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The Disproportionate Costs of Uncertainty:Evidence from Dodd Frank and Small Banks
Working Paper
Raymond Kim †
A. Gary Anderson School of ManagementUniversity of California at Riverside‡
January 16, 2018
Abstract
I examine the disproportionate impact of regulatory uncertainty on smaller banks.Baker, Bloom, and Davis (2016) indicates that firm level policy exposures matters foreconomic outcomes. I find that cost of regulatory uncertainty has a greater impact onsmaller banks with fewer resources and limited access to capital markets. Uncertaintystems from delayed Dodd Frank rulings on the cost of bank hedging activities. Usingfirm fixed effects with interactions, I find that during regulatory uncertainty, smallerbanks with an increase of 1% in compliance costs leave 18% more balance sheet riskexposures and provide 8.1% fewer corporate clients with hedging services. These find-ings support the need for tailored regulations that reduce the cost of uncertainty forsmaller banks.
JEL classification:
Keywords : Regulatory uncertainty, banking regulation, hedging, risk management, interestrate risk, derivatives, mortgage backed securities, Dodd Frank
†Advisor: Dr. Jean Helwege, Area Coordinator for Finance, University of California at Riverside. I wouldlike to thank Dr. Jean Helwege for her guidance and mentorship. I would also like to thank Peter Chungand Scott Hein for their helpful comments.
‡University of California at Riverside, 900 University Ave. Riverside, CA 92521email:[email protected]
1. Introduction
During the global financial crisis, concerns about policy uncertainty intensified as central
banks and corporate firms noted that uncertainties about policies were contributing to steep
economic declines and tepid recoveries. Measures of economic policy uncertainty (EPU)
Baker et al. (2016) and monetary policy uncertainty (MPU) Husted, Rogers, and Sun (2017)
have measured policy uncertainty that foreshadow declines in business investment and ac-
tivity. In banking, Gissler, Oldfather, and Ruffino (2016) looks at uncertainty of mortgage
regulation and it’s negative effects on bank lending at the firm level while Bordo, Duca,
and Koch (2016) also looks at EPU and it’s aggregate effect lending. In a highly regulated
industry like banking, academic literature also highlights costs of funding uncertainty and
crisis regulations. 1.
This paper measures the costs of policy uncertainty by looking at the bank’s hedging ac-
tivities. Given that banks are repositories for interest rate risk Gorton and Rosen (1995),
the function of bank hedging activity is a central risk management function. As far as I
know, this is the first paper to look at the effects of regulatory on hedging activity and its
disproportionate impact on smaller banks. Using a unique dataset from the Conference of
State Board Supervisors and quarterly Call Report data from the Federal Reserve, I ex-
amine a period of regulatory uncertainty regarding the undecided risk weightings of bank
balance sheet assets, which impacted smaller banks that were well capitalized during the
global financial crisis. 2. This period of uncertainty starts from the passage of The Dodd-
Frank Wall Street Reform and Consumer Protection Act in 2010 until February 2015 when
risk weightings for regulatory capital were finally released by the Federal Insurance Deposit
Corporation (FDIC) and the Federal Reserve FDIC (2015) 3. Dodd Frank’s regulations were
mostly centered around regulating complicated large too-big-to-fail (TBTF) banks, which
drew out the process of final rulings on various regulatory measures such as risk weightings
for interest rate derivatives. This unusually long period of regulatory uncertainty in a highly
regulated industry provides a unique environment to study the costs of uncertainty on a
bank’s hedging activities.
1 Ritz and Walther (2015) points to bank level differences in lending due to costs of funding uncertaintywhile Banerjee and Mio (2017) and Cyree (2016) research the actual effects of crises regulation on banks.
2”[[The impact] of changing risk weight calculations [on assets] is surprising to many that have been andremain well capitalized through the most recent economic difficulties.” in a letter by the American Banker’sAssociation to the Office of the Comptroller of the Currency and the Board of Governors of the FederalReserve System on October 25, 2012
3 [”Proposed changes starting March 31, 2015] will include an increased number of risk-weight cate-gories to which on-balance sheet assets, derivatives, off-balance sheet items, and other items subject to riskweighting would be allocated.” in a news release by the FDIC on February 20, 2015 FDIC (2015)
1
Using a unique dataset of surveyed compliance costs collected by the Conference of State
Board Supervisors, Dahl, Meyer, and Neely (2016a) as found that smaller banks have been
disproportionately burdened by regulation intended for ”Too Big to Fail” (TBTF) banks.
