ramelli and Wagner, 2020), financial constraints and the cash needs of affected
firms (Fahlenbrach et al., 2021), changing discount rates because of higher uncertainty (Gormsen and Koijen 2020,
Landier and Thesmar 2020), social-distancing measures (Pagano et al., 2020) and corporate governance and
ownership (Ding et al., 2021). Demirguc-Kunt et al. (2021) investigate the bank stock market response to the
COVID-19 pandemic and policy responses globally. They highlight that the effectiveness of policy measures was
dependent on bank capitalization and fiscal space in the respective country.
9
See, e.g., Sufi (2009), Jiménez et al. (2009), Campello et al. (2010, 2011), Acharya et al. (2013, 2014), Ippolito
et al. (2016), Berg et al. (2016, 2017), Nikolov et al. (2019) and Chodorow-Reich and Falato (2020).
8
and Steffen (2020a) document a dash-for-cash and run on credit lines at the beginning of the
COVID-19 pandemic.10 Darmouni and Siani (2020) show that a large percentage of these credit
lines were repaid through bond issuances in Q2 and Q3 2020. We show, however, that not all
banks (equally) benefited from the repayments and the capital that was freed-up. Some banks
were earning high interest or fees on the drawn portion of the credit lines which they had to
forego due to their repayment. To summarize, we propose a two-sided “credit-line” channel to
make sense of the stock price performance of banks during the COVID-19 pandemic.
Finally, we also compare our liquidity risk measure – defined as unused credit line
commitments plus wholesale funding minus liquidity, all relative to total assets – for banks with
two frequently used measures in the literature, the Berger and Bouwman (2009) liquidity
creation measure (which is based both on- and off-balance-sheet data) and the Bai et al. (2018)
liquidity risk measure (which also employs markets data). All three measures significantly
explain bank stock returns in individual regressions.11 When we run a horse race including all
measures, our liquidity risk measure remains significant (while the other two measures become
insignificant) suggesting that it contains information about aggregate drawdown risk of credit
lines that is not included or fully captured in the other liquidity measures.
3. Balance-sheet liquidity risk and bank stock returns
3.1. Data
We collect data for all publicly listed bank holding companies of commercial banks in the U.S.
and construct our main dataset following Acharya and Mora (2015), dropping all banks with
total assets below USD 100mn at the end of 2019 and keeping only those banks that we can
10
There is growing literature analyzing the implications of COVID for corporate finance and capital markets such
as the disruption in corporate bond markets (e.g., Haddad et al., 2021; O’Hara and Zhou, 2021), the role of
FinTechs in providing credit (Erel and Liebersohn, 2022) or the impact of government support programs on the
supply of loans (e.g., Balyuk et al., 2021; Boyarchenko et al., 2022; Minoiu et al., 2021; Vissing-Jorgensen, 2021).
11
While there's no consensus in literature on measuring a bank’s liquidity, various approaches exist. Deep and
Schaefer (2004) focus on on-balance-sheet liquidity, using scaled assets minus liabilities. Berger and Bouwman
(2009) offer a broad measure incorporating on- and off-balance-sheet components, emphasizing liquidity creation.
We zero in on liquidity risk during economic downturns via credit lines and short-term funding. Bai et al. (2018)
build a dynamic liquidity risk measure from both balance sheets, reflecting current market conditions. In contrast,
our approach provides a simpler, ex-ante view of bank liquidity risk exposure.
9
match to the CRSP/Compustat database. All financial variables (on the holding-company level)
are obtained from FDIC Call Reports (FR-Y9C) and augmented with data sourced from SNL
Financial. We keep only those banks for which we have all data available for our main
specifications during the COVID-19 pandemic, which limits our sample to 147 U.S. bank
holding companies (accounting for about 99% of all outstanding credit lines).12 All variables
are explained below or in Appendix III.
We match our sample with a variety of different datasets. Data on daily drawdowns
during the start of the COVID-19 pandemic as well as information about loan amendments is
obtained from the EDGAR database and firms’ 10-K/10-Q filings. We obtain daily stock
returns for our sample banks from CRSP. Capital IQ provides quarterly data on credit-line
drawdowns and repayments by firm as well as credit ratings. We manually match our banks to
the Refinitiv Dealscan database to obtain outstanding credit lines on a bank–firm level as well
as term loan exposures for the banks in our data set. Information about industries affected by
COVID-19 is obtained from other studies as described below. For some tests and statistics, we
use secondary market data about different industry sectors (e.g., the oil or retail sector) from
Refinitiv. We obtain information about a bank’s systemic risk measure, SRISK, from the
Volatility and Risk Institute at NYU Stern (vlab.stern.nyu.edu/srisk). Other market information
is downloaded from Bloomberg (e.g., oil volatility (CVOX), VIX, and S&P 500 market return).
3.2. Measuring balance-sheet liquidity risk of banks
To construct our measure of a bank’s balance-sheet liquidity risk, we collect bank balance-sheet
information as of Q4 2019 from FDIC Call Reports and construct three key variables following
Acharya and Mora (2015): (1) Unused C&I Commitments: The sum of credit lines secured by
1–4 family homes, secured and unsecured commercial real estate credit lines, commitments
12
Berger and Bouwman (2009), among others, document that off-balance-sheet credit commitments are important
for large banks, but not medium-sized and small banks. The smaller number of banks in our dataset is a
consequence of changes in reporting requirements over time (i.e., an increase in the size threshold above which
banks have to provide specific information).
10
related to securities underwriting, commercial letter of credit, and other credit lines (which
includes commitments to extend credit through overdraft facilities or commercial lines of
credit); (2) Wholesale Funding: The sum of large time deposits, deposits booked in foreign
offices, subordinated debt and debentures, gross federal funds purchased, repos, and other
borrowed money; and, (3) Liquidity: The sum of cash, federal funds sold and reverse repos, and
securities excluding MBS/ABS securities. All variables are defined in Appendix III. Using
these components, we construct a comprehensive measure of bank balance-sheet liquidity risk
(Liquidity Risk):
𝑈𝑛𝑢𝑠𝑒𝑑 𝐶&𝐼 𝐶𝑜𝑚𝑚𝑖𝑡𝑚𝑒𝑛𝑡𝑠 + 𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒 𝐹𝑢𝑛𝑑𝑖𝑛𝑔 − 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦
𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑖𝑠𝑘 =
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
Figure 2 shows the time-series of the cross-sectional mean of quarterly Liquidity Risk (using
our sample banks and weighted by total assets) since January 2010, as well as its components,
i.e., Unused C&I Credit Lines and Wholesale Funding, all relative to total assets.
[Figure 2 about here]
Liquidity Risk of banks decreased since Q1 2010 to a level of about 20% relative to total assets
by Q4 2016 (Panel A of Figure 2). In 2017, Liquidity Risk started to increase until Q4 2019,
i.e., before the start of the COVID-19 pandemic. At the beginning of the pandemic in Q1 2020,
liquidity risk dropped about 40% and continued to decline somewhat between Q2 and Q4 of
2020.
Panel B of Figure 2 shows the different components of bank balance-sheet liquidity risk.
The decrease since Q1 2010 is driven by the declining share of wholesale funding relative to
total assets during the COVID-19 pandemic. However, since 2017, the marginal increase in the
importance of unused C&I loans has been larger than the marginal decline in wholesale funding
exposure; as a result, Liquidity Risk started to increase again. The large decline of Liquidity Risk
during the first quarter in 2020 was driven by the decrease in unused C&I credit lines consistent
with the increase in drawdowns documented in Figure 1. We saw an immediate reversal of
11
Unused C&I Credit Lines in Q2 and Q3 2020 albeit not to pre-COVID-19 levels, pointing to a
partial repayment of credit lines by U.S. firms. We further investigate the role of repayments
for bank stock returns in Section 6.
3.3. Methodology
To show that balance-sheet liquidity risk affects the cross-section of bank stock returns, we
run the following ordinary-least-squares (OLS) regressions:
𝑟! = 𝛼 + 𝛾 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑅𝑖𝑠𝑘! + ∑ 𝛽 𝑋! + 𝜀! (1)
We compute daily excess returns (𝑟! ), which we define as the log of one plus the total return on
a stock minus the risk-free rate defined as the one-month daily Treasury-bill rate. 𝛾 is our
coefficient of interest. As explained in the Introduction, our null hypothesis is that investors
price liquidity risk according to their expectations regarding the possible credit line drawdowns
during crises. However, these expectations might naturally deviate from realized drawdowns in
times of stress. Larger stock price declines of banks with higher ex-ante exposure to drawdown
risk during COVID (i.e., 𝛾 < 0) can therefore be intrepreted as reflecting the difference between
expected and realized drawndown risk. X is a vector of control variables measured at the end
of 2019 and captures key bank performance measures (capitalization, asset quality,
profitability, liquidity and investments) that prior literature has shown to be important
determinants of bank stock returns (e.g., Fahlenbrach et al., 2012; Beltratti and Stulz, 2012).
All variables, including control variables, are described in detail in Appendix III and are shown
in the regression specifications in the sections below. Standard errors in all cross-sectional
regressions are heteroscedasticity robust.
3.4. Descriptive evidence
We first investigate graphically whether differences in ex-ante liquidity risk (measured as of
Q4 2019) across banks can explain their stock price development since the outbreak of
COVID-19. We classify banks into two categories based on high or low balance-sheet liquidity
risk using a median split of our Liquidity Risk variable. We then create a stock index for each
12
subsample of banks indexed at January 2, 2020 using the (market-value weighted) average
stock returns of banks in each sample. We repeat this exercise for a median split of
𝑈𝑛𝑢𝑠𝑒𝑑 𝐶&𝐼 𝐶𝑜𝑚𝑚𝑖𝑡𝑚𝑒𝑛𝑡𝑠. The differences in the stock indices using both measures are
shown in Panel A of Figure 3. Bank stock prices collapsed as the COVID-19 pandemic started
at the beginning of March 2020. Consistent with the idea that liquidity risk explains bank stock
returns, we find that banks with higher liquidity risk perform worse than other banks. The
development around March 2020 is almost identical for banks who had high unused credit line
commitments indicating the importance of credit line commitments in our liquidity risk
measure. In Panel B of Figure 3, we plot bank stock returns over the March 1 – March 23, 2020
period cross-sectionally against our measure of Liquidity Risk. The regression line through the
scatter plot has a negative (and statistically significant) slope. That is, banks with higher
Liquidity Risk had lower stock returns in the cross-section of our sample banks.
[Figure 3 about here]
Panel A of Table 1 shows the excess stock returns of the firms in our sample for three
different periods: January 2020, February 2020, and the March 1, 2020 to March 23, 2020
period (i.e., until policy interventions). The average excess return is negative in all periods,
ranging from -7.2% in January 2020 to -47.2% during the period March 1, 2020 to March 23,
2020 (and cumulatively as low as -66.9% from January 1, 2020 to March 23, 2020). Panel B of
Table 1 shows descriptive statistics of bank characteristics as of Q4 2019.13
[Table 1 about here]
3.5. Multivariate results
The estimation results for regression (1) are reported in Table 2.
[Table 2 about here]
13
In addition to the control variables used in our regression, we also provide summary statistics of Liquidity Risk
and its components. For example, the average Liquidity Risk is 19.5%, the average bank has unused C&I loan
commitments of about 7.7% relative to total assets, and the average wholesale funding–asset ratio is 13.6%. The
average bank has an equity beta of 1.2 measured against the S&P 500 (i.e., it broadly resembles the U.S. economy)
and a capitalization (book equity–to-book asset ratio) of 12%.
13
As a dependent variable we use bank stock returns measured as excess returns in January 1,
2020 to March 23, 2020, i.e., the first phase of the current COVID-19 pandemic and before the
decisive fiscal and monetary interventions. In column (1), we only include Liquidity Risk and
Equity Beta (defined as a firm’s equity beta times the realized market return) and show that
banks with a higher ex-ante balance-sheet liquidity risk and (as expected) higher beta have
lower stock returns during this period. When we add the different control variables, the
coefficient of Liquidity Risk becomes, if anything, economically stronger and the explanatory
power of the regressions almost doubles from column (1) to column (6). Economically, a one-
standard-deviation increase in Liquidity Risk reduces stock returns during this period by
between 4.9 pp and 8.4 pp (which is 12.5% of the unconditional mean return).
A possible concern is that liquidity risk through the provision of credit lines is correlated
with bank portfolio composition. As credit-line drawdowns in a time of stress tend to come
from riskier borrowers or those most in need of liquidity, banks facing larger drawdowns may
be engaged with riskier borrowers or industries and firms more vulnerable to financial and
economic crises. Flexibly controlling for industry and risk composition of bank portfolios is
therefore essential for isolating the effect of credit-line exposure on bank stock returns.
Another confounding factor during the pandemic-onset stress of March 2020 could be a
large exposure to the real estate sector (as measured using a Real Estate Beta), large security
warehouses as banks act as dealer banks (Current Primary Dealer Indicator), or larger
derivative portfolios (Derivates/Assets). Our regressions show, however, that stock returns do
not load significantly on these factors (columns (3) to (4)) once these exposures are accounted
for.
It could also be that those banks with high unused C&I credit lines are also those with
high retail credit card commitments and consumer loan exposures. Given the potential stress
induced by the pandemic in the retail sector due to, e.g., lay-offs and furloughs, these borrowers
might have higher liquidity needs. We collect each bank’s exposure to off-balance-sheet credit
14
card commitments and add this as a control variable to our regression model. This variable does
not enter significantly in our regression (column 5); more importantly, the coefficient on
Liquidity Risk remains unchanged. Using on-balance-sheet Consumer Loans/Assets does not
change our results either. We also include in column (5) the NPL/Loan-ratio as a comprehensive
measure of portfolio risk as well as control for a bank’s distance-to-default as banks with more
non-performing loans and lower distance-to-default tend to have lower stock returns during
stress. We also include Idiosyncratic Volatility measured as the residual from a market model
as banks with higher idiosyncratic volatility tend to have lower stock returns in stressed times.
In column (6), we further add SRISK/Assets as a measure of a bank’s systemic risk at the end
of 2019.14
Importantly, the coefficient on Liquidity Risk remains consistent, even after accounting
for other bank attributes. Moreover, Liquidity Risk is economically the most important
determinant of bank stock returns at the beginning of the COVID-19 pandemic and accounts
for 15% of the variation in bank stock returns, whereas Equity Ratio explains just 1%, indicating
bank leverage does not drive the underperformance of bank stock returns.15 Next, we analyse
the impact of bank portfolio composition in further detail, especially exposure to industries hit
hardest by the COVID-19 pandemic.
3.6. Bank portfolio composition: Exposure to COVID-19-affected industries
Examining the impact of portfolio composition on bank stock returns is complex due to limited
public data on bank portfolios. Echoing Acharya and Steffen (2015), who inferred bank
exposure to sovereign risk via stock return sensitivities to sovereign bond returns, we leverage
market data to discern banks' exposure to industries hit hard during the COVID-19 pandemic.
14
SRISK is a bank’s capital shortfall over a six-month period in a stress scenario, which is a decline in the S&P
500 of 40%, similar to what we observed in March 2020. Banks with higher systemic risk have lower stock returns
during aggregate shocks (such as the pandemic).
15
We interact Liquidity Risk also with measures of bank size and do not find any evidence that, for example,
bailout-expectations of larger banks are reflected in bank stock returns during the pandemic. Somewhat
mechanical, we find that the effect is muted for banks with more available liquidity.
15
Using industry definitions from sources such as Fahlenbrach et al. (2021), which lists the 20
most impacted industries by March 23, 2020, we form 12 different stock-return indices of these
affected industries. Through multifactor models, we gauge bank exposure by assessing stock
return sensitivities (betas) to these respective indices for 2019, terming these as “Affected
Industries (𝛽"#$%& )”. These serve as controls in our regression analysis for bank portfolio
composition. Details and methodologies are expanded upon in Appendix IV and Table 3.
The results are reported in columns (1) to (12) of Table 3 including all control variables.
The negative coefficient on all 12 betas shows that banks with larger exposures to industries
particularly affected by the pandemic had lower stock returns over the January 1, 2020 to
March 23, 2020 period. Importantly, the coefficient of Liquidity Risk hardly changes once
exposure betas are controlled for. The pairwise correlation between the exposure betas ranges
from 0.2 to 0.8 (i.e., they are far from perfectly correlated). The correlation between Liquidity
Risk and our exposure betas is, on average, 0.2, reducing concerns regarding possible spurious
correlations. To reduce the dimensionality of the data associated with 12 different exposure
betas, we also use their first principal component. In column (13), we use the first principal
component (PC1) instead of the exposure beta in our regression and find results consistent with
the interpretation that balance-sheet liquidity risk is a key driver of bank stock returns at the
beginning of the pandemic, independent of the effect of bank portfolio exposures to COVID-19-
affected industries.
[Table 3 about here]
Syndicated loan exposures. Another way to assess banks’ exposure to COVID-19-affected
industries is to use exposures via syndicated corporate loans sourced from Refinitiv Dealscan,
which provides information about originating banks, firms and loan amounts, among others.
We can thus construct a proxy for each bank’s exposure to firms in the affected industries based
16
on the 12 methods mentioned above.16 This variable is called “Loan Exposure/Assets” and we
scale all exposures by a bank’s total assets.