Dahl et al. (2016a) finds that the smallest banks faced Dodd Frank compliance costs of 8.7%
of noninterest expenses vs. 2.9% for medium sized banks. Given that Dodd Frank was
enacted to curb excessive risk taking by TBTF banks, a natural question emerges: What
are the costs to non-TBTF banks regarding one size fits all regulatory measures? Does
uncertainty surrounding pending regulations affect non-TBTF banks? As smaller banks
prefer less risk Hein, Koch, and MacDonald (2005) and more conservative capital structures
Purnanandam (2007), has their ability to hedge interest rate risk been burdened? Examining
this question requires an understanding of the period of uncertainty that non-TBTF banks
experienced.
The Dodd-Frank Wall Street Reform and Consumer Protection Act was passed in 2010 as
a response to the financial crisis of 2008. Over 22,000 pages long and intended to decrease
risks to the US financial system, many smaller banks have been snared in an increasingly
complex set of rules designed for larger banks.
”it’s important to look for ways to relieve regulatory burden on community
banks and smaller institutions to tailor regulation so that it’s appropriate for the
systemic risk profile of the particular institutions”
- Federal Reserve Chairman Janet Yellen
Press Conference
December 14, 2016
Janet Yellen, the Federal Reserve Chairman made these remarks regarding the regulatory
burden that smaller banks have faced throughout the implementation of Dodd Frank. This
naturally leads to the question, did uncertainty regarding Dodd Frank’s regulations restrict
smaller banks in unintended ways, such as restricting their ability to reduce balance sheet
risk so that they can make more loans and grow their lending assets? In particular, this paper
focuses on the beginning of uncertainty for banks in Dodd Frank’s Subpart D, Section 34
which outlines future changes for risk weighting of interest rate derivatives. By eliminating
the 50% risk weight cap on interest rate derivatives, Dodd Frank waved a broad stroke
across all types of derivatives throwing smaller banks into uncertainty regarding the new
weightings of their hedging portfolios. Elimination of the risk weight cap on interest rate
derivatives opened up uncertainty as to what these new risk weight caps would actually be.
2
This uncertainty would last from the passage of Dodd Frank in 2010 until February 20, 2015
when the Federal Deposit Insurance Corporation (FDIC) released a last round of changes
before the first Dodd Frank regulatory risk weightings were to be used in March 31, 2015
FDIC (2015). This long period of regulatory uncertainty would have a particularly negative
cost for banks with limited resources for regulatory compliance. The literature shows that
high costs of compliance for smaller banks translate into having fewer staff to efficiently
process regulatory costs Dahl et al. (2016a), and regulatory uncertainty would tax smaller
banks into simply reducing the use of interest rate derivatives. This type of behavior would
fall in line with Boyson, Helwege, and Jindra (2014) and the selling of assets by banks in
order to meeting regulatory requirements.
Such reduction in hedging would also be broadly consistent with literature on banking regu-
lation and how uncertainty has negative effects on bank lending (Gissler et al., 2016; Bordo
et al., 2016), contributing a banking perspective to the growing literature on effects of eco-
nomic policy uncertainty (Baker et al., 2016; Brogaard and Detzel, 2015; Bonaime, Gulen,
and Ion, 2016; Koijen, Philipson, and Uhlig, 2016; Giavazzi and McMahon, 2012) on the
economy. In order to extend the literature to include the costs of regulatory uncertainty on
bank hedging, the next section will outline the use of interest rate derivatives in banking.
2. Interest Rate Derivatives in Bank Hedging
Literature in this growing field focused on the positive relationship between derivative use
and loan growth (Brewer III, Minton, and Moser (2000))(Landier, Sraer, and Thesmar (2013)
since loans expose banks to macroeconomic cash flow risks and interest rate derivatives help
to mitigate these cash flow risks. So as banks increase their loans held to maturity, they are
exposed to more interest rate risk, which increases the benefit of mitigating interest rate risk
by using interest rate derivatives. Lending policies become less sensitive to macroeconomic
shocks with the use of interest rate derivatives Purnanandam (2007) and subsequently lend
more than non users. However, use of derivatives for portfolio hedging has differed between
TBTF banks and smaller banks. While non-TBTF banks use interest rate derivatives to
hedge, allowing a bank to address other balance sheet risks more relevant to it’s simpler
banking model, TBTF banks additionally use CDS to hedge credit risk for bonds held as
investment and as credit insurance to sell bonds to buyersShan, Tang, and Yan (2014).
As banks face cash flow risk from mismatched maturities, repricing risk, bankruptcy risk
Smith and Stulz (1985), financing risks Froot, Scharfstein, and Stein (1993), interest rate
3
risk, monetary policy risks, and market risks, derivative use has become common practice
to hedge exposures to balance sheet risk. Larger banks use interest rate derivatives not only
for hedging4, but also dealer intermediation5 , and speculation6
The use of interest rate derivative in banks can be likened to a the next layer of risk manage-
ment after the fixed-rate deposit insurance system, such as the Federal Deposit Insurance
Corporation (FDIC). By insuring a bank’s liabilities, banks can borrow at below market
rates from depositors, and invest in riskier loans at higher interest rates. As Merton (1977)
noted, insured deposits act as a “put option” on bank assets with a strike price equal to
the maturity value of its debt. The diversification of financial services over the past several
decades were argued to contribute to the stability of the financial system as Demsetz and
Strahan (1995) looked at data from 1980-1993 and found that large bank holding compa-
nies (BHC) were indeed more diversified in their lending portfolio than smaller BHC’s. It
was during this time that banks started to increase their use of interest rate derivatives
for risk management purposes. Brewer III et al. (2000) noted how since 1985, commercial
banks started to use interest rate derivative products. By using derivatives to address the
credit risk and interest rate risk of their loans, banks that used these derivatives experienced
greater growth in their lending portfolios than banks that didn’t. The use of derivatives
allowed banks to focus on their comparative advantage in credit monitoring Diamond (1984)
So how does this increasing use of derivatives for hedging help grow a bank’s lending port-
folio? Much of the academic literature revolves around a bank using derivatives to manage
interest rate exposures, monetary policy risks, and external financing risks. Diamond (1984)
model suggests that derivatives lead to a reduction in loan costs that provide incentives
for banks to lend more. Brewer III et al. (2000) show that empirically there is a positive
relationship between interest rate derivatives and commercial and industrial loans. Banks
also use derivatives to hedge themselves against costly external financing and the cost of
financial distress. Banks that use derivatives are more immune to policy shocks in lending
volumes, while non-derivative users experience significantly declining volumes Purnanandam
(2007). The use of derivatives in risk management allows banks to weather storms better,
and strong risk management at banks has been successful in mitigating “tail risk”, leading
to lower non-performing loans and better operating performance during the financial crisis
4 Brewer, Minton and Moser (2000) found that banks that use interest rate derivatives experience greatergrowth in their loan portfolios than banks that did not use them.
5Begenau, Piazzesi and Schneider, 2015 find that banks use pay-fixed positions in swaps to insure againstsurprise interest rate increases. Hentschel and Kothari (2001) and Chernenko and Faulkender (2011) alsoshow this empirically, while Jermann and Yue (2012) use a theoretical framework to study why non-financialfirms need pay-fixed swaps
6Gorton and Rosen 1995 find that agents claim that speculative risk taking was unintentional.
4
Ellul and Yerramilli (2013). Greater derivative use and a greater fraction of income from
non-banking activities have become an accepted practice of effective bank risk management.
With adequate bank capitalization and derivative skills, loans and interest rate derivatives
can be seen as complimentary assets that move together on a bank’s balance sheet. However,
this relationship can become one of substitution when a regulatory uncertainty lowers the
marginal risk weighted return of a derivative, so that the re-calibration of a t+1 equilibrium
will have a substitution effect between the two assets. Boyson et al. (2014) outlines the
process of how a bank can sell assets in order to meet regulatory requirements such as
capital adequacy ratios. As the cost of uncertainty literature has shown, anticipation of such
change can also have economic costs for banks as well.