We use these exposures in three steps: First, we construct an average exposure to affected
industries (Loan Exposure/Assets) based on the 12 different methods and correlate Loan
Exposure/Assets with PC1 (the first principal component of our exposure betas). The
correlation is 26% and is significant at the 1% level, suggesting that our exposure betas at least
in part reflect syndicated loan exposures but also that banks are exposed to COVID-19-affected
industries not only through their syndicated loan portfolio. Second, we include
Loan Exposure/Assets instead of the exposure betas in our regression. The results are reported
in column (14). Banks with larger syndicated loan exposures to affected industries experience
lower stock returns, but the coefficient on Liquidity Risk remains (again) almost unaffected.
Third, we run the regressions using the individual loan exposures (always scaled by total assets)
constructed using the different methods and obtain similar results. They are omitted for brevity
but available upon request.
Overall, these results suggest that liquidity risk from undrawn credit lines appears to be
almost orthogonal to bank portfolio risk in terms of its adverse effect on bank stock returns
during the pandemic’s onset.
4. Balance-sheet liquidity risk and bank stock returns: Robustness and extensions
The pandemic began in Asia in January 2020 and hit Western economies by mid-February
2020, culminating in stringent lockdowns by March. With corporate bond markets freezing,
firms urgently sought liquidity, triggering a surge in credit line usage (Figure 1). We aim to
understand how liquidity risk influenced bank stock returns in these phases of the onset and, in
16
We allocate loan amounts among syndicate banks following the prior literature (e.g., Ivashina, 2009). The loan
share of each bank is available for only 25% of loans. We can thus use a limited set of exposure based on these
shares, or allocate the full loan amount to each lender or 1/N of the loan amount, where N is the number of banks
in the syndicate. As we are not interested in the exact exposure of each bank but rather the relative exposure across
lenders, all methods provide similar results.
17
particular, how undrawn C&I credit lines compared to wholesale funding in this influence. We
also investigate the effect of policy interventions.
4.1. Balance-sheet liquidity periodically explains bank stock returns
Panel A of Table 4 shows the estimation results from equation (1) separately for three periods:
the coefficient estimates for January 2020 are shown in columns (1) and (2), February 2020
estimates are in columns (3) and (4), and those for March 1, 2020 to March 23, 2020 are in
columns (5) and (6).
[Table 4 about here]
While Liquidity Risk also somewhat explained stock returns at the time of the initial outbreak
in Asia in January 2020, the economic magnitude of the impact is much smaller than that during
the March 1 to 23, 2020 period. A one-standard-deviation increase in Liquidity Risk decreases
stock returns by about 0.9pp in January 2020, compared to 6.5pp during the March period. The
coefficient of interest is close to zero in February 2020 and increases to -0.462 (March 1, 2020
to March 23, 2020). At the same time, the R2 increases by about 65% suggesting that Liquidity
Risk has substantially more explanatory power after COVID-19 broke out in the Western
economies. In the light of our main hypothesis, this suggests that actual drawdowns only
deviated significantly from expected drawdowns in March 2020. From Panel B of Figure 1, we
had already seen that massive drawdowns only happened in March, supporting this argument
for why liquidity risk is priced (much) more in March 2020 than it was in February or January.17
4.2. Components of liquidity risk and bank stock returns
In the next step, we split Liquidity Risk into its components, viz., C&I credit lines and wholesale
funding, to investigate their differential impact on bank stock returns during the first phase of
the pandemic. The results are reported in Panel B of Table 4.
17
We provide supporting evidence in the Online Appendix based on time-series regressions that relate daily
aggregate drawdowns to bank-level stock returns.
18
We first include only Unused C&I Loan Assets (column 1), then only Liquidity/Assets
(column 2), and then only Wholesale Funding/Assets (column 3), in the regression model. In
columns (4) and (5) we add the components sequentially. Two results emerge: First, the size of
the coefficients and the R-squared in the different regressions suggest that Unused C&I Loans
/ Assets is the most important component in explaining banks’ stock returns at the beginning of
the COVID pandemic. Specifically, a one-standard-deviation rise in unused C&I loans led to a
roughly 5.5pp drop in stock returns. Liquidity / Assets is also statistically and economically
significant: a one-standard-deviation increase led to a 5.2pp increase in stock returns.18
However, Wholesale Funding / Assets is statistically insignificant. Second, the size of the
coefficients of all three variables does not change much when we include them simultaneously
(see column (5)) suggesting that these variables are not highly correlated.19
4.3. The importance of wholesale funding
During the 2008-2009 financial crisis, fears about the banking sector's health led to significant
withdrawals by uninsured wholesale creditors of banks, causing funding liquidity risks for
banks. However, during the COVID pandemic, the banking sector's health was not a primary
concern. Our tests below offer further insights into the role of wholesale funding on bank stock
returns during the pandemic.
We include two different measures for Wholesale Funding in our specifications, one
from Acharya and Mora (2015), abbreviated as AM, and the other one from Dubois and
Lambertini (2018), abbreviated as DL.20 We report these results in Panel C of Table 4. In
18
Our results suggest that credit lines are not similar to term loans regarding to their implications for bank stock
returns. For example, the coefficient on Unused C&I Loans/Assets in Column (5) of Panel B in Table 4 is -1.084,
which is about 2.5 times the size of the coefficient on Loans/Assets. That is, shareholders appear to price the
exposure to aggregate drawdown risk over and above credit risk associated with term loans.
19
We examine the correlations between key variables. For instance, the correlation between Unused C&I
Loans/Assets and Wholesale Funding/Assets is -12% in our bank sample. A t-test comparing banks with above-
median and below-median Wholesale Funding/Assets ratios reveals no significant difference in their average
Unused C&I Loans/Assets. This suggests no clear relationship between access to wholesale funding and banks'
decisions to underwrite credit lines.
20
The key differences between both measures are: The DL measure does not include large time deposits nor
subordinated debt. In contrast to AM, it adds commercial paper. A minor difference is that DL measure splits other
borrowed money by maturity (< and >= 1 year) and differentiates between repos and fed fund purchased.
19
columns (2) and (3), we use the AM and DL wholesale funding proxies. In column (4), we
include the individual components. The wholesale funding proxies are both insignificant during
these crises. Unused C&I Commitments / Assets are economically more meaningful than
wholesale funding components in the COVID period. Interestingly, Large Time Deposits /
Assets negatively impacts bank stock returns, likely because they are uninsured and can thus
quickly be withdrawn. Overall, wholesale funding does not appear to substantially affect bank
stock returns during COVID.
4.4. Liquidity risk and bank stock returns after policy interventions
During the early stages of the COVID-19 pandemic, balance-sheet liquidity risk significantly
influenced bank stock returns. However, after the Federal Reserve's interventions on March 23,
2020, capital market funding was swiftly restored, pausing credit-line drawdowns for most
firms except the riskiest (Acharya and Steffen, 2020a). We thus explore the impact of liquidity
risk on bank stock returns post-Fed actions in this section.
Panel D of Table 4 outlines bank stock returns in 2020: a 51% drop in Q1, a 10% rise in
Q2, an 8% fall in Q3, and a 35% increase in Q4 (during significant events like the U.S. elections
and vaccine introductions). Overall, bank stocks ended the year 4% lower.
Panel E of Table 4 shows the results from panel regressions of bank stock return on
Liquidity Risk (columns (1) and (2)) and its components (columns (3) and (4)) with and without
quarter fixed effects over the post-intervention period, i.e., Q2 to Q4 2020 period. Standard
errors are clustered in these regressions at the bank level. While the coefficient on Liquidity
Risk is close to zero, the coefficient on Unused C&I Loans is small and only significant at the
10% level in a model with quarter fixed effects. We split the sample into the three different
quarters, and find that, while the coefficient on Liquidity Risk is close to zero in Q2 and Q4
2020 (columns (5) and (7)), liquidity risk appears to become a concern again in Q3 (column
(6)) when stock prices of banks declined amid a possible second wave of COVID-19 and
lockdown measures. Taken together, banks with high liquidity risk experienced a stock price
20
decline during the first phase of the COVID-19 pandemic as well as the second wave but
recovered after the considerable monetary and fiscal interventions as well as vaccine arrivals.
5. Understanding the mechanisms: Funding versus bank capital
In this section, we investigate the mechanisms driving the effect of balance-sheet liquidity risk
on bank stock returns during the COVID-19 pandemic. Does funding liquidity to source new
loans become a binding constraint for banks whose deposit funding dries up (the “funding
channel”)? Or, does the drawdown of credit lines lock up bank capital and impair bank loan
origination, preventing banks from making possibly more profitable loans (the “capital
channel”)? And, what are the credit implications for firms borrowing from banks with large ex-
ante credit line exposures?
5.1. Net versus gross credit-line drawdowns and bank stock returns
To distinguish between the funding and the capital channels in how credit line drawdowns
affect intermediation by banks and their stock returns, we construct two measures based on
actual drawdowns experienced by our sample banks during the first quarter in 2020. Gross
Drawdowns is defined as the change of a banks’ off-balance-sheet unused C&I loan
commitments between Q4 2019 and Q1 2020 relative to total assets using FDIC’s Call Report
data. We construct a second proxy, Net Drawdowns, which is defined as the change in banks’
unused C&I commitments minus the change in deposits, in percentage of total assets, over the
same period. Holding gross drawdowns fixed, our measure of net drawdowns helps us
understand the importance of changes in bank deposits on bank stock returns. In other words,
Gross Drawdowns proxies for the importance of drawdowns per se which encumber capital,
while Net Drawdowns is a proxy for the importance of bank deposit funding which affects its
ability to meet drawdowns; therefore, the measures help us identify the relative importance of
the capital versus the funding channels.21
21
The correlation between Gross Drawdowns and Net Drawdowns of our sample banks is below 10% and
statistically insignificant at the beginning of the COVID-19 pandemic, addressing potential concerns that we are
measuring the same economic effect with both variables.
21
We plot the time-series of both measures since Q1 2010 in Figure 4. Panel A shows the
evolution of Gross Drawdowns. While Gross Drawdowns have been relatively stable since
2015, we observe a sudden increase by about 13.5% from Q4 2019 to Q1 2020. As observed
for banks’ off-balance-sheet levels of unused C&I loans, Gross Drawdowns had already
reverted back to pre-COVID-19 levels by the end of Q2 2020.
[Figure 4 about here]
Panel B of Figure 4 displays the development of Net Drawdowns since Q1 2010. Net
Drawdowns have been relatively stable since 2015 and in fact decreased by about 5% in Q1
2020. In other words, the change in deposits during the first quarter of 2020 has been larger
than the change in unused C&I commitments, suggesting that funding of new loans should not
have been a binding constraint for banks. Similar to gross drawdowns, net drawdowns also
returned to pre-COVID-19 levels over the next two quarters (in Q3 2020).
[Table 5 about here]
We investigate the effect of gross and net drawdowns on bank stock returns formally
using the model specification and control variables from column (5) of Table 2. Table 5 reports
the results. We introduce both proxies sequentially in columns (1) and (2) and then together in
column (3). The coefficient of Net Drawdowns is small and insignificant, while the coefficient
of Gross Drawdowns is statistically significant and economically meaningful (column (2)). A
one-standard-deviation increase in Gross Drawdowns reduces bank stock returns by about
4.8pp (= -5.128 × 0.0094), which is economically large and corresponds to approximately 10%
of the unconditional stock price decline. When we include both proxies in column (3) we find
that, holding Gross Drawdowns fixed, Net Drawdowns still has no significant effect on bank
stock returns. That is, since the variation in Net Drawdowns is driven by changes in bank
deposits (holding Gross Drawdowns fixed), funding of drawdowns through bank deposits does
not appear to be a binding constraint for banks during the pandemic drawdowns. Finally, adding
SRISK/Assets as additional control (column (4)) does not change the coefficient of Gross
22
Drawdowns, suggesting that SRISK likely does not seem to capture systemic implications
associated with aggregate credit-line drawdowns (a point we will revisit later).
We interact Gross Drawdowns with High Capital, an indicator equal to 1 if bank equity
capital is above the median of the distribution (column (5)). In column (6), we observe the
interaction between Gross Drawdowns and Capital Buffer, which is the difference between a
bank’s equity–asset ratio and the cross-sectional average of the equity–asset ratio of all sample
banks in Q4 2019. A larger difference implies that a bank has a higher capital buffer. The
coefficient of both interaction terms is positive and statistically significant emphasizing that the
negative effect of drawdowns on stock returns is attenuated for banks with better capitalization.
Consistently, the coefficient of the interaction term of High Capital (Capital Buffer) and Net
Drawdowns is not significant (columns (7) and (8)). Columns (9) and (10) confirm these results
including interaction terms of High Capital (Capital Buffer) with both Gross Drawdowns and
Net Drawdowns.22
Overall, we infer that balance-sheet liquidity risk of banks affects their stock returns as
the manifestation of such risk in the form of credit line drawdowns locks up bank capital away
from more profitable investment opportunities. In the next section, we investigate this
mechanism directly focusing on the impact of credit line drawdowns on corporate bank lending.
5.2. Implications for bank lending during the COVID-19 pandemic
We now explore a testable hypothesis that banks with more balance-sheet liquidity risk reduced
their credit supply during 2020 by a greater extent than other banks. In particular, if banks’
capital constraints matter, then we expect lending to be particularly sensitive to gross (but not
to net) drawdowns.
We use data from Refinitiv Dealscan to investigate these issues. We use data on both
outstanding exposures and new loan originations from January 2019 to October 2020 and divide
22
Robustness tests with other liquidity proxies and time windows are documented in Online Appendix E.
23
our sample into a “pre” and “post” period, where the post-period is defined as the period starting
April 1, 2020 (Q2 2020), i.e., during the COVID-19 pandemic. In unreported tests, we collapse
our sample at the bank × month level and show that banks with higher Liquidity Risk and higher
Gross Drawdowns decrease lending in the post-period relative to the pre-period and relative to
banks with lower exposures using bank and month fixed effects. Net Drawdowns have no effect
on lending. Banks reduce lending especially to riskier borrowers, consistent with the higher
capital requirements associated with these loans. However, while these tests are promising they
do not allow us to control for loan demand. A plausible alternative explanation could be a
reduction in loan demand due to lower investments by riskier firms in a period characterized
by high uncertainty or because riskier borrowers have already drawn down existing lines of
credit. Another alternative explanation for a reduction in lending could be a loss of
intermediation rents due to the low-interest-rate environment.
Methodology. We use a Khwaja and Mian (2008) estimator to formally disentangle
demand and supply in a regression framework, investigating the change in lending of banks to
the same borrower before and after the outbreak of the COVID-19 pandemic. We construct two
variables, Exposurei,b,m,t, which is the natural logarithm of the outstanding loan amount issued
to firm i by bank b as loan-type m as of quarter t, and Originationi,b,m,t, which is the natural
logarithm of the newly issued loan amount to firm i by bank b as loan-type m in quarter t. We
estimate two primary model specifications. We first use Exposurei,b,m,t as the LHS (Y) variable
and absorb time-varying (and loan-type specific) loan demand using borrower (𝜂! ) × time (𝜂" )
× loan type (𝜂# ) fixed effects. Moreover, we saturate the specification with borrower (𝜂! ) ×
bank (𝜂$ ) fixed effects to measure changes in credit supply within a borrowing relationship
thereby controlling for (time-invariant) portfolio composition effects. Lastly, we add bank
lending controls following prior literature (𝑋$,"&' : NPL ratio, log of total assets, ROA, Tier-1
capital ratio, loan-to-assets ratio) giving us the specification:
𝑌!,(,),* = 𝛽+ × 𝐷𝐷( × 𝑃𝑜𝑠𝑡 + J𝜂𝑖 × 𝜂𝑡 × 𝜂𝑚 K + J𝜂𝑖 × 𝜂𝑏 K + 𝑋𝑏,𝑡−1 + 𝜀𝑖,𝑏,𝑚,𝑡
24
In a second model, we use Originationi,b,m,t as the Y-variable and restrict our sample to one pre-
(Q4 2019) and one post period (Q2 2020).23 We then directly compare the issuance behaviour
between these two points in time, while again controlling for time-varying loan demand and
measuring the lending impact within a credit relationship through fixed effects. In all our
specifications, we cluster standard errors at the bank level.
A negative 𝛽+ implies that a bank with more exposure to drawdown risk (𝐷𝐷( ) –
measured as either Gross Drawdowns or Net Drawdowns – decreases lending more than banks
with less exposure during the COVID-19 pandemic after controlling for loan demand and other
bank- and loan-specific effects. Gross Drawdowns and Net Drawdowns are measured over the
Q1 2020 period. To detect potential non-linearities in the reaction of banks’ lending behaviour
to the level of drawdown risk, we further create two dummy variables that take the value 1 if
the Gross (Net) Drawdowns of a bank are above the median of Gross (Net) Drawdowns of all
banks in the sample (High Gross (Net)). Finally, we consider both term loan and credit line
exposures and originations. While a reduction in term loans is consistent with banks
experiencing a shock to their capital, a reduction in credit line originations might be consistent
with the interpretation that banks have learned from COVID-related drawdowns.24
Results. The results are reported in Table 6. Columns (1)–(4) show the results with
Exposurei,b,m,t, and columns (5)–(8) with Originationi,b,m,t as dependent variables.
[Table 6 about here]
Columns (1) and (2) show that banks with large gross drawdowns (also accounting for possible
non-linearities in column (2)) do not adjust their loan exposure to firms differently from banks
with low gross drawdowns after COVID-19 broke out. We then differentiate by loan type and
23
This approach is similar to the one used in Kapan and Minoiu (2021).
24
Our analysis diverges from Greenwald et al. (2023) who emphasize macroeconomic aggregates and
distributional impacts of credit line drawdowns on firms lacking such access. Instead, we delve into the broader
lending behavior of banks and the effects of credit line drawdowns on the supply of both credit lines and term
loans. Supporting this, both Chodorow-Reich et al. (2022) and Greenwald et al. (2023) demonstrate that credit-
line drawdowns by large firms led banks to reduce lending to smaller firms, possibly due to capital constraints.
Furthermore, our Online Appendix B indicates increased loan spreads for small firms in secondary markets since
the pandemic's onset, underscoring reduced intermediation for those reliant on bank financing.
25
find that banks with high gross drawdowns increase credit-line exposures relative to low gross
drawdown banks during COVID-19, consistent with the interpretation that these banks can
sustain off-balance-sheet rather than on-balance-sheet exposures as the former require less
upfront equity capital. Also consistent with the bank capital channel, we find that banks with
high gross drawdowns actively reduce term-loan exposures relative to low gross drawdown
banks as the triple interaction term in column (3) suggests (for example, by actively selling term
loans or by not rolling them over). In column (4) we add lagged control variables, to further
account for compositional differences of the treatment and the control group. The size and
significance of the effects described above remain unaffected.
Columns (5) to (8) show the results for new loan originations. Similar to before, banks
appear to be concerned about their loan portfolio size once drawdowns become large (relative
to the sample median). Banks with high gross and net drawdowns both reduce new loan
originations compared to low drawdown banks and they reduce both credit lines and term loans
as the coefficients on the triple interaction terms are insignificant (column (7)). Once we include
our control variables, the effect of net drawdowns becomes insignificant. That is, holding the
effect of deposit inflows constant, banks with larger impact on equity capital through large
credit-line drawdowns reduce lending more than other banks, highlighting the relative
importance of the capital channel in relation to the funding channel during COVID-19.
5.3. Real effects for firms borrowing from high gross drawdown banks
How do firms respond to the contraction of lending supply? We focus on a subsample of
publicly listed borrowers in Refinitiv Dealscan that can be matched to Compustat and loan
exposures as of Q4 2019. For every firm, we calculate the weighted average of gross
drawdowns across its syndicate lenders, where the weights are the size of the loan exposure of
each lender to this firm. We then construct an indicator that takes the value one if this average
drawdown share is above the median of its distribution across firms. These firms borrow from
high gross drawdown banks in our terminology.
26
Within the short period of time in the post-COVID-19 phase that is part of our sample
period, significant shifts in slow-moving variables such as assets or investments are unlikely,
and we do not find significant differences investigating these variables. However, firms can
quickly make changes to their working capital requirement and respective funding needs. In
unreported tests, whose results are available upon request, we find (using simple mean
differences) that firms which borrow from banks with high gross drawdowns increase current
assets less relative to those firms borrowing from low drawdown banks, but current liabilities
are unaffected. That is, these firms reduce the necessary investments in working capital, likely
because access to bank loans becomes more difficult, as demonstrated above. Moreover, these
firms reduce their R&D expenditures (relative to total assets) four times as much compared to
unaffected firms. Given the importance of R&D for innovation and competition, even a short-
term reduction in R&D expenditure might adversely impact these firms over the long run. Firms
might also make immediate changes in their payouts to shareholders. We obtain data on payouts
from Capital IQ for our sample firms. While we do not find a significant differential effect on
stock repurchases, we find that affected firms borrowing from banks with high gross
drawdowns significantly reduce dividend payouts (the reduction is twice as large compared to
non-affected firms).
6 The value of credit line repayments
Our previous results suggest that bank stock prices did not recover fulls by end of 2020 from
the Q1 2020 correction and substantially underperformed those of non-financial firms even in
the post-intervention period. In this section, we propose a two-sided “credit-line channel” to
make sense of the stock price performance of banks during this period. Importantly, credit lines
provide firms with two options, an option to draw from the credit line, but also an option to
repay (or not repay) the part of the credit line they have already drawn down. Understanding
the value of the repayment option for banks appears crucial in this context.
27
6.1. Methodology
The value of the repayment option for the bank is the difference between the revenue it
generates if the credit line remains drawn (fees, interest rate) and the revenue of alternative
investments it could undertake with the repaid amount. For a fair assessment of the revenue of
alternative investments, this investment should carry the same risk and regulatory capital cost
(e.g., a corporate bond with the same rating as the credit line borrower). Our hypothesis is
therefore that banks should benefit less from repayment if the fee structure of their drawn credit
lines being repaid, compared to the refinancing costs of the underlying borrowers, is
comparatively high, and vice versa if fees are relatively low.
We construct a new variable FeesEarned as a proxy for the option value of firm
repayment for the bank. This variable is defined as
𝐹𝑒𝑒𝑠𝐸𝑎𝑟𝑛𝑒𝑑! = ∑" ,-AI𝑆𝐷"! − (R# + RP" )8 ∗ DrawdownVolume"! ∗ 8%H
and scaled by total commitments, where 𝑗 is a bank and 𝑖 is a borrowing firm of bank j. It sums
up the return or all-in-drawn spread (𝐴𝐼𝑆𝐷!, ) on the capital deployed (𝐷𝑟𝑎𝑤𝑑𝑜𝑤𝑛𝑉𝑜𝑙𝑢𝑚𝑒!, ∗
8%) for each credit line borrower, adjusted for the opportunity costs – the risk-free rate (R - )
plus a risk premium (𝑅𝑃! ) which is rating-specific – that the banks could earn from investing
the freed-up capital into another interest-bearing asset. We measure the term 𝑅. + 𝑅𝑃! as the
secondary market bond yield for corporate bonds in the same rating category as borrower i.
Importantly, this measure depends on the drawn amount of the credit line (not the undrawn
amount), i.e., the bank earns the all-in-spread-drawn (AISD) paid by the borrower and not the
commitment fee (AISU). We use the rating as a proxy for freed-up capital as we lack detailed
information on the actual risk weights applied by banks on their credit lines to individual
borrowers.
This variable allows us to compare for two banks with the same volume of drawdowns
and the same level of AISD charged to borrowers, how much equity capital is being freed-up.
Suppose there are two banks A and B, both experience 100 million USD in drawdowns
28
(DrawdownVolume) and earn from their borrowers 5% AISD (as per the ex-ante contract). The
only difference in the FeesEarned variable then comes from a difference in 𝑅𝑃! , which
translates to different capitalization levels as capital requirements are risk-sensitive. For
example, assume that the borrowers of bank A have a higher credit rating than the borrowers
of bank B. If the risk-free rate (R. ) is zero and the risk premium (𝑅𝑃! ) for higher credit ratings
is 2%, and for lower credit ratings is 4% then FeesEarned is 3% for bank A and 1% for bank
B. Hence, bank A (B) gains an additional 3 (1) percentage points per unit of capital on the
drawn credit line compared to investing the freed-up capital in a comparable investment. Our
hypothesis is that the value of the borrowers’ repayment option for bank A is lower than for the
bank B, because bank A loses the same amount of revenue (5% AISD) but effectively gets less
risk-adjusted capital freed up. In line with the analysis of the 2020Q1 period, this is our measure
for the capital channel, while the ratio of repayments to committed amounts (Repayments)
serve as the measure for the funding channel. Thus, FeesEarned incorporates the opportunity
cost for banks when borrowers draw down credit lines, and interacting it with the repayments
ratio captures the differential value of repayment given these opportunity costs.
6.2. Empirical results
We first look at summary statistics related to credit line repayments by rating category in Panel
A of Table 7. We find that borrowers with higher credit ratings repay more compared to
borrowers with lower credit ratings, both in the second and the third quarter of 2020, relative
to their previous drawdowns. In terms of repayment relative to the overall committed volume,
better-rated borrowers repay more in the second quarter (i.e., earlier) and worse-rated borrowers
more in the third quarter (i.e., they repay later). Overall, we see that there are significant
differences in the repayment behavior of firms by rating category. Since rating categories matter
for the deployed bank capital, it is a testable hypothesis that this heterogeneity at the firm-level
aggregates up to the bank level and affects banks’ stock returns.
[Table 7 about here]
29
To test this hypothesis, we estimate the following regression specification in OLS:
𝑟!* = 𝛽+ ∗ 𝐹𝑒𝑒𝑠𝐸𝑎𝑟𝑛𝑒𝑑! + 𝛽/ ∗ 𝑅𝑒𝑝𝑎𝑦𝑚𝑒𝑛𝑡𝑠! +
𝛽0 ∗ 𝑅𝑒𝑝𝑎𝑦𝑚𝑒𝑛𝑡𝑠! 𝑥 𝐹𝑒𝑒𝑠𝐸𝑎𝑟𝑛𝑒𝑑! + 𝐷𝑟𝑎𝑤𝑑𝑜𝑤𝑛𝑠 2020𝑄1! + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠!* + 𝑢!*
We control for Drawdowns2020Q1, i.e., the drawdowns in Q1 2020 scaled by total assets. We
also include a set of controls variables such as equity beta, capitalization levels, systemic risk,
and business model proxies (unreported). Panel B of Table 7 summarizes the results of the
above baseline specification and further tests regarding repayments. Column (1) measures the
impact of FeesEarned as well as Repayments on stock returns. Our results show that banks that
can earn higher fees on their credit lines, adjusted for the borrower’s rating category, perform
better during the second quarter of 2020. Conditional on the level of Q1 drawdowns and the
fees earned on those drawdowns, credit line repayments appear to be positive for banks. In other
words, repayments matter both for the capital and the funding channel. A one standard-
deviation increase in FeesEarned translates to an 8.9 pp increase in the stock return, while an
additional standard deviation of Repayments increases the stock return by 5 pp. The average
stock return in the second quarter of 2020 is 23.5%. That is, these economic magnitudes are
sizeable.
In column (2), we add an interaction term between FeesEarned and Repayments. As
explained earlier, the higher the fees a bank earns, the lower its benefit from repayment. The
results confirm this hypothesis with a negative sign for the interaction term. Next, if repayments
are the reversal of drawdowns, then the higher the market value loss during COVID (the higher
the sensitivity to the aggregate drawdown risk), the more a bank should benefit from repayment,
e.g., because the higher market value loss reflects a tighter capital constraint as we document.
To test this hypothesis, we further interact Repayments with the market value loss during the
first quarter of 2020. We add this to the regression in column (3). The interaction term is
positive and highly significant, while all other coefficients remain largely unchanged. That is,
30
repayments increase a bank’s stock return more if it had lost more of its market value at the
beginning of the COVID-19 pandemic.
Similarly, if the capital channel is the main driver of the market value loss in Q1, then
we conjecture that the recovery depends also on the capital levels. We test this hypothesis in
column (4) by interacting Repayments with the Capital Buffer. Stock returns in Q2 significantly
depend on the interaction of capital levels and repayments. The lower the capital level in Q1,
the more a bank profits from repayments. In column (5), we interact Repayments with Q1
drawdowns. The interaction term turns out to be insignificant in both specifications. In column
(6), we run a horse race between all interaction terms described above. The market value loss
and capital buffer interactions remain significant and prove to be the most important
determinants in understanding the importance of repayments for banks’ stock return.
In summary, we document the importance of a two-sided “credit-line channel” for bank
stock returns. While correlated credit line drawdowns negatively affect banks’ performance,
the cash flows generated by the fees and the drawdown interest rate can soften the blow.
Repayments are good for bank stock returns on average because they free up encumbered
capital, but banks prefer to have low (opportunity cost-adjusted) fee credit lines repaid. In the
end, the banks (and their investors) want to be compensated in fees for the opportunity cost and
the exposure to drawdown risk.
7. Discussion
In this section we discuss our results and their extensions along three dimensions: (1) how credit
line commitments affect bank stock returns during and outside crisis periods; (2) whether credit
line spread provide a signal to investors regarding aggregate drawdown risk; and, (3) whether
banks change their credit line lending behaviour following the drawdowns in Q1 2020.
7.1 Credit line commitments and bank stock returns in and out of crises
Is the aggregate drawdown risk of banks priced in bank stock returns more generally or is it
priced only in crises periods? And, do investors get compensated in normal market times for
31
lower returns under aggregate stress? To answer these questions, we regress quarterly bank
stock returns (𝑟!* ) “through-the-cycle” on credit line commitments over three different samples
in the 1995Q1 to 2021Q1 period: 2019Q4 to 2021Q1 (Covid), 2004Q1 to 2011Q4 (GFC) and
2000Q1 to 2002Q4 (Dotcom). We document the results for the COVID pandemc in columns
(1) to (3), for the GFC in columns (4) to (6), and for the Dotcom crisis in columns (7) to (9) in
Table 8, where we estimate the following specification:
𝑟!* = 𝛾 ∗ 𝐹𝐹5* + 𝛿 ∗ 𝐶𝑟𝑖𝑠𝑖𝑠* + 𝛽+ ∗ 𝐶𝑜𝑚𝑚𝑖𝑡𝑚𝑒𝑛𝑡 (𝐻𝑖𝑔ℎ)! +
𝛽/ ∗ 𝐶𝑟𝑖𝑠𝑖𝑠* 𝑥 𝐶𝑜𝑚𝑚𝑖𝑡𝑚𝑒𝑛𝑡(𝐻𝑖𝑔ℎ)! + 𝑢*
where FF5 are the five Fama-French factors (Market, Small Minus Big, High Minus Low,
Robust Minus Weak, Conservative Minus Aggressive), Crisis is a dummy variable equal to 1
if the economy is in a recession according to the NBER business cycle dating committee, and
Commitment (High) is a dummy variable indicating if bank volume of committed but undrawn
credit lines is above the median. We construct a matched sample of banks with high and low
cedit-line commitments – defined one quarter before the respective crisis – based on other bank
characteristics (capitalization, NPL-to-loan ratio, asset size and the loan-to-asset ratio). This
ensures that we are comparing stock returns of banks with similar health, size and business
model.
We find that high commitments – and therefore (ex-post) aggregate drawdown risk –
negatively affects stock returns during all crises periods in our sample: Covid, GFC, and
Dotcom. That is, the coefficient 𝛽/ on the interaction term of crises periods and above-median
commitments is negative and significant. Importantly, we find that β+ is positive and
statistically significant in the Covid and the GFC period, that is, investors do get compensated
for aggregate drawdown risk outside of crises periods. Our results thus indicate no evidence for
a complete neglect or mispricing of (aggregate) drawdown risk but are consistent with the
32
intrepretation that investors re-evaluate the implications of (higher than expected) credit line
drawdowns during an aggregate liquidity crunch like the one we observed during the pandemic.
[Table 9 about here]
Across crises periods, the coefficient estimates for 𝛽/ in the Dotcom and GFC episodes
are of very similar magnitudes. The coefficient during the Covid crisis, however, is about 2.5
times larger than the coefficient in the previous two crisis episodes. Similarly, the R-squared
values increase substantially in chronological order from crisis to crisis. This shows that the
impact of aggregate drawdown risk on bank stock returns during periods of stress has increased
with the buildup of credit line volumes, particularly after the GFC.
7.2. Do credit line fees provide investors a signal regarding aggregate drawdown risk?
We follow earlier work on the pricing of credit lines such as Acharya et al. (2013) and Berg et
al. (2016) and build a panel data set of U.S. non-financial firms that have obtained credit lines
in the primary loan market over the 2010 to 2019 period. That is, using all originated loans from
the Refinitiv Dealscan database, we keep only credit lines issued over the sample period, keep
the lead arranger (following the procedures outlined in many previous papers), and collapse the
All-In-Spread-Drawn (AISD) and the All-In-Spread-Undrawn (AISU) at their respective means
at the firm-year-lead-arranger level to construct a panel dataset.
We then use the merged CRSP/Compustat database to add firm characteristics that
affect a firm’s cost of credit, in particular a firm’s equity volatility as a measure of idiosyncratic
risk and a firm’s market beta for systematic risk. Other control variables include size,
profitability, tangibility, Tobin’s Q and leverage. We source bank characteristics from FDIC’s
Call Report data including NPL/Loans, capital, non-interest income, bank size and bank
profitability. Importantly, we also use data from Call Reports, CRSP and the NYU Volatility
Lab to obtain banks’ aggregate risk exposure including Bank Equity Beta (as a measure of
systematic risk), LRMES (as a measure of a bank’s market equity’s aggregate downside risk),
SRISK/Assets (as a measure of equity shortfall in times of an aggregate or market-wide shock),
33
and Liquidity Risk (as a measure of aggregate drawdown risk). LIBOR is included as all
contracts are floating rate and prior literature has shown that spreads and fees are sensitive to
the current level of LIBOR. We estimate the following regression:
𝐶𝑜𝑠𝑡!,,,* = 𝜇1 + 𝜇+ 𝐴𝑔𝑔𝑅𝑖𝑠𝑘,,* + 𝜇0 𝐿𝐼𝐵𝑂𝑅* + 𝜇2 𝑋,,* + 𝜇3 𝑋!,* + 𝛾* + 𝜆4 + 𝜀!,,,*
where 𝐴𝑔𝑔𝑅𝑖𝑠𝑘,,* are bank-specific aggregate risk proxies, 𝑋,,* (𝑋!,* ) are bank (firm)
characteristics, 𝛾* are year and 𝜆4 industry fixed effects. Cost is either the AISD or AISU.