3. Data
We obtained the data for this study from three main sources: 1) Call Reports from the Federal
Reserve for quarterly financial data of all US insured commercial banks, 2) Compliance cost
data from the Conference of State Board Supervisors and 3) Chicago Board of Exchange for
swap interest rate data, covering the period from 2010 Q4 to 2017 Q3. 2010 Q4 was chosen
because it is the first full reporting quarter after Dodd Frank was signed into federal law by
President Barack Obama on July 21, 2010. Implementation of new risk weight and capital
adequacy ratios was implemented January 1, 2015 for non-TBTF banks. Final details of risk
weightings for off balance sheet items were not released until February 20, 2015, mandating
2015 Q1 as the first reporting quarter where regulatory certainty was established.
TBTF banks were dropped from the data in order to study the effects of regulatory uncer-
tainty on firms that were not subject to additional regulatory requirements such as stress
tests. For the logistical regression in Table 3, all non TBTF bank observations were included
to test whether the amount of loan assets were an effective proxy for interest rate risk and
whether there is predictive power for a bank hedges it’s interest rate risk. For the sum-
mary statistics and all other regressions, banks with non-missing values for mortgages and
interest rate derivatives were used. Consolidated and domestic bank data were merged and
duplicates were eliminated. Due to various changes to reporting requirements such as the
implementation of Dodd Frank, some call report variables are not consistent over time. Con-
sistent time series were formed by looking at the Call Report forms and matching variables
as they change from quarter to quarter. All bank quarter observations reflect banks with
5
active mortgage lending and derivatives divisions. The total number of bank quarter obser-
vations are 16,930 from 2010 Q4 to 2017 Q3. Summary statistics show that banks increased
in asset size and all ratios after regulatory certainty was established. Concerns that increase
in assets, lending and hedging are endogenous to an economic recovery are addressed by the
use of two way and three way interaction variables between compliance burden, regulatory
certainty, and covariates in Table 5.
In Table 4, pooled OLS where compliance cost ratios were regressed on balance sheet co-
variates demonstrating that compliance cost is highly negatively related to bank size. Data
on compliance costs were derived from Dahl et al. (2016a) and Call Reports. Covariates
were log scaled and bank observations were pooled across 2010 Q4 - 2017 Q3. Tier 1 Ratio
was obtained from Schedule RC-R of the Call Reports. Off balance sheet data on bank’s
use of interest rate derivatives were obtained from the Schedule RC-L of the quarterly Call
Reports. Interest rate derivatives are defined as total gross notional amounts of interest
rate derivative contracts held by either trading or non trading purposes. In examining the
off balance sheet data, about 80-90% of derivative use consists of interest rate derivatives.
Trading and non trading interest rate derivatives were combined for the variable ”Interest
Rate Derivatives”.
Bank loan data was taken from the Schedule RC-P and Schedule RC-C of the quarterly
Call Reports. While Schedule RC has overall loan data, it does not break out residential
mortgages, which is listed in more detail in the Schedule RC-C and RC-P. The Schedule
RC-P reports a bank’s residential mortgage activities while the Schedule RC-C reports a
bank’s general loans and leases activity. Residential mortgage loans held to maturity is not
directly given in the Call Reports, so it must be calculated. Total residential mortgage loans
are calculated by combining mortgages secured by first and junior liens in the Schedule RC-
C. This figure includes both loans held to maturity and loans held for sale or trading. In
order to subtract out loans held for sale or trading, the Schedule RC-P was used to calculate
this figure. By using the amount of residential mortgages held to maturity, we can isolate
bank risk management in regards to macro and interest rate risk in its long term balance
sheet. Residential mortgages did not have it’s risk weighting and capital treatment of 50%
affected in Dodd Frank regulation. The first differences of this variable measures the change
in willingness of banks to hold loans on it’s long term balance sheet. This variable can be
shown in simple form:
Loans Held to Maturityresidential = Loanstotal − Loansheldforsale
6
Table 3 also shows that residential mortgages are significant estimators of interest rate deriva-
tive usage by banks using both logit regression. This supports our use of residential mort-
gages held for investment as a proxy for interest rate risk. Loans Held to Maturity represent
a loan that is of higher credit quality and lower risk than a loan held for sale. n order to
isolate the risk management function of derivatives to balance sheet risk, control factors
were used. Banks mainly use interest rate derivatives for three reasons: hedging Brewer III
et al. (2000), dealer intermediation Begenau, Piazzesi, and Schneider (2015) Hentschel and
Kothari (2001) Chernenko and Faulkender (2012)Jermann and Yue (2013), and speculation
Gorton and Rosen (1995). In order to control for dealer activity and speculation,Chicago
Board of Options Exchange (CBOT) data consisting of the 10-Year Interest Rate Swaps
were used.