The results are reported in Table 10. We first show that idiosyncratic drawdown risk
(measured using a firm’s realized equity volatility over the past 12 months) and systematic
drawdown risk (measured using a firm’s stock beta) are priced in both commitment fee (AISU)
and spread (AISD). This is consistent with, for example, Acharya et al. (2013) and Berg et al.
(2016). However, while a higher Bank Beta and LRMES both somewhat increase the price of
credit lines, Liquidity Risk or Unused C&I / Assets, on average, do not. Also, SRISK / Assets,
which measures bank capital shortfall in times of aggregate market downturn, does not appear
to be priced in credit line fees. In other words, banks do not appear to be considering the deep
out-of-the-money put option associated with aggregate drawdown risk when setting ex-ante
price terms of credit lines. This may partly explain their need to cut back term loans when
aggregate drawdown risk materializes as their equity capital then gets unexpectedly
encumbered, as witnessed during the pandemic.25
In summary, credit line pricing does not contain perfect or adequate signals regarding
exposure to aggregate drawdown risk that is episodic or in the tails of the distribution. Investors
thus may be unable to adjust their expectations fully regarding credit line drawdowns in periods
of aggregate stress. Overall, these results are consistent with our earlier interpretation that
25
Banks in our study, regardless of their liquidity risk, committed to credit lines, suggesting that matching biases
are more likely influenced by borrower traits than just bank liquidity risk. Our data analysis confirms that
borrowers from banks with varying liquidity risks show no significant differences in credit risk or drawdown
intensity, implying that selection bias is unlikely to impact our findings on credit line pricing.
34
investors have to reprice in response to unexpectedly high drawdowns during periods of
heightened aggregate stress.
7.3 Did banks change their credit line issuance behavior post COVID?
If credit lines turned out to be value-destroying for banks due to unexpectedly large aggregate
drawdowns during COVID, did banks change their issuance behaviour thereafter? We first
investigate descriptively changes in the volume and pricing of credit lines before and after the
COVID-19 pandemic. Panel A of Figure 5 shows the aggregate quarterly issuance volume of
credit lines by U.S. banks in billion U.S. dollar during the Q1 2018 to Q4 2021 period sourced
from Refinitiv Dealscan. The horizontal lines show the mean issuance volume in the pre- and
post-COVID period during this sample period. We observe a temporary decline in credit line
issuances after the start of the COVID-19 pandemic. This is in line with our results documented
in Section 5.2. Issuances, however, recovered sharply after the Q2 2020 period and even
exceeded pre-COVID-19 levels already in Q2 2021. On average, credit lines issuance volumes
have not been statistically (or economically) significantly different between the pre-pandemic
and pandemic periods.
What about the pricing of credit lines? Key pricing terms that banks might adjust to
reflect risks associated with the issuances of credit lines are the all-in-spread-drawn (AISD), the
all-in-spread-undrawn (AISU), which is the commitment fee banks charge to provide the
liquidity commitment, and the upfront fee (UFR). Prior literature has shown that the UFR is
highly cyclical and adjusts fast when economic conditions deteriorate. However, the UFR is
not frequently recorded in our data. Below, we use only those credit lines issuances for which
the UFR is available, when we plot the UFR graph.
In Panel B of Figure 5, we chart the average AISD for credit lines issued between Q1
2018 and Q4 2021 on a quarterly basis. Before the COVID-19 pandemic, the average AISD
hovered around 240bps, with minimal fluctuations. In Q1 2020, the AISD declined as only
high-quality borrowers secured new credit lines. Subsequently, we noted a brief surge in the
35
AISD for new issuances, which then retreated to pre-pandemic levels by Q1 2021. This aligns
with our findings in Section 5.2, suggesting that, temporarily, a reduction in credit supply due
to capital encumbrance led to both diminished volumes and elevated prices. As banks received
more repayments in the latter quarters of 2020, they gradually resumed regular lending
practices. Overall, comparing pre- and post-COVID-19 periods, the AISD averages show no
significant discrepancies. In Panel C of Figure 5, we show the quarterly average AISU (left
panel) and the quarterly average UFR (right panel). Also these pricing measures remain, on
average, unchanged in the post-COVID-19 period.
Overall, both volume and pricing of credit line originations remain unchanged, on
average, in the post-COVID-19 period highlighting that it remained – at least privately –
optimal for banks to issue credit lines to firms. Descriptively, there does not appear to be
evidence in the data that banks regard the issuance of credit lines as a value-destroying activity
or that their assessment as to the riskiness has changed after the start of the COVID-19
pandemic.
8. Addressing aggregate drawdown risk ex-ante using stress tests
We showed that balance-sheet liquidity risk of banks – mainly driven by undrawn credit lines
– has severe implications on their ability to extend new loans because drawn credit lines
encumber capital. How can policymakers address this aggregate drawdown risk in an ex-ante
manner? We suggest incorporating these commitments to better assess capital requirements
during aggregate stress periods by illustrating how to adjust SRISK.
8.1. Methodology
Capital shortfall in a systemic crisis (SRISK). SRISK is defined as the capital that a firm is
expected to need if we have another financial crisis. Symbolically it can be defined as:
𝑆𝑅𝐼𝑆𝐾!,* = 𝐸* (𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑆ℎ𝑜𝑟𝑡𝑓𝑎𝑙𝑙! |𝐶𝑟𝑖𝑠𝑖𝑠)
36
That is,
𝑆𝑅𝐼𝑆𝐾!,* = 𝐸 [𝐾 (𝐷𝑒𝑏𝑡 + 𝐸𝑞𝑢𝑖𝑡𝑦) − 𝐸𝑞𝑢𝑖𝑡𝑦 |𝐶𝑟𝑖𝑠𝑖𝑠]
= 𝐾 𝐷𝑒𝑏𝑡!,* − (1 − 𝐾)(1 − 𝐿𝑅𝑀𝐸𝑆!,* )𝐸𝑞𝑢𝑖𝑡𝑦!,*
where 𝐷𝑒𝑏𝑡!,* is the nominal on-balance-sheet debt of bank i’s liabilities, assumed to be
constant between time t and Crisis over t to t+h. 𝐸𝑞𝑢𝑖𝑡𝑦!,* is bank’s i market value of equity at
time t. LRMES is the Long Run Marginal Expected Shortfall, approximated in Acharya et al.
(2012) as 1 − 𝑒 (6+7×9:;) , where MES is the one-day loss expected in bank i’s return if market
returns are less than -2% and Crisis is taken to be a scenario where the broad index such as the
S&P 500 or MSCI Global falls by 40% over the next six months (h=6m). K is an assumed
required quasi-market-value-to-quasi-market-assets capital ratio of 8%, where quasi-market-
assets is the sum of book debt and market value of equity.26
To account for off-balance-sheet liabilities fully, the necessary adjustments to SRISK can
be broken down into two components. First, off-balance-sheet (contingent) liabilities such as
bank credit lines enter banks’ balance sheets as loans once they are drawn and need to be funded
with capital. Second, we also have to account for the effects of unexpected drawdown risk on
stock returns conditional on stress as demonstrated in our results throughout this paper. We
explain the two components in detail below:
./
i) 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!," = 8% × 𝐸[𝐷𝑟𝑎𝑤𝑑𝑜𝑤𝑛 𝑟𝑎𝑡𝑒 | 𝐶𝑟𝑖𝑠𝑖𝑠] ×
𝑈𝑛𝑢𝑠𝑒𝑑 𝐶𝑜𝑚𝑚𝑖𝑡𝑚𝑒𝑛𝑡𝑠!,"
This is the additional capital needed due to drawdown rates in crises periods. We estimate the
drawdown function with a simple OLS regression between aggregate drawdowns for non-
financial borrowers and the return of the S&P 500 index and define a crisis period as a 40% fall
in the market index.
26
SRISK is based on market equity. That is, if banks fund credit line commitments with some equity, the market
value of equity and LRMES should already reflect it. In other words, we do not need to make further adjustments
when calculating the incremental SRISK needed to adjust for credit line commitments.
37
/0123 !
ii) 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!," = (100% − 8%) × 𝛾I × 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑖𝑠𝑘!," × 𝐸𝑞𝑢𝑖𝑡𝑦!,"
This is the additional equity market value loss due to high drawdowns in stress periods. 𝛾l is the
estimated episodic effect of liquidity risk on bank stock returns on balance-sheet liquidity risk
from our tests.
8.2. Estimating the drawdown function under aggregate stress
To calculate the expected percentage drawdown in a crisis, we use drawdown data from during
the COVID-19 pandemic as well as the GFC crisis and estimate the expected drawdown in a
stress scenario with a 40% market correction for both stressed periods. We show plots of this
exercise in Figure 6.
[Figure 6]
In Panel A of Figure 6, we plot the cumulative quarterly drawdown rates during the COVID-
19 pandemic (i.e., Q4 2019 and Q1 2020) and the GFC (i.e., Q1 2007 to Q4 2009) as a function
of the respective quarterly S&P 500 returns. We also show the linear regression fits for both
periods. In Panel B of Figure 6, we use the lowest cumulative daily S&P 500 return within each
quarter (instead of the quarterly return). This presentation has two advantages. First, it shows
that for quarters with relatively low negative S&P 500 returns (i.e., “normal times”),
drawdowns are somewhat clustered.27 Second, drawdown decisions are arguably based on how
bad a quarter has been within rather than on the situation at the end of each quarter. We therefore
calculate drawdown rates based on Panel B of Figure 6.
We find that the sensitivity of credit-line drawdowns to changes in market returns was
higher during the COVID-19 pandemic (the slope coefficient, 𝛽, is -0.57) compared with the
GFC (the slope coefficient, β, is -0.27). The projected drawdown rate in a market downturn of
40% is thus also substantially higher in the COVID-19 pandemic (39.97% versus 25.79%). A
possible explanation of the differential impact on absolute drawdowns could be that corporate
27
The intercept in the COVID-19 pandemic and the GFC are 17% and 15%, respectively.
38
balance sheets were less impacted during the GFC, which originated in the banking and
household sector. The COVID-19 pandemic, however, had an immediate effect on firms’
balance sheets, resulting in elevated demand for liquidity from pre-arranged credit lines
compared with the GFC. The quarterly drawdown rates in both stress scenarios or crises are
summarized together with the sensitivities of the drawdown rates in a market correction in Panel
A of Table 11.
[Table 11 about here]
8.3. Incremental SRISK due to credit-line drawdowns
Using these expected drawdown rates, we calculate the equity capital that would be required to
fund these new loans based on banks’ unused commitments at the end of Q4 2019
(𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!"= ). We use the Q4 2019 unused credit-line commitments of banks and
apply the drawdown rates calculated in the three different stress scenarios assuming a prudential
capital ratio of 8%:
𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!"= = 𝐷𝑟𝑎𝑤𝑑𝑜𝑤𝑛 𝑟𝑎𝑡𝑒 × 8% × 𝑈𝑛𝑢𝑠𝑒𝑑 𝐶𝑜𝑚𝑚𝑖𝑡𝑚𝑒𝑛𝑡𝑠 (4)
In Panel B of Table 11, we show the top 10 banks with the largest undrawn commitments as of
Q4 2019 and report 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!"= individually for each of these banks. We also report
the total 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾 "= for the top 10 and for all banks in our sample. Overall, we find
that 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾 "= , i.e., the additional capital, amounts to about USD 37.9bn to
USD 58.7bn depending on the estimates of the drawdown rate.
8.4. Incremental SRISK due to MESC and contingent SRISK (SRISKC)
We also account for the effect of liquidity risk on bank stock returns. Using the loadings from
our regressions of bank stock returns on balance-sheet liquidity risk during the COVID-19 crisis
(i.e., the 𝛾 in equation (2)), we estimate the additional (marginal) equity shortfall of banks based
!
on their end of Q4 2019 market values of equity (MV), called the 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!=>9:; :
39
!
𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!=>9:; = (1 − 𝐾) × 𝑀𝑉! × 𝐿𝑅𝑀𝐸𝑆!"
= (1 − 𝐾) × 𝑀𝑉! × 𝛾l × 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑖𝑠𝑘! (5)
where 𝐿𝑅𝑀𝐸𝑆!" is the contingent marginal expected shortfall due to the impact of liquidity risk
!
on bank stock returns. We report the 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!=>9:; in Panel C of Table 11. We
use a minimum and maximum loading (𝛾) estimated from different regressions based on
" "
equation (1) and calculate a range of 𝐿𝑅𝑀𝐸𝑆)!? and 𝐿𝑅𝑀𝐸𝑆)@A , which is between 9.5% and
!
16.4%. The corresponding 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑆𝑅𝐼𝑆𝐾!=>9:; amounts to USD 177bn to USD 307bn.
In a final step, we calculate the conditional SRISK (𝑆𝑅𝐼𝑆𝐾 " ) adding the two incremental
SRISK components. Adding both components we show that the additional capital shortfall for
the U.S. banking sector due to balance-sheet liquidity risk amounts to more than $366 billion
as of December 31, 2019 in a stress scenario of a 40% correction to the stock market, with the
top 10 banks contributing USD 293bn. The incremental capital shortfall of the top 10 banks is
about 1.7 times the SRISK estimate without accounting for contingent liabilities and the effect
of liquidity risk.
Overall, our estimates show that the incremental capital shortfall in an aggregate
economic downturn due to banks’ contingent liabilities is sizeable, because it requires an
additional amount of capital to fund the new loans on their balance sheets and, importantly,
there is an (even larger) incremental capital shortfall due to the episodic impact of bank balance-
sheet liquidity risk on bank stock returns. Our results, however, show clearly that most of the
impact on banks’ balance sheets arises due to the market’s re-evaluation of liquidity risk in
banks’ equity. As described throughout the paper, markets react when actual drawdown rates
deviate from expected ones by re-pricing bank equity. This channel is economically highly
relevant (as the numbers above document) and should thus be considered in stress tests and
similar exercises.
40
9. Conclusion
Our research underscores the importance of banks' liquidity risk in explaining the decline of
bank stock prices during the pandemic's initial phase. We identified balance-sheet liquidity risk
as a vital determinant of bank stock returns, regardless of banks' exposure to COVID-affected
sectors. We delved into two main channels affecting bank stock prices: the "funding channel"
and the "capital channel". By constructing proxies for gross and net drawdowns, we discerned
that bank stock returns were more influenced by gross drawdowns, especially for banks with
higher capital and superior capital buffers.
Our analysis of bank stock price recovery in 2020Q2 spotlighted the significant role of
credit-line repayments. We established two primary factors: liquidity returned to banks and the
revenue discrepancy between the drawn credit line and potential alternative investments. Our
data validates the importance of both elements, indicating banks and their investors prioritize
compensation for capital opportunity cost and drawdown risk. The capital channel proves
crucial not only in understanding the ramifications of credit line drawdowns but also in the
effects of repayments.
These findings have potential implications for how economic shocks may affect banks in
future. Darmouni and Siani (2020) show that U.S. non-financial firms issued bonds following
the monetary policy and fiscal interventions starting March 2020 and used the proceeds to repay
credit lines. While a large proportion of credit lines have been repaid in Q2 and Q3 2020,
corporate preference for cash of firms has remained high (Online Appendix A) and total debt
on firms’ balance sheet has substantially increased. The non-financial sector’s leverage and
exposure to capital markets thus increased further during and after the COVID-19 pandemic.
In other words, ex-ante aggregate drawdown risk of banks is again high in case of another
aggregate shock such as a rise in interest rates or a recession (or both, i.e., a stagflation) were
to stress capital markets. In that scenario, the value of the put option in the form of bank credit
lines for corporates and capital markets would be even more pronounced if bond market
41
liquidity conditions were to severely deteriorate. In summary, additional corporate leverage
accumulated since the pandemic has likely increased the likelihood of future impact on bank
stock returns via the credit-line drawdown channel. This makes it crucial for stress tests to factor
in aggregate drawdown risk and its impact on bank equity, as we illustrated. Clearly, much
scope for research and policy reform around bank credit lines remains.
42
References
Acharya, V., H. Almeida, F. Ippolito, and A. Pérez Orive, 2014, Credit Lines as Monitored
Liquidity Insurance: Theory and Evidence. Journal of Financial Economics 112 (3),
287–319.
Acharya, V., H. Almeida, and M. Campello, 2013, Aggregate Risk and the Choice Between
Cash and Lines of Credit. The Journal of Finance 68 (5), 2059–116.
Acharya, V., R. Engle, and M. Richardson, 2012, Capital Shortfall: a New Approach to Ranking
and Regulating Systemic Risks. American Economic Review 102, 59–64.
Acharya, V. and N. Mora, 2015, A Crisis of Banks as Liquidity Providers. Journal of Finance,
2015, 70 (1), 1-44.
Acharya, V., L. Pedersen, T. Philippon, and M. Richardson, 2016, Measuring Systemic Risk.
Review of Financial Studies 30, 2-47.
Acharya, V. and S. Steffen, 2015, The “Greatest” Carry Trade Ever? Understanding Eurozone
Bank Risk. Journal of Financial Economics 115 (2), 215-36.
Acharya, V. and S. Steffen, 2020a, The Risk of Being a Fallen Angel and the Corporate Dash
for Cash in the Midst of COVID. Review of Corporate Finance Studies 9 (3), 430-71.
Acharya, V. and S. Steffen, 2020b, ‘Stress Tests’ for Banks as Liquidity Insurers in a Time of
COVID. CEPR VoxEU.org.
Adrian, T. and M. Brunnermeier, 2016, American Economic Review 106 (7), 1705-1741.
Allen, F., and D. Gale, 2004, Financial Intermediaries and Markets. Econometrica 72, 1023–
61.
Allen, F., and A. M. Santomero, 1998, The Theory of Financial Intermediation. Journal of
Banking and Finance 21, 1461–85.
Bai, J., A. Krishnamurthy, and C.-H. Weymuller, 2018, Measuring Liquidity Mismatch in the
Banking Sector. Journal of Finance 73, 51-93.
43
Balyuk, T., M. Puri, and N. Prabhala, 2021, Indirect Costs of Government Aid and Intermediary
Supply Effects: Lessons From the Paycheck Protection Program, Working Paper.
Beltratti, A. and R. Stulz, 2012, The Credit Crisis around the Globe: Why did some Banks
Perform Better? Journal of Financial Economics 105 (1), 1-17.
Berg, T., A. Saunders and S. Steffen, 2016, The Total Cost of Borrowing in the Loan Market –
Don’t Ignore the Fees. Journal of Finance 71 (3), 1357-92.
Berg, T., A. Saunders, S. Steffen and D. Streitz, 2017, Mind the Gap: The Difference between
U.S. and European Loan Rates. Review of Financial Studies 30 (3), 948-87.
Berger, A., and C. Bouwman, 2009, Bank Liquidity Creation. Review of Financial Studies, 22
(9), 3779-837.
Bhattacharya, S., and A. V. Thakor, 1993, Contemporary Banking Theory. Journal of Financial
Intermediation 3, 2–50.
Boyarchenko, N., A. Kovner and O. Shachar. 2022. It’s What You Say and What You Buy: A
Holistic Evaluation of the Corporate Credit Facilities. Journal of Financial Economics
144(3), 695-731.
Brownlees, C., and R. Engle, 2017, SRISK: A Conditional Capital Shortfall Measure of
Systemic Risk. Review of Financial Studies 30 (1), 48–79.
Campello, M., J. Graham and C. Harvey, 2010, The Real Effects of Financial Constraints:
Evidence from a Financial Crisis, Journal of Financial Economics 97, 470-487.
Campello, M., J. Graham and C. Harvey, 2011, Liquidity Management and Firm Investment in
a Financial Crisis, Review of Financial Studies 24, 1944-1979.
Cai, J., F. Eidam, A. Saunders, and S. Steffen, 2018, Syndication, Interconnectedness, and
Systemic Risk. Journal of Financial Stability 34, 105-20.
Chodorow-Reich, Gabriel, and Antonio Falato, 2022. The Loan Covenant Channel: How Bank
Health Transmits to the Real Economy, Journal of Finance 77 (1), 85-128.
44
Chodorow-Reich, G., O. Darmouni, S. Luck, and M. Plosser, 2022, Bank Liquidity Provision
across the Firm Size Distribution. Journal of Financial Economics 144 (3), 908-32.
Coval, J. D., and A. V. Thakor, 2005, Financial Intermediation as a Beliefs-Bridge between
Optimists and Pessimists. Journal of Financial Economics 75, 535–69.
Darmouni, O., and K. Y. Siani, 2020, Crowding Out Bank Loans: Liquidity-Driven Bond
Issuance. Working Paper, Columbia University.
Deep, A., and G. Schaefer, 2004, Are Banks Liquidity Transformers? Working Paper, Harvard
University.
Demsetz, R., and P. Strahan, 1997, Diversification, Size, and Risk at Bank Holding Companies.
Journal of Money, Credit and Banking 29 (3), 300-13.
Demirguc-Kunt, A., A. Pedraza, and C. Ruiz-Ortega, 2021, Banking Sector Performance during
the COVID-19 Crisis. Journal of Banking & Finance 133, 106305.
Diep, P., Eisfeldt, A.L. and Richardson, S., 2021, The cross section of MBS returns, The
Journal of Finance 76(5), 2093-2151.
Dubois, C., and L. Lambertini, 2018, A Macroeconomic Model of Liquidity, Wholesale
Funding and Banking Regulation. Working Paper École Polytechnique Féd Érale de
Lausanne.
English, W.B., Van den Heuvel, S.J., and Zakrajšek, E., 2018, Interest rate risk and bank equity
valuations, Journal of Monetary Economics 98, 80-97.
Erel, I., and J. Liebersohn, 2022, Can FinTech Reduce Disparities in Access to Finance?
Evidence from the Paycheck Protection Program, Journal of Financial Economics 146
(1), 90-118.
Fahlenbrach, R., R. Prilmeier, and R. M. Stulz, 2012, This Time is the Same: Using Bank
Performance in 1998 to Explain Bank Performance During the Recent Financial Crisis.
Journal of Finance 67, 2139-85.
45
Fahlenbrach, R., K. Rageth, and R. M. Stulz, 2021, How Valuable is Financial Flexibility when
Revenue Stops? Evidence from the Covid-19 crisis. Review of Financial Studies 34 (11),
5474-521.
Gatev, E., and P. Strahan, 2006, Banks' Advantage in Hedging Liquidity Risk: Theory and
Evidence from the Commercial Paper Market. Journal of Finance 61 (2), 867-92.
Gormsen, N. J., and R. S. J. Koijen, 2020, Coronavirus: Impact on Stock Prices and Growth
Expectations. Review of Asset Pricing Studies 10 (4), 574-97.
Greenwald, D. L., J. Krainer, and P. Paul, 2023, The Credit Line Channel, Working Paper, New
York University Stern School of Business.
Haddad, V., A. Moreira, and T. Muir, 2021, When Selling Becomes Viral: Disruptions in Debt
Markets in the COVID-19 Crisis and the Fed’s Response, Review of Financial Studies 34
(11), 5309–5351.
Ippolito, F., J.-L. Peydró, A. Polo, and E. Sette, 2016, Double Bank Runs and Liquidity Risk
Management. Journal of Financial Economics 122 (1), 135–54.
Ivashina, V., 2009, Asymmetric Information Effects on Loan Spreads. Journal of Financial
Economics 92, 300–319.
Ivashina, V., and D. Scharfstein, 2010, Bank Lending During the Financial Crisis of 2008.
Journal of Financial Economics 97(3), 319–38.
Jiménez, G., J. A Lopez, and J. Saurina, 2009, Empirical Analysis of Corporate Credit Lines.
Review of Financial Studies 22 (12), 5069–98.
Kapan, T., and C. Minoiu, 2021, Liquidity Insurance vs. Credit Provision: Evidence from the
COVID-19 Crisis, Working Paper, Federal Reserve Board of Governors.
Kashyap, A., R. Rajan and J. Stein, 2002, Banks as Liquidity Providers: An Explanation for the
Coexistence of Lending and Deposit-taking. Journal of Finance 57 (1), 33-73.
Kovner, A. and A. Martin, 2020, Expanding the Toolkit: Facilities Established to Respond to
the COVID-19 Pandemic, Federal Reserve Bank of New York Liberty Street
Economics.
46
Khwaja, A. and A. Mian, 2008, Tracing the Impact of Bank Liquidity Shocks: Evidence from
an Emerging Market. American Economic Review 98 (4), 1413–42.
Landier, A., and D. Thesmar, 2020, Earnings Expectations in the COVID Crisis. Review of
Asset Pricing Studies 10 (4), 598-617.
Li, L., P. Strahan, and S. Zhang, 2020, Banks as Lenders of First Resort: Evidence from the
COVID-19 Crisis. Review of Corporate Finance Studies 9 (3), 472-500.
Minoiu, C., R. Zarutskie, and A. Zlate, 2021, Motivating Banks to Lend? Credit Spillover
Effects of the Main Street Lending Program, Working Paper, Federal Reserve Bank of
Atlanta.
Nikolov, B., L. Schmid, and R. Steri, 2019, Dynamic Corporate Liquidity. Journal of Financial
Economics 132 (1), 76–102.
O’Hara, M., and X. A. Zhou, 2021, Anatomy of a Liquidity Crisis: Corporate Bonds in the
Covid-19 Crisis. Journal of Financial Economics 142, 46-68.
Pagano, M., C. Wagner, and J. Zechner, 2022, Disaster resilience and asset prices. Journal of
Financial Economics, forthcoming.
Ramelli, S., and A. F. Wagner, 2020, Feverish Stock Price Reactions to COVID-19. Review of
Corporate Finance Studies 9 (3), 622-55.
Repullo, R, 2004, Capital Requirements, Market Power, and Risk-Taking in Banking. Journal
of Financial Intermediation 13, 156–82.
Sufi, A., 2009, Bank Lines of Credit in Corporate Finance: An Empirical Analysis. Review of
Financial Studies 22 (3), 1057–88.
Vissing-Jorgensen, 2021, The Treasury Market in Spring 2020 and the Response of the Federal
Reserve, Journal of Monetary Economics 124, 19–47.
Von Thadden, E.-L, 2004, Bank Capital Adequacy Regulation under the New Basel Accord.
Journal of Financial Intermediation 13, 90–95.
47
Figure 1. Credit lines, cumulative drawdowns and bank stock prices
Panel A shows the annual financing of U.S. publicly listed firms by term loans, undrawn credit lines and bonds
(as a percentage of GDP) over the 2002-2019 period. Panel B shows cumulative drawdowns of US publicly listed
firms at the beginning of the COVID-19 pandemic during the period Mar-June 2020. Panel C shows cumulative
drawdowns by rating class. Panel D shows the stock prices of U.S. publicly listed banks, non-bank financial and
non-financial firms over the Jan 1st to Dec 31st, 2020 period. The sample of 147 banks is documented in Appendix
II.
Panel A. Bond vs loan financing of U.S. publicly listed firms
Panel B. Cumulative drawdowns (in USD bn)
48
Panel C. Cumulative drawdowns by rating class (in USD bn)
Panel D. Stock prices
49
Figure 2. Bank balance-sheet liquidity risk
Panel A of Figure 2 shows the time-series of balance-sheet Liquidity Risk over the Q1 2010 to Q4 2020 period.
We measure Liquidity Risk as undrawn commitments to commercial and industrial (C&I) firms plus wholesale
funding minus cash or cash equivalents (all relative to assets). Panel B shows the time-series of its components.
All variables are defined in Appendix III.
Panel A. Liquidity risk
Panel B. Components of liquidity risk
50
Figure 3. Stock prices and liquidity risk of U.S. banks
This figure shows stock prices of U.S. banks in relationship to their liquidity risk. Panel A uses (1) a median split
to distinguish between banks with Low vs. High Liquidity Risk and (2) a median split to distinguish between banks
with Low vs. High Credit Line Commitments and shows the time-series of stock price difference of each respective
group of banks indexed at Jan 1, 2020. We measure Liquidity Risk as undrawn C&I commitments plus wholesale
finance minus cash or cash equivalents (all relative to assets). Panel B plots the cross-section of bank stock returns
during the March 1 – March 23, 2020 period as a function of banks’ Liquidity Risk. All variables are defined in
Appendix III.
Panel A. Bank stock prices for high vs low liquidity risk/credit line commitment banks
Panel B. Bank stock return and liquidity risk
51
Figure 4. Net vs. gross drawdowns
This figure shows the time-series of Gross Drawdowns (Panel A) and Net Drawdowns (Panel B) over the Q1 2010
to Q4 2020 period. Gross Drawdowns is the percentage change in a bank’s off-balance-sheet unused C&I loan
commitments. Net Drawdowns are defined as the change in a bank’s off-balance-sheet unused C&I loan
commitments minus the change in deposits, relative to total assets. All variables are defined in Appendix III.
Panel A. Gross Drawdowns
Panel B. Net Drawdowns
52
Figure 5. Credit line issuances (volume and spread/fees)
This figure shows quarterly issuance volume in USD billion (Panel A), all-in-spread-drawn or AISD (Panel B),
all-in-spread-undrawn or AISU (Panel C), and upfront fees or UFR (Panel D), with spreads and fees in basis points
(bps), of credit line issuances by U.S. firms over the 2018 to 2021 period. All variables are defined in Appendix
III.
Panel A. Quarterly loan amounts of newly issued credit lines
Panel B. Quarterly average AISD of newly issued credit lines
53
Panel C. Quarterly average AISU of newly issued credit lines
Panel D. Quarterly average upfront fees of newly issued credit lines
54
Figure 6. Credit line drawdowns and stock market returns
This figure plots the cumulative drawdown of credit lines of non-financial firms, i.e., C&I credit lines, on the
cumulative market return (using the S&P 500 index as the market). In Panel A, we plot the cumulative quarterly
drawdown rates during the COVID-19 pandemic (i.e., Q4 2019 and Q1 2020) and the Global Financial Crisis (i.e.,
the Q1 2007 to Q4 2009 period) on the respective quarterly S&P 500 returns. We also show the linear regressions
for both periods. In Panel B, we use the lowest cumulative daily S&P 500 return within each quarter (instead of
the quarterly return). All variables are defined in Appendix III.
Panel A. Quarterly drawdowns vs quarterly S&P 500 returns
Panel B. Quarterly drawdowns vs lowest cumulative S&P 500 return in each quarter
55
Table 1. Descriptive statistics
This table shows descriptive statistics of the variables included in the cross-sectional regressions. The list of
sample banks is shown in Appendix II. All variables are defined in Appendix III.
Panel A. Bank stock returns
Variable Obs. Mean Std. dev. Min Max
Return January 2020 147 -0.072 0.046 -0.181 0.064
Return February 2020 147 -0.125 0.040 -0.246 0.071
Return 3/1-3/23 2020 147 -0.472 0.186 -1.084 -0.131
Return 1/1-3/23 2020 147 -0.669 0.206 -1.225 -0.227
Panel B. Bank characteristics
Liquidity Risk 147 0.195 0.147 -0.453 0.590
Unused LC / Assets 147 0.077 0.051 0.000 0.263
Liquidity / Assets 147 0.136 0.109 0.029 0.607
Wholesale Funding / Assets 147 0.144 0.100 0.013 0.624
Beta 147 1.170 0.328 0.156 2.313
NPL / Loans 147 0.008 0.008 0.000 0.044
Non-Interest Income 147 0.268 0.185 0.021 0.966
Log(Assets) 147 16.982 1.437 14.397 21.712
ROA 147 0.013 0.006 0.003 0.061
Deposits / Loans 147 1.306 1.130 0.504 11.002
Income Diversity 147 0.446 0.212 0.043 0.993
Z-Score 147 3.619 0.536 1.859 5.060
Loans / Assets 147 0.670 0.166 0.027 0.899
Deposits / Assets 147 0.745 0.105 0.191 0.879
Idiosyncratic Volatility 147 0.200 0.041 0.121 0.417
Real Estate Beta 147 0.544 0.197 -0.266 1.136
Primary Dealer 147 0.041 0.199 0.000 1.000
Derivatives / Assets 147 1.161 4.753 0.000 37.242
Credit Card Commitments /Assets 147 0.075 0.389 0.000 3.998
Consumer Loans / Assets 147 0.056 0.117 0.000 0.828
SRISK /Assets 147 0.003 0.007 0.000 0.039
56
Table 2. Liquidity risk and bank stock returns
This table reports the results of OLS regressions of U.S. banks’ excess stock returns over the 1/1/2020 – 3/23/2020
period on bank Liquidity Risk and a bank’s Equity Beta and control variables. Equity Beta is constructed as bank
stock beta relative to the S&P 500 using daily stock returns over the 2019 period, multiplied with the realized
excess return of the S&P 500 over the 1/1/2020 – 3/23/2020 period. We add SRISK/Assets as additional control
(column (6)). SRISK is available for banks in the NYU Stern School of Business VLAB database at
vlab.stern.nyu.edu/srisk. The regressions include a dummy for banks for whom we do not find exposure data
(coefficient unreported). P-values based on robust standard errors are in parentheses. All variables are defined in
Appendix III.