Interest rate swaps are a core derivative product that banks use for dealer activities as well
as hedging their balance sheet interest rate risk. Gorton and Rosen (1995) show that interest
rate risk is non diversifiable and banks are repositories of interest rate risk. This interest
rate risk is hedged using swaps ( Gorton and Rosen (1995)) and in larger banks, swaps used
for hedging are difficult to separate from speculation. Recent interest rate swap models are
outlined in Begenau et al. (2015) and Vuillemey (2015). For the purposes of this paper,
interest rate swaps are in demand by bank clients who have floating interest rate liabilities.
When interest rates fall, companies seek to lock in lower fixed interest rate payments and
takes a fixed payer swap position with a bank. This is why we expect a banks interest rate
derivative assets to increase when interest rates fall and volatility rises. Companies want to
lock in lower interest rates and also hedge the volatility of their interest rate exposures.
4. Empirical Model and Results
To test the impact of regulatory uncertainty on bank core operations, I designed two empirical
tests using panel data from 2010 Q4 to 2017 Q3.
4.1. Logistic Regression of Loans as a Predictor of Bank Hedging
The first tests whether loans held on a bank balance sheet is a significant predictor of banks
hedging with interest rate derivatives. Significant results will suggest that mortgage loans
are a proxy for bank interest rate risk. The greater interest rate risk a bank has, the more
7
likely they will use interest rate derivatives to hedge that balance sheet risk. Using a logistic
regression design, I use a cross section of banks in the latest available quarter (2017 Q3)
that files quarterly financial reports with the Federal Reserve. Table 3 tests the following
empirical model:
ln[ pi1− pi
]= α + β1(Loan Assets)i + β2
Tier 1 CapitaliRiskWeightedAssetsi
+ εi
Where pi is the probability that bank i is a user of interest rate derivatives are defined as
Interest Rate Derivativesi = Non Trading IRDi + Trading IRDi
For independent variables I use quarterly holding of loan assets on the balance sheet to see
whether amount of loans is a contemporaneous predictor of hedging use by a bank. For the
purposes of this logit regression, loan assets are scaled in units of a $1 billion.
Loan Assets ∈[Residential Mortgages, Commerical & Industrial Loans
]Tier 1 Ratio is used as a control for financial health of a bank. Tier 1 ratio is a measure
of a bank’s financial strength and ability to absorb unexpected losses. Logistic regression
results show that both residential mortgages and consumer and industrial loan assets are
economically and statistically significant predictors of whether a bank is a hedger of loans or
not. In regression (2) the coefficient for Residential Mortgages is 0.85 and has a t-statistic
of 6.62, representing an increase in log odds of .85 for every $1B of increase in mortgage
assets held on the balance sheet. .85 log odds is a 134% increase in odds of a bank being in
a user of derivative hedging. The Tier 1 Ratio is also significant shows a coefficient of -.10,
representing a decrease in log odds of -.10 for every 1% increase in a bank’s Tier 1 Ratio.
-.10 log odds is a 9.6% decrease in odds of a bank being a derivative user.
In regression (4) results are similar for C&I loans where the coefficient of .69 is significant
with a t-statistic of 5.11, representing an increase of .69 log odds for every $1B increase in
C&I loans. This translates to a 99% increase in odds of a bank being a user of derivative
hedging for every $1B increase in commerical and industrial loans. The Tier 1 Ratio also
shows negative significance with a coefficient of .09, representing a decrease in log odds of
-.09 for every 1% increase in a bank’s Tier 1 Ratio. -.09 log odds is a 8.7% decrease in odds of
a bank being a derivative user. The results of Table 3 demonstrates the significance of loans,
especially residential mortgages, as a proxy for interest rate risk. For the next empirical
model, I will use residential mortgages as a proxy for interest rate risk in understanding the
8
costs of hedging in the context of regulatory uncertainty.
4.2. Firm Fixed Effects Panel Data Regression with Three Way Interactions
This section will deal with the main empirical model which tests for the costs of regulatory
uncertainty for banks that have varying degrees of regulatory burden. There are two main
hypothesis being tested. The first is that the costs of uncertainty is higher for banks with
fewer resources and higher compliance costs. The second hypothesis is that a bank under
constraint of uncertainty will prioritize resources to core operations. The