(1) (2) (3) (4) (5) (6)
Liquidity Risk -0.329*** -0.409*** -0.565*** -0.550*** -0.568*** -0.551***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Equity Beta 0.734*** 0.706*** 0.566*** 0.557*** 0.577*** 0.476***
(0.000) (0.000) (0.001) (0.001) (0.001) (0.004)
NPL / Loans -7.038*** -3.682** -3.603** -3.408* -3.665**
(0.000) (0.033) (0.039) (0.054) (0.035)
Equity Ratio 0.522 -0.119 -0.103 -0.519 -0.897
(0.425) (0.858) (0.878) (0.443) (0.179)
Non-Interest Income 0.297*** 0.169 0.189 0.132 0.0973
(0.003) (0.139) (0.106) (0.273) (0.412)
Log(Assets) -0.000996 -0.0330** -0.0363** -0.0210 0.00422
(0.938) (0.046) (0.036) (0.267) (0.844)
ROA -3.726 1.193 1.167 5.406 6.158
(0.310) (0.757) (0.766) (0.237) (0.163)
Deposits / Loans -0.0217 -0.057*** -0.054*** -0.015*** -0.054***
(0.115) (0.001) (0.002) (0.002) (0.003)
Income Diversity -0.0226 -0.0343 -0.0257 -0.0263
(0.799) (0.705) (0.775) (0.747)
Distance-to-Default 0.0606* 0.0581* 0.0583* 0.0517*
(0.061) (0.075) (0.067) (0.075)
Loans / Assets -0.483** -0.461** -0.408* -0.352*
(0.020) (0.032) (0.062) (0.099)
Deposits / Assets -0.0587 -0.0207 -0.0873 -0.235
(0.786) (0.938) (0.735) (0.346)
Idiosyncratic Volatility -1.174*** -1.206*** -1.018** -1.051**
(0.003) (0.002) (0.017) (0.014)
Real Estate Beta 0.180* 0.184* 0.113 0.0951
(0.099) (0.093) (0.380) (0.441)
Current Primary Dealer Indicator 0.0845 0.00641 -0.0951
(0.430) (0.958) (0.381)
Derivatives / Assets -0.00151 -0.000340 0.00526
(0.808) (0.958) (0.415)
Credit Card Commitments /Assets -0.0371 -0.0926
(0.510) (0.135)
Consumer Loans / Assets -0.218 -0.147
(0.395) (0.591)
SRISK /Assets -6.409***
(0.009)
R-squared 0.256 0.354 0.448 0.449 0.462 0.502
Number obs. 147 147 147 147 147 147
57
Table 3. Controlling for bank portfolio composition via exposure to COVID-19-affected
industries
Panel A reports the results of OLS regressions of U.S. banks’ excess stock returns over the 3/1/2020 – 3/23/2020
period on bank Liquidity Risk. Columns (1) – (12) add different measures that proxy for bank exposures to COVID-
19-affected industries. These measures are defined in Appendix IV. Exposures “Affected Industries (𝛽"#$%& )” are
calculated in regressions of bank excess stock returns on stock returns of COVID-19-affected industries and
various (macro) variables: Market return, SMB, HML, risk-free interest rate, VIX, term spread, BBB-AAA spread,
the Consumer Price Inflation (as explained in Note at the bottom of this table). Column (13) uses the first principal
component based on all 12 exposure betas. Column (14) uses a bank’s average Dealscan syndicated loan exposure
to affected industries based on different definitions relative to total assets (Loan Exposure / Assets). P-values based
on robust standard errors are in parentheses. All variables are defined in Appendix III.
(1) (2) (3) (4) (5) (6)
Liquidity Risk -0.568*** -0.543*** -0.546*** -0.527*** -0.481*** -0.530***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Affected Industries (𝛽"#$%& ) -1.410*** -0.531* -0.455 -0.526*** -0.635*** -0.493**
(0.005) (0.097) (0.116) (0.005) (0.000) (0.026)
Controls Yes Yes Yes Yes Yes Yes
Fahlenbrach
Fahlenbrach Moody's Koren and Dingel and et al. (2021) Koren and
et al. (2021) (2020) Peto (2020) Neiman – 6 NAIC Peto (2020)
Affected Measure
– stock COVID – Customer (2020) – level – Presence
performance industries share Telework COVID share
industries
R-squared 0.505 0.475 0.475 0.502 0.537 0.498
Number obs. 147 147 147 147 147 147
(7) (8) (9) (10) (11) (12)
Liquidity Risk -0.515*** -0.518*** -0.541*** -0.524*** -0.534*** -0.521***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Affected Industries (𝛽"#$%& ) -0.541** -0.709*** -0.221* -0.910** -1.528*** -2.090***
(0.013) (0.004) (0.090) (0.018) (0.001) (0.004)
Controls Yes Yes Yes Yes Yes Yes
Chodorow-
Koren and Reich et al.
ONET – ONET – ONET –
Peto (2020) – YoY sales (2022) –
Affected Measure Physical Face-to-face External
Teamwork decline Abnormal
proximity discussion customers
share employment
decline
R-squared 0.496 0.519 0.476 0.501 0.517 0.504
Number obs. 147 147 147 147 147 147
58
(13) (14)
Liquidity Risk -0.515*** -0.496***
(0.000) (0.000)
Affected Industries (𝛽"#$%& ) -0.040**
(0.012)
-0.074**
Loan Exposure / Assets (0.024)
Controls Yes Yes
First Principal Average Syndicated
Affected Measure Component of exposure Loan Exposure to affected
betas to affected industries industries
R-squared 0.524 0.478
Number obs. 147 147
Note:
Detailed data describing bank portfolio composition are hardly available to empirical researchers. Our approach
to estimate banks’ exposure to COVID-19-affected industries is similar to the procedure employed, e.g., by
Agarwal and Naik (2004) to characterize the exposures of hedge funds or the approach in Acharya and Steffen
(2015) in estimating European banks’ exposure to sovereign debt. We use multifactor models in which the
sensitivities of banks’ stock returns to “COVID-19-affected industry” returns are measures of banks’ exposure to
these industries. We call these sensitivities “Affected Industries (𝛽"#$%& )”. The lack of micro level portfolio
holdings of banks gives these tests more power and increases the efficiency of the estimates. More precisely, we
run the following regression daily over the Jan 1, 2019 to Dec 31, 2019 period for each bank i:
𝑟' = 𝛽( + 𝛽"#$%& 𝑟"#$%&,' + 𝛽* 𝑟*,' + 𝛽+,- 𝐻𝑀𝐿' + 𝛽.,/ 𝑆𝑀𝐵' + 𝛾 , 𝑿' + 𝜀'
𝑟' is the daily bank excess return. 𝑟"#$%&,' is the daily excess return of the COVID-19-affected industry. 𝑟*,' is the
daily market excess return. HML and SML are the Fama-French factors. 𝑿' is a vector of control variables: risk-
free interest rate, VIX, term spread, BBB-AAA spread, and the CPI. Because of the co-movement of 𝑟*,' and
𝑟"#$%&,' , we orthogonalize 𝑟*,' to 𝑟"#$%&,' .
59
Table 4. Liquidity risk and bank stock returns – Robustness tests
Panel A reports the results of OLS regressions of U.S. bank’ realized stock returns during January 2020 (columns
(1)-(2)), February 2020 (columns (3) to (4)) and 1-23 March 2020 (columns (5) to (6)). Regressions with control
variables are based on column (5) in Table 2. P-values based on robust standard errors are in parentheses. Panel B
reports the results of OLS regressions of U.S. banks’ excess stock returns over the 1/3/2020 – 3/23/2020 period
on the different components of Liquidity Risk with control variables as in column (5) in Table 2. We first show
each component separately in columns (1)-(3) and then add them sequentially in columns (4) and (5). P-values
based on robust standard errors are in parentheses. Panel C reports the results of OLS regressions of U.S. banks’
excess stock returns over the 1/3/2020 – 3/23/2020 period on the different components of Liquidity Risk and
different proxies for wholesale funding with control variables as in column (5) in Table 2. Columns (1) to (5)
sequentially add additional components and proxies. P-values based on robust standard errors are in parentheses.
Panel D reports descriptive statistics of bank excess stock returns for Q1 – Q4 2020. Panel E reports the results of
OLS regressions of U.S. banks’ excess stock returns over the Q2 to Q4 2020 period on bank Liquidity Risk, Equity
Beta and control variables as shown in column (5) of Table 2. Control variables are lagged by one quarter. Columns
(1) and (2) report the results using Liquidity Risk and columns (3) and (4) the components of Liquidity Risk.
Columns (2) and (4) include quarter fixed effects. Standard errors are clustered at the bank level. Columns (5) to
(7) repeat the results separately for each quarter. P-values based on robust standard errors are in parentheses. All
variables are defined in Appendix III.
Panel A. Liquidity risk and bank stock returns by month
(1) (2) (3) (4) (5) (6)
January 2020 February 2020 1/3-23/3/2020
Liquidity Risk -0.0594** -0.0625** -0.0470 -0.0439 -0.462*** -0.445***
(0.022) (0.023) (0.306) (0.357) (0.000) (0.000)
Equity Beta 0.0452 0.0699* 0.0350 0.0197 0.497*** 0.386**
(0.253) (0.066) (0.185) (0.465) (0.003) (0.011)
SRISK /Assets 1.317** -1.122* -6.604***
(0.048) (0.075) (0.007)
Controls Yes Yes Yes Yes Yes Yes
R-squared 0.341 0.387 0.258 0.285 0.413 0.471
Number obs. 147 147 147 147 147 147
Panel B. Components of liquidity risk
(1) (2) (3) (4) (5)
3/1-3/23/2020
Unused C&I Loans / Assets -1.110*** -1.006*** -1.084***
(0.001) (0.001) (0.001)
Liquidity / Assets 0.563*** 0.477*** 0.488***
(0.004) (0.009) (0.006)
Wholesale Funding / Assets -0.114 -0.279
(0.562) (0.107)
Equity Beta -0.578*** -0.513** -0.498** 0.599*** 0.597***
(0.004) (0.012) (0.015) (0.004) (0.003)
SRISK /Assets -6.559** -6.733*** -7.128*** -6.208** -5.922**
(0.015) (0.005) (0.005) (0.014) (0.018)
Controls Yes Yes Yes Yes Yes
R-squared 0.456 0.439 0.408 0.479 0.486
Number obs. 147 147 147 147 147
60
Panel C. Wholesale funding and bank stock returns during COVID
(1) (2) (3) (4)
Liquidity Risk -0.445***
(0.000)
Unused Commitments / Assets -1.084*** -1.020*** -1.149***
(0.001) (0.001) (0.000)
Liquidity / Assets 0.488*** 0.487*** 0.326*
(0.006) (0.008) (0.083)
Wholesale Funding / Assets -0.279
(Acharya and Mora, 2015) (0.107)
Wholesale Funding / Assets -0.0788
(Dubios and Lambertini, 2018) (0.689)
Large Time Deposits / Assets -1.164**
(0.034)
Foreign Deposits / Assets -0.0464
(0.846)
Subordinated Debt / Assets -1.581
(0.445)
Fed Funds Purchased / Assets 1.681
(0.117)
Other Borrowed Money / Assets 0.0778
(0.892)
R-squared 0.471 0.486 0.480 0.523
Number obs. 147 147 147 147
Panel D. Descriptive statistics of bank stock returns over the quarters of 2020
Variable Obs. Mean Std. dev. Min Max
2020Q1 147 -0.511 0.181 -0.996 -0.075
2020Q2 146 0.096 0.149 -0.398 0.537
2020Q3 145 -0.079 0.104 -0.282 0.249
2020Q4 144 0.346 0.115 0.014 0.706
Total 582 -0.039 0.343 -0.996 0.706
Panel E. Liquidity risk and bank stock returns after the policy interventions of March 2020
(1) (2) (3) (4) (5) (6) (7)
Q2–Q4 2020 Q2 2020 Q3 2020 Q4 2020
Liquidity Risk 0.0104 -0.0406 -0.00979 -0.132* -0.0368
(0.856) (0.446) (0.931) (0.073) (0.714)
Unused C&I Loans / Assets -0.105 -0.194*
(0.481) (0.094)
Liquidity / Assets -0.0726 0.00860
(0.352) (0.901)
Wholesale Funding / Assets -0.0845 -0.101
(0.268) (0.148)
Controls Yes Yes Yes Yes Yes Yes Yes
Quarter FE Yes Yes
Cluster Bank Bank Bank Bank
R-squared 0.122 0.751 0.123 0.751 0.434 0.380 0.441
Number obs. 435 435 435 435 146 145 144
61
Table 5. Understanding the mechanisms: Funding versus capital during Q1 2020 (prior to policy interventions)
Panel A reports the results of OLS regressions of U.S. bank’ excess stock returns during the 1/1/2020 to 3/23/2020 period on Net Drawdowns (column (1)) and Gross Drawdowns
(column (2)) and control variables. Net Drawdowns are defined as the change in a bank’s off-balance-sheet unused C&I loan commitments minus the change in deposits (all
measured during Q1 2020) relative to total assets. Gross Drawdowns is the percentage change in a bank’s off-balance-sheet unused C&I loan commitments (measured during Q1
2020). Column (4) adds SRISK/Assets as additional control. SRISK is only available for banks in the NYU Stern School of Business VLAB database at vlab.stern.nyu.edu/srisk.
These regressions include a dummy for banks for whom we do not find SRISK (unreported coefficient). Column (5) includes an interaction term of Gross Drawdowns with High
Capital, and indicator variable that is one if a bank’s equity capital ratio is above the median of the distribution. Column (6) includes an interaction term of Gross Drawdowns with
Capital Buffer, which is the difference between a bank’s equity capital ratio and the average capital ratio of all sample banks. The secular term Capital Buffer is thus absorbed.
Column (7) (column ((8)) include interaction terms of Net Drawdowns and High Capital (Capital Buffer). In columns (9) and (10), we compare both interaction terms of Gross and
Net Drawdowns. Panel B reports the results using Deposit Inflows, defined as deposit inflows in Q1 2020 relative to total assets, instead of Net Drawdowns. Control variables as in
column (5) in Table 2 are included. P-values based on robust standard errors are in parentheses. All variables are defined in Appendix III.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Net Drawdowns 0.0686 0.356 0.393 0.382 0.357 0.366 0.305 0.366 0.295
(0.881) (0.421) (0.333) (0.363) (0.398) (0.538) (0.469) (0.527) (0.461)
Gross Drawdowns -5.142*** -5.618*** -5.357*** -9.156*** -5.213*** -5.615*** -5.551*** -9.153*** -5.117***
(0.009) (0.003) (0.007) (0.001) (0.005) (0.002) (0.003) (0.001) (0.006)
SRISK / Assets -6.236**
(0.039)
Gross Drawdowns x High Capital 5.927** 5.913**
(0.034) (0.033)
Gross Drawdowns x Capital Buffer 1.840** 1.909**
(0.046) (0.035)
Net Drawdowns x High Capital 0.186 0.0356
(0.845) (0.969)
Net Drawdowns x Capital Buffer -0.115 -0.139
(0.454) (0.324)
High Capital 0.0298 0.0671 0.0304
(0.559) (0.132) (0.554)
Capital Buffer -1.375* -0.697 -1.676*
(0.094) (0.377) (0.065)
Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
R-squared 0.377 0.411 0.415 0.457 0.439 0.435 0.425 0.418 0.439 0.439
Number obs. 147 147 147 147 147 147 147 147 147 147
62
Table 6. Implications for bank lending during the COVID-19 pandemic
This table provides results of difference-in-differences regressions of the change in the outstanding loan amounts (exposures) and new loan originations in the pre- versus post-
COVID-19 period on Gross and Net Drawdowns. The analysis is based on exposures / originations in the period between January 2019 and October 2020 (Post is denoted as the
period starting 4/1/2020). Columns (1) to (4) show the results using quarterly exposures (defined as all previously issued and non-matured credit – both term loan and credit line –
reported in Dealscan as the dependent variable). High Gross (Net) are indicator variables equal to 1 if drawdowns are in the upper quartile of the distribution. Term Loan
Indicator is an indicator variable equal to 1 if the loan is a term loan. All regressions include borrower x time x loan type and borrower x bank fixed effects. Columns (5) – (8)
show the results using newly originated credit -- both term loan and credit line -- as the dependent variable. Standard errors are clustered at the bank level. Control variables
include banks’ NPL ratio, log of total assets, ROA, Tier 1 capital ratio and loan-asset-ratio. Detailed variable definitions can be found in Appendix III. ***, **, * denote
significance at the 1%, 5% and 10% level, respectively.
(1) (2) (3) (4) (5) (6) (7) (8)
Exposures New originations
Gross Drawdowns (Gross) x Post 0.169 -1.364
(0.611) (0.323)
Net Drawdowns (Net) x Post 0.0299 -0.317
(0.579) (0.228)
High Gross x Post 0.00115 0.0140* 0.0131* -0.0426** -0.0467*** -0.0417*
(0.849) (0.070) (0.075) (0.011) (0.007) (0.086)
High Net x Post 0.00554 -0.000600 -0.00303 -0.0320 -0.0334 -0.0108
(0.366) (0.940) (0.684) (0.135) (0.153) (0.741)
High Gross x Post x Term Loan Indicator -0.0363*** -0.0366*** 0.0158 0.0240
(0.009) (0.009) (0.345) (0.216)
High Net x Post x Term Loan Indicator 0.0173 0.0174 0.00626 0.0134
(0.220) (0.229) (0.641) (0.470)
Controls No No No Yes No No No Yes
Borrower x Time x Loan Type FE Yes Yes Yes Yes Yes Yes Yes Yes
Bank x Borrower FE Yes Yes Yes Yes Yes Yes Yes Yes
R-squared 0.976 0.976 0.976 0.977 0.993 0.993 0.993 0.993
Number obs. 340641 340641 340641 296779 6745 6745 6745 6745
63
Table 7. Understanding the mechanisms: Credit line repayments during 2020Q2-Q3
(post policy interventions)
Panel A of Table 8 shows descriptive statistics for the repayment behavior of borrowers by rating category. Sub-
panels A.i and A.ii display the behavior in 2020Q2 in relation to total committed credit or remaining drawdown
balance, and sub-panels A.iii and A.iv show the analogue for 2020Q3. Panel B reports the results of OLS
regressions of US banks' stock returns between March 23, 2020, and June 30, 2020, on bank-level variables
capturing the opportunity-cost adjusted Fees Earned on outstanding credit lines as well as credit-line Repayments
during the 2020Q2 period. Columns (3), (4), and (5) interact Repayments with indicators of previous distress:
market value loss between December 31, 2019, and March 23, 2020 (MV Loss Covid), the regulatory capital level
(Capital Buffer), and the bank-level credit line drawdowns in the first quarter of 2020 (Drawdowns 2020Q1).
Credit-line repayments are constructed by combining FDIC Call Report, Dealscan and Capital IQ data and are
thus only available for a subset of banks. P-values based on robust standard errors are in parentheses. All variables
are defined in Appendix III.
Panel A. Repayment statistics in 2020Q2 and 2020Q3
Panel A.i. 2020Q2 repayments scaled by commitment
Rating Mean Median SD Min Max
AAA-A 0.24 0.159 0.266 0.000 0.990
BBB 0.26 0.199 0.245 0.000 1.000
non-IG 0.26 0.149 0.276 0.000 1.000
NR 0.2 0.107 0.243 0.000 1.000
Panel A.ii. 2020Q2 repayments scaled by remaining
drawdown balance
Rating Mean Median SD Min Max
AAA-A 0.69 0.988 0.373 0.000 1.000
BBB 0.64 0.736 0.365 0.000 1.000
non-IG 0.5 0.409 0.393 0.000 1.000
NR 0.39 0.256 0.369 0.000 1.000
Panel A.iii. 2020Q3 repayments scaled by commitment
Rating Mean Median SD Min Max
AAA-A 0.08 0.000 0.166 0.000 0.802
BBB 0.12 0.024 0.184 0.000 1.000
non-IG 0.15 0.059 0.208 0.000 1.000
NR 0.14 0.068 0.197 0.000 1.000
Panel A.iv. 2020Q3 repayments scaled by remaining
drawdown balance
Rating Mean Median SD Min Max
AAA-A 0.57 0.670 0.395 0.000 1.000
BBB 0.52 0.497 0.382 0.000 1.000
non-IG 0.43 0.303 0.380 0.000 1.000
NR 0.4 0.286 0.378 0.000 1.000
64
Panel B. Which banks recover market-value losses?
(1) (2) (3) (4) (5) (6)
Fees Earned 0.118*** 0.216*** 0.186*** 0.0772 0.205*** 0.109*
(0.010) (0.003) (0.000) (0.299) (0.009) (0.082)
Repayments 0.673** 0.442 -1.886*** -0.195 0.609 -1.409**
(0.046) (0.294) (0.004) (0.493) (0.265) (0.019)
MV Loss Covid 0.499*** 0.561*** 0.294*** 0.565*** 0.569*** 0.388***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.001)
Drawdowns 2020Q1 3.004*** 3.190*** 3.017*** 4.264*** 5.836* 5.038**
(0.006) (0.006) (0.004) (0.000) (0.089) (0.021)
Fees Earned x Repayments -0.382* -0.562*** 0.135 -0.369 -0.193
(0.061) (0.001) (0.567) (0.102) (0.385)
Repayments x MV Loss Covid 2.817*** 1.889**
(0.000) (0.016)
Repayments x Capital Buffer -31.25*** -18.34*
(0.003) (0.061)
Repayments x Drawdowns 2020Q1 -20.57 -10.37
(0.415) (0.555)
Constant -0.232** -0.161 0.144 -0.120 -0.183 0.0564
(0.016) (0.137) (0.106) (0.215) (0.133) (0.569)
Controls Yes Yes Yes Yes Yes Yes
R-squared 0.901 0.914 0.951 0.949 0.917 0.961
Number obs. 32 32 32 32 32 32
65
Table 8. Credit line commitments, liquidity risk and bank stock returns during crises (including pre-COVID crisis)
This table reports the results of OLS regressions of quarterly U.S. banks’ excess stock returns for three samples on a dummy variable indicating banks with above median credit
line commitments (assigned one quarter before the respective crisis), a dummy variable indicating a crisis quarter and control variables. Separate time-series samples are 2019Q4
to 2021Q1 (Covid), 2004Q1 to 2011Q4 (GFC) and 2000Q1 to 2002Q4 (Dotcom). Crisis quarters are 2001Q1 to 2001Q4 (Dotcom), 2007Q3 to 2009Q2 (GFC) and 2020Q1 (Covid).
Columns sequentially add control variables and bank fixed effects for each sample. The sample of banks is matched on total assets, capitalization, NPL-to-loans and loans-to-assets
ratio. All variables are defined in Appendix III. P-values based on robust standard errors are in parentheses.
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Commitment Above Median 0.0166 0.0144** 0.0143** 0.0145*** 0.00204 0.00229
(0.144) (0.045) (0.010) (0.005) (0.773) (0.742)
Crisis -0.466*** -0.253*** -0.253*** -0.0791*** -0.00839 -0.00343 0.0683*** 0.0520*** 0.0536***
(0.000) (0.000) (0.000) (0.000) (0.336) (0.725) (0.000) (0.000) (0.000)
Commitment Above Median x Crisis -0.0811** -0.0789*** -0.0784*** -0.0302*** -0.0299*** -0.0308*** -0.0317*** -0.0318*** -0.0325***
(0.017) (0.000) (0.001) (0.006) (0.003) (0.005) (0.005) (0.003) (0.003)
MKTRF -0.277* -0.276 0.387*** 0.386*** 0.201*** 0.205***
(0.055) (0.107) (0.000) (0.000) (0.000) (0.000)
SMB FF3 2.217*** 2.219*** 0.448*** 0.448*** 0.529*** 0.530***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
HML 0.349*** 0.351*** 1.099*** 1.112*** 0.540*** 0.547***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Constant 0.117*** 0.0814*** 0.0919*** -0.00325 -0.0270*** -0.0178*** 0.0272*** 0.00223 0.00299
(0.000) (0.000) (0.000) (0.489) (0.000) (0.000) (0.000) (0.717) (0.261)
Sample Covid Covid Covid GFC GFC GFC Dotcom Dotcom Dotcom
Bank FE No No Yes No No Yes No No Yes
R-squared 0.485 0.806 0.806 0.047 0.221 0.221 0.024 0.103 0.103
Number obs. 1364 1364 1364 8109 8109 8109 3914 3914 3914
66
Table 9. Pricing of drawdown options in credit line fees
This table reports the results of OLS regressions of the All-In-Spread-Drawn (AISD) in Panel A and the All-In-
Spread-Undrawn (AISU) in Panel B on banks’ aggregate risk exposures including Bank Equity Beta (as a
measure of systematic risk), LRMES (as a measure of downside risk; LRMES is the Long Run Marginal Expected
Shortfall, approximated in Acharya et al. (2012) as 1-e^((-18×MES)), where MES is the one-day loss expected in
a bank’s return if market returns are less than -2%), SRISK/Assets (as a measure of equity shortfall in times of a
severe crisis) and Liquidity Risk (as a measure of aggregate drawdown risk and defined as Unused Commitments
plus Wholesale Funding minus Liquidity (% Assets)). We include them individually in regressions (2) to (5) and
(7) to (10). All regressions include bank characteristics: NPL/Loans (Non-performing loans (% Loans)), Capital
(Equity/Assets), Non-Interest Income (Non-interest-income (%Operating revenues)), Bank Size (Log of Total
Assets), Bank Profitability (Return on assets: Net Income / Assets). All regressions further include borrower
characteristics: Equity Volatility (12-months equity volatility), Firm Equity Beta (12-month daily beta with the
S&P 500 return), Firm Size (Log of Total Assets; deflated using the U.S. PPI), Firm Profitability (EBITDA /
Assets), Tangibility (Net PP&E / Assets), Tobin’s Q (Market Assets / Assets), Leverage ((LT Debt + ST Debt) /
Market Assets). All regressions include the LIBOR as well as year and industry (2-digit) fixed effects. Standard
errors are clustered at the firm level. All variables are defined in Appendix III.
Panel A. AISD
(1) (2) (3) (4) (5)
AISD
Bank Equity Beta 0.0582
(0.147)
LRMES 1.293**
(0.039)
SRISK / Assets 1.772
(0.293)
Liquidity Risk -0.330
(0.185)
LIBOR -0.288*** -0.278*** -0.243*** -0.272*** -0.311***
(0.000) (0.001) (0.006) (0.002) (0.000)
Bank Characteristics
NPL / Loans 1.864 2.298 2.120 2.474 1.832
(0.342) (0.261) (0.281) (0.242) (0.339)
Capital -5.395** -4.925** -4.967** -4.981** -5.412***
(0.013) (0.018) (0.013) (0.020) (0.009)
Non-Interest Income -0.0560 -0.0490 -0.171 -0.0628 -0.163
(0.796) (0.820) (0.458) (0.773) (0.476)
Bank Size -0.107*** -0.111*** -0.110*** -0.119*** -0.126***
(0.001) (0.001) (0.001) (0.001) (0.000)
Bank Profitability -10.20** -8.032 0.132 -2.877 -10.47**
(0.042) (0.113) (0.984) (0.741) (0.036)
Firm Characteristics
Equity Volatility 0.360** 0.366** 0.367** 0.364** 0.360**
(0.019) (0.015) (0.015) (0.016) (0.020)
Firm Equity Beta 0.175*** 0.176*** 0.173*** 0.174*** 0.176***
(0.001) (0.001) (0.001) (0.001) (0.001)
Firm Size -0.170*** -0.169*** -0.167*** -0.169*** -0.170***
(0.000) (0.000) (0.000) (0.000) (0.000)
Firm Profitability -0.200 -0.201 -0.193 -0.203 -0.211
(0.327) (0.328) (0.345) (0.318) (0.290)
Tangibility -0.475*** -0.478*** -0.475*** -0.474*** -0.476***
(0.000) (0.000) (0.000) (0.000) (0.000)
Tobin's Q -0.0279** -0.0281** -0.0274** -0.0275** -0.0265**
(0.040) (0.038) (0.042) (0.042) (0.049)
Leverage 1.756*** 1.753*** 1.764*** 1.755*** 1.757***
(0.000) (0.000) (0.000) (0.000) (0.000)
Year Fixed Effect Yes Yes Yes Yes Yes
Industry Fixed Effect Yes Yes Yes Yes Yes
R-squared 0.463 0.463 0.464 0.463 0.463
Number obs. 2657 2657 2657 2657 2657
67
Panel B. AISU
(1) (2) (3) (4) (5)
AISU
Bank Equity Beta 0.0161**
(0.021)
LRMES 0.187*
(0.085)
SRISK / Assets 0.255
(0.382)
Liquidity Risk -0.0253
(0.581)
LIBOR -0.0516*** -0.0488*** -0.0451*** -0.0492*** -0.0534***
(0.000) (0.000) (0.002) (0.001) (0.000)
Bank Characteristics
NPL / Loans 0.565* 0.684** 0.602* 0.652* 0.562*
(0.072) (0.032) (0.054) (0.053) (0.072)
Capital -0.576 -0.446 -0.514 -0.516 -0.577
(0.153) (0.246) (0.173) (0.198) (0.146)
Non-Interest Income 0.0103 0.0122 -0.00638 0.00930 0.00208
(0.795) (0.752) (0.880) (0.815) (0.962)
Bank Size -0.0117** -0.0127** -0.0122** -0.0135** -0.0132**
(0.034) (0.019) (0.022) (0.025) (0.040)
Bank Profitability -1.486* -0.887 0.0100 -0.430 -1.507*
(0.077) (0.300) (0.993) (0.765) (0.074)
Firm Characteristics
Equity Volatility 0.0700*** 0.0716*** 0.0709*** 0.0706*** 0.0700***
(0.000) (0.000) (0.000) (0.000) (0.000)
Firm Equity Beta 0.0482*** 0.0485*** 0.0480*** 0.0480*** 0.0482***
(0.000) (0.000) (0.000) (0.000) (0.000)
Firm Size -0.0326*** -0.0324*** -0.0323*** -0.0325*** -0.0327***
(0.000) (0.000) (0.000) (0.000) (0.000)
Firm Profitability 0.0113 0.0110 0.0123 0.0108 0.0105
(0.716) (0.725) (0.693) (0.726) (0.735)
Tangibility -0.0904*** -0.0913*** -0.0905*** -0.0903*** -0.0905***
(0.000) (0.000) (0.000) (0.000) (0.000)
Tobin's Q -0.0103*** -0.0104*** -0.0103*** -0.0103*** -0.0102***
(0.000) (0.000) (0.000) (0.000) (0.000)
Leverage 0.320*** 0.319*** 0.321*** 0.320*** 0.320***
(0.000) (0.000) (0.000) (0.000) (0.000)
Year Fixed Effect Yes Yes Yes Yes Yes
Industry Fixed Effect Yes Yes Yes Yes Yes
R-squared 0.472 0.473 0.473 0.472 0.472
Number obs. 2657 2657 2657 2657 2657
68
Table 10. Credit-line drawdowns and Conditional SRISK
This table reports the predicted drawdown rates (Drawdown Rate) from credit lines in a stress scenario of 40%
correction to the global stock market (Panel A) and the Slope of the drawdown function (compare Figure 6). In
Panel B, we report the Unused Commitments (C&I loans), and the incremental required capital to fund the
predicted drawdowns (Incremental SRISKCL) using both (stressed) historical drawdown rates: Incremental
SRISKCL = Drawdown Rate x 8% x Unused Commitments (C&I loans). Debt is total liabilities (from NYU Stern
School of Business VLAB site, vlab.stern.nyu.edu/srisk). Panel C reports the calculation of Incremental
SRISKLRMES-C due to the sensitivity of bank stock returns to Liquidity Risk using the minimum (gmin) and maximum
(gmax) sensitivity from different model specifications shown in prior tables. Incremental LRMES-Cmin (%) is
calculated as Liquidity Risk x gmin. Incremental SRISKLRMES-Cmin is calculated as (1 – 8%) x Liquidity Risk x gmin x
MV where MV is market value of bank equity. Other variants are calculated accordingly. In Panel D, we show the
Conditional SRISK (SRISK-C) which is the sum of Incremental SRISKCL and Incremental SRISKLRMES-C. All
variables are defined in Appendix III.
Panel A. Estimating the drawdown rates in a stress scenario
Slope Drawdown Rate
(S&P Return
-40%)
Predicted Quarterly Q1 2020 -0.57 22.91%
Drawdowns Quarterly 2007-2009 -0.27 10.82%
Panel B. Incremental SRISKCL
Incremental Incremental
Unused C&I SRISKCL with SRISKCL with
Commitments Drawdown Drawdown
Name (USD mn) rate: 25.79% rate: 39.97% Debt (USD mn)
JPMORGAN CHASE & CO. 273,278 5,638 8,738 2,496,125
BANK OF AMERICA CORPORATION 310,824 6,413 9,939 2,158,067
CITIGROUP INC. 200,912 4,145 6,424 1,817,838
WELLS FARGO & COMPANY 198,316 4,092 6,341 1,748,234
GOLDMAN SACHS GROUP, INC., THE 111,247 2,295 3,557 913,472
MORGAN STANLEY 78,411 1,618 2,507 818,732
U.S. BANCORP 96,020 1,981 3,070 433,158
TRUIST FINANCIAL CORPORATION 86,995 1,795 2,782 204,178
PNC FINANCIAL SERVICES GROUP, INC., THE 84,238 1,738 2,694 358,342
CAPITAL ONE FINANCIAL CORPORATION 18,618 384 595 320,520
Top 10 BHC 1,458,858 30,099 46,648 11,268,666
Vlab BHC 1,777,617 36,676 56,841 14,524,200
All BHC 1,837,220 37,906 58,747
69
Panel C. Incremental SRISKLRMESC
Incremental Incremental Incremental SRISK LRMES-C (USD mn)
MV (USD mn) LRMES Liquidity Risk gmin gmax LRMES-Cmin LRMES-Cmax at LRMES-Cmin at LRMES-Cmax
JPMORGAN CHASE & CO. 437,226 43.4% 20.3% -0.32 -0.56 6.5% 11.3% 28,411 49,276
BANK OF AMERICA CORPORATION 316,808 45.9% 25.7% -0.32 -0.56 8.2% 14.3% 26,052 45,183
CITIGROUP INC. 174,415 47.3% 37.1% -0.32 -0.56 11.9% 20.6% 20,690 35,883
WELLS FARGO & COMPANY 227,540 44.9% 24.2% -0.32 -0.56 7.7% 13.4% 17,612 30,546
GOLDMAN SACHS GROUP, INC., THE 81,415 54.2% 28.7% -0.32 -0.56 9.2% 15.9% 7,471 12,958
MORGAN STANLEY 82,743 51.1% 14.3% -0.32 -0.56 4.6% 7.9% 3,781 6,557
U.S. BANCORP 92,603 36.6% 46.3% -0.32 -0.56 14.8% 25.7% 13,730 23,813
TRUIST FINANCIAL CORPORATION 75,544 42.5% 41.1% -0.32 -0.56 13.2% 22.8% 9,943 17,245
PNC FINANCIAL SERVICES GROUP, INC., THE 69,945 40.1% 39.9% -0.32 -0.56 12.8% 22.1% 8,928 15,485
CAPITAL ONE FINANCIAL CORPORATION 47,927 49.2% 18.6% -0.32 -0.56 5.9% 10.3% 2,849 4,942
Top 10 BHC 1,606,166 9.5% 16.4% 139,467 241,888
VLAB BHC 2,226,522 168,438 292,134
All BHC 2,408,434 177,412 307,699
Panel D. SRISKC (USD mn)
SRISK (Q4 2019) SRISK-Cmin SRISK-Cmax
w/o neg w/ neg
Name SRISK SRISK
JPMORGAN CHASE & CO. 0 -27,848 34,050 58,014
BANK OF AMERICA CORPORATION 14,898 14,898 32,465 55,122
WELLS FARGO & COMPANY 24,425 24,425 21,704 36,887
CITIGROUP INC. 60,887 60,887 24,835 42,308
AMERICAN EXPRESS COMPANY 0 -35,344 5,688 9,864
U.S. BANCORP 0 -19,352 15,711 26,883
MORGAN STANLEY 28,302 28,302 5,398 9,064
GOLDMAN SACHS GROUP, INC., THE 38,774 38,774 9,766 16,515
TRUIST FINANCIAL CORPORATION 0 -23,608 11,738 20,026
PNC FINANCIAL SERVICES GROUP, INC., THE 0 -9,895 10,666 18,179
Total (Top 10 Banks) 167,287 51,238 172,020 292,863
Total (Vlab Banks) 195,033 40,994 205,113 348,975
Total (All Sample Banks) 215,318 366,446
70
Appendix I. Example – Drawdowns during COVID-19
71
Appendix II. Sample Banks
Name Total Assets Name Total Assets Name Total Assets
JPMORGAN CHASE & CO. 2,687,379 UMPQUA HOLDINGS CORPORATION 28,847 PROVIDENT FINANCIAL SERVICES, INC. 9,809
BANK OF AMERICA CORPORATION 2,434,079 PINNACLE FINANCIAL PARTNERS, INC. 27,805 NBT BANCORP INC. 9,716
CITIGROUP INC. 1,951,158 WESTERN ALLIANCE BANCORPORATION 26,822 FIRST BUSEY CORPORATION 9,696
WELLS FARGO & COMPANY 1,927,555 INVESTORS BANCORP, INC. 26,773 OFG BANCORP 9,298
GOLDMAN SACHS GROUP, INC., THE 992,996 PACWEST BANCORP 26,771 CAPITOL FEDERAL FINANCIAL, INC. 9,255
MORGAN STANLEY 895,429 UMB FINANCIAL CORPORATION 26,561 EAGLE BANCORP, INC. 8,989
U.S. BANCORP 495,426 COMMERCE BANCSHARES, INC. 26,084 SERVISFIRST BANCSHARES, INC. 8,948
TRUIST FINANCIAL CORPORATION 473,078 STIFEL FINANCIAL CORP. 24,610 BOSTON PRIVATE FINANCIAL HOLDINGS, INC. 8,832
PNC FINANCIAL SERVICES GROUP, INC., THE 410,373 FLAGSTAR BANCORP, INC. 23,265 S&T BANCORP, INC. 8,765
CAPITAL ONE FINANCIAL CORPORATION 390,365 FULTON FINANCIAL CORPORATION 21,862 SANDY SPRING BANCORP, INC. 8,629
BANK OF NEW YORK MELLON CORPORATION, THE 381,508 SIMMONS FIRST NATIONAL CORPORATION 21,265 BANCFIRST CORPORATION 8,566
CHARLES SCHWAB CORPORATION, THE 294,005 OLD NATIONAL BANCORP 20,412 PARK NATIONAL CORPORATION 8,563
STATE STREET CORPORATION 245,610 FIRST HAWAIIAN, INC. 20,167 FIRST COMMONWEALTH FINANCIAL CORPORATION 8,309
AMERICAN EXPRESS COMPANY 198,314 UNITED BANKSHARES, INC. 19,662 FIRST FINANCIAL BANKSHARES, INC. 8,262
ALLY FINANCIAL INC. 180,644 AMERIS BANCORP 18,243 OCEANFIRST FINANCIAL CORP. 8,260
FIFTH THIRD BANCORP 169,369 BANK OF HAWAII CORPORATION 18,095 COLUMBIA BANK MHC 8,187
CITIZENS FINANCIAL GROUP, INC. 166,090 CATHAY GENERAL BANCORP 18,094 BROOKLINE BANCORP, INC. 7,875
KEYCORP 145,570 FIRST MIDWEST BANCORP, INC. 17,850 BANC OF CALIFORNIA, INC. 7,828
NORTHERN TRUST CORPORATION 136,828 ATLANTIC UNION BANKSHARES CORPORATION 17,563 TRISTATE CAPITAL HOLDINGS, INC 7,766
REGIONS FINANCIAL CORPORATION 126,633 CENTERSTATE BANK CORPORATION 17,142 ENTERPRISE FINANCIAL SERVICES CORP 7,334
M&T BANK CORPORATION 119,873 WASHINGTON FEDERAL, INC. 16,423 SEACOAST BANKING CORPORATION OF FLORIDA 7,109
DISCOVER FINANCIAL SERVICES 113,996 SOUTH STATE CORPORATION 15,921 FLUSHING FINANCIAL CORPORATION 7,018
HUNTINGTON BANCSHARES INCORPORATED 109,002 WESBANCO, INC. 15,719 HOMESTREET, INC. 6,812
SYNCHRONY FINANCIAL 104,826 HOPE BANCORP, INC. 15,668 SOUTHSIDE BANCSHARES, INC. 6,749
COMERICA INCORPORATED 73,519 HILLTOP HOLDINGS, INC 15,172 TOMPKINS FINANCIAL CORPORATION 6,726
SVB FINANCIAL GROUP 71,384 HOME BANCSHARES, INC. 15,032 LAKELAND BANCORP, INC. 6,712
E*TRADE FINANCIAL CORPORATION 61,416 INDEPENDENT BANK GROUP, INC. 14,958 1ST SOURCE CORPORATION 6,623
PEOPLE'S UNITED FINANCIAL, INC. 58,580 FIRST INTERSTATE BANCSYSTEM, INC. 14,644 KEARNY FINANCIAL CORPORATION 6,610
NEW YORK COMMUNITY BANCORP, INC. 53,641 FIRST FINANCIAL BANCORP 14,512 DIME COMMUNITY BANCSHARES, INC. 6,354
POPULAR, INC. 52,115 COLUMBIA BANKING SYSTEM, INC. 14,080 MERIDIAN BANCORP, INC. 6,344
CIT GROUP INC. 50,833 GLACIER BANCORP, INC. 13,684 FIRST FOUNDATION INC. 6,314
SYNOVUS FINANCIAL CORP. 48,203 TRUSTMARK CORPORATION 13,498 CONNECTONE BANCORP, INC. 6,174
TCF FINANCIAL CORPORATION 46,672 RENASANT CORPORATION 13,401 FIRST BANCORP 6,144
EAST WEST BANCORP, INC. 44,196 BERKSHIRE HILLS BANCORP, INC 13,217 MIDLAND STATES BANCORP, INC. 6,087
FIRST HORIZON NATIONAL CORPORATION 43,314 HEARTLAND FINANCIAL USA, INC. 13,210 CENTRAL PACIFIC FINANCIAL CORP. 6,013
BOK FINANCIAL CORPORATION 42,324 UNITED COMMUNITY BANKS, INC. 12,919 NATIONAL BANK HOLDINGS CORPORATION 5,896
RAYMOND JAMES FINANCIAL, INC. 40,154 GREAT WESTERN BANCORP, INC. 12,852 WESTAMERICA BANCORPORATION 5,646
FIRST CITIZENS BANCSHARES, INC. 39,824 FIRST BANCORP 12,611 REPUBLIC BANCORP, INC. 5,620
VALLEY NATIONAL BANCORP 37,453 BANNER CORPORATION 12,604 HANMI FINANCIAL CORPORATION 5,538
WINTRUST FINANCIAL CORPORATION 36,608 FIRST MERCHANTS CORPORATION 12,457 UNIVEST FINANCIAL CORPORATION 5,381
F.N.B. CORPORATION 34,620 AXOS FINANCIAL, INC. 12,269 TRIUMPH BANCORP, INC. 5,060
CULLEN/FROST BANKERS, INC. 34,097 WSFS FINANCIAL CORPORATION 12,256 CITY HOLDING COMPANY 5,019
BANKUNITED, INC. 32,871 INTERNATIONAL BANCSHARES CORPORATION 12,113 QCR HOLDINGS, INC. 4,909
TEXAS CAPITAL BANCSHARES, INC. 32,548 PACIFIC PREMIER BANCORP, INC. 11,776 GERMAN AMERICAN BANCORP, INC. 4,399
ASSOCIATED BANC-CORP 32,386 CUSTOMERS BANCORP, INC 11,521 FIRST FINANCIAL CORPORATION 4,020
PROSPERITY BANCSHARES, INC. 32,195 FIRST AMERICAN FINANCIAL CORPORATION 11,519 BUSINESS FIRST BANCSHARES, INC. 2,276
IBERIABANK CORPORATION 31,713 COMMUNITY BANK SYSTEM, INC. 11,410 CHEMUNG FINANCIAL CORPORATION 1,788
STERLING BANCORP 30,639 INDEPENDENT BANK CORP. 11,403
HANCOCK WHITNEY CORPORATION 30,620 CVB FINANCIAL CORP. 11,282
WEBSTER FINANCIAL CORPORATION 30,424 NORTHWEST BANCSHARES INC 10,638
72
Appendix III. Variable definitions
Variable name Definition Source
Assets Total Assets Call Reports
Capital Buffer Difference between a bank’s equity–asset ratio and the cross-sectional average of the equity–asset-ratio of all sample Call Reports
banks in Q4 2019
Consumer Loans / Assets Consumer loans (%Assets) Call Reports
Credit Card Commitments / Assets Unused credit card commitments (%Assets) Call Reports
Credit Lines Indicator if loan type within list: Dealscan
Cumulative Total Drawdowns Natural logarithm of the realized daily cumulative credit-line drawdowns across all firms 8-K
Cumulative BBB Drawdowns Natural logarithm of the realized daily cumulative credit-line drawdowns across all BBB-rated firms 8-K
Cumulative NonIG Drawdowns Natural logarithm of the realized daily cumulative credit-line drawdowns across all NonIG rated firms 8-K
Cumulative Not Rated Drawdowns Natural logarithm of the realized daily cumulative credit-line drawdowns across all unrated firms 8-K
Current Primary Dealer Indicator Indicator = 1 if bank is current primary dealer bank (https://www.newyorkfed.org/markets/primarydealers#primary- NY Fed
dealers)
Debt Market value of bank liabilities (12/31/2019) Vlab
Deposits / Assets Deposits (%Assets) Call Reports
Deposits / Loans Deposits (%Loans) Call Reports
Derivatives / Assets Interest rate, exchange rate and credit derivatives (% Assets) Call Reports
Distance-to-Default Mean(ROA+CAR)/volatility(ROA) where CAR is the capital-to-asset ratio and ROA is return on assets Call Reports
Drawdown Rate Sensitivity of changes in credit-line drawdowns to changes in the market returns (projected in a market downturn of Capital IQ, 8-K, CRSP
40%)
Equity Beta Constructed using monthly data over the 2015 to 2019 period and the S&P 500 as market index CRSP
Equity Ratio Equity (%Assets) Call Reports
Fees Earned Fees and interest earned minus opportunity cost of capital for every credit line summed up over all borrowers Dealscan, Capital IQ,
CRSP
Gross Drawdowns Percentage change of banks’ off-balance-sheet unused C&I commitments between Q4 2019 and Q1 2020 Call Reports
HML Fama-French-Factor: High-minus-Low (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f- Ken French Website
f_bench_factor.html)
Idiosyncratic Volatility Annualized standard deviation of the residuals from the market model CRSP
Income Diversity 1 minus the absolute value of the ratio of the difference between net interest income and other operating income to total Call Reports
operating income
Incremental SRISKCL Equity capital that would be required to fund new loans based on banks’ unused commitments (CL = credit lines) at the Call Reports
end of Q4 2019
Incremental SRISKLRMESC (Marginal) equity shortfall of banks based on their end of Q4 2019 market values of equity due to effect of liquidity risk Call Reports
on stock returns
Liquidity The sum of cash, federal funds sold & reverse repos, and securities excluding MBS/ABS securities. Call Reports
Liquidity Risk Unused Commitments plus Wholesale Funding minus Liquidity (% Assets) Call Reports
Loan Either natural log of loan amount or natural log of 1+number of loans Dealscan
Loans / Assets Total loans (%Assets) Call Reports
Log(Assets) Natural log of Assets Call Reports
LRMES LRMES is the Long Run Marginal Expected Shortfall, approximated in Acharya et al. (2012) as Call Reports
73
1-e^((-18×MES)), where MES is the one-day loss expected in bank i’s return if market returns are less than -2%
LRMESC Contingent marginal expected shortfall due to the impact of liquidity risk on bank stock returns. Call Reports, CRSP
MV Loss Covid Market equity loss during the 1/1/2020 – 3/23/2020 period (USD mn) as % of market equity as of 1/1/2020 CRSP
Net Drawdowns Absolute change in banks’ unused C&I commitments minus the change in deposits (% Assets) over the same period Call Reports
Non-Interest Income Non-interest-income (%Operating revenues) Call Reports
NPL / Loans Non-performing loans (%Loans) Call Reports
Post Post is defined as the period starting April 1, 2020
Ratings: Not Rated, AAA-A, BBB, Indicator variables equal to 1 if firms are in either rating category CapitalIQ
NonIG Rated
Slope of the regression of weekly excess stock returns on the Fama and French real estate industry excess return in a CRSP
Real Estate Beta regression that controls for the MSCI World excess return
Repayments Total repayment of credit lines by customers in Q2 as % of 2019Q4 commitments CapitalIQ, Dealscan
Return 1/1-3/23/2020 Cumulative stock return from January 1 to March 23, 2020; log excess returns are calculated as the log(1 + r - rf), where CRSP
r is the simple daily return (based on the daily closing price, adjusted for total return factor and daily adjustment factor),
and rf is the 1-month daily Treasury-bill rate
ROA Return on assets: Net Income / Assets Call Reports
S&P 500 Return (Daily) excess return of the S&P 500 index; log excess returns are calculated as the log(1 + r - rf), where r is the simple CRSP
daily return (based on the daily closing price, adjusted for total return factor and daily adjustment factor), and rf is the 1-
month daily Treasury-bill rate
SMB Fama-French-Factor: Small-minus-Big (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f- Ken French Website
f_bench_factor.html)
SRISK Bank capital shortfall in a systemic crisis as in Acharya et al. (2012); See NYU Stern Volatility & Risk Institute, Vlab
https://vlab.stern.nyu.edu/welcome/srisk, Acharya et al. (2016) and Brownlees and Engle (2017) for definition and
estimation of LRMES and SRISK.
SRISK/Assets SRISK scaled by total assets Vlab and Call Reports
SRISKC Incremental SRISKCL + Incremental SRISKLRMES-C Call Reports
Term Loan Indicator if loan type within list: Dealscan
Unused C&I Commitments Unused C&I credit lines Call Reports
Unused Commitments The sum of credit lines secured by 1-4 family homes, secured and unsecured commercial real estate credit lines, Call Reports
commitments related to securities underwriting, commercial letter of credit, and other credit lines (which includes
commitments to extend credit through overdraft facilities or commercial lines of credit)
Wholesale Funding The sum of large time deposits, deposited booked in foreign offices, subordinated debt and debentures, gross federal Call Reports
funds purchased, repos and other borrowed money.
74
Appendix IV. Different measures for “COVID-19-affected industries”
This table shows the “COVID-19-affected industries” definition used to construct portfolio risk proxies.
Variable name Explanation
Stock Performance 20 industries with worst stock performance as in Fahlenbrach et al. (2021)
COVID industries Firms that are part of the Fama-French 49 industries identified by Moody’s (2020)
as particularly exposed to COVID-19.
Customer share Customer share as defined by Koren and Peto (2020) at the three-digit NAICS
level. Measures the percentage of workers in customer-facing occupations.
Exposed firms belong to industries in the top quartile of the customer share
distribution.
Telework Share of jobs that can be performed at home from Dingel and Neiman (2020),
defined at the three-digit NAICS industry level. Exposed firms are part of
industries in the bottom quartile of the distribution.
Manual classification Manual classification of industries at the six-digit NAICS level. These are the
firms we manually classified as highly affected in Fahlenbrach et al. (2021).
Presence Share Presence share as defined by Koren and Peto (2020) at the three-digit NAICS
level. Measures the percentage of workers in occupations requiring physical
contact. Exposed firms belong to industries in the top quartile of the presence
share distribution.
Teamwork Share Teamwork share as defined by Koren and Peto (2020) at the three-digit NAICS
level. Measures the percentage of workers in teamwork-intensive occupations.
Exposed firms belong to industries in the top quartile of the teamwork share
distribution.
YoY Sale Decline Q2 2020 year-on-year change in sales, defined at the firm level. Exposed firms are
the ones in the bottom quartile of the change in sales.
Abnormal employment Abnormal employment decline in the industry between 2019:Q2 and 2020:Q2 at
decline the three-digit NAICS level as in Chodorow-Reich et al. (2022). Exposed firms
belong to industries in the top quartile of the distribution.
Physical proximity To what extent does this job require the worker to perform job tasks in close
physical proximity to others (at the three-digit NAICS)? Based on ONET survey.
Exposed firms belong to industries in the top quartile of the distribution.
Face-to-face discussion How often do you have to have face-to-face discussions with individuals or teams
in this job (at the three-digit NAICS)? Based on ONET survey. Exposed firms
belong to industries in the top quartile of the distribution.
External customers How important is it to work with external customers (at the three-digit NAICS)?
Based on ONET survey. Exposed firms belong to industries in the top quartile of
the distribution.
75
File and source
- File
- why-did-bank-stocks-crash-during-covid-19.pdf
- Size
- 2,806,650 bytes
- SHA-256
- 66bb54c356a06dc664baf929db75026ba43c9a679da4a8fe5192d654390c7a40
- Original
- doi.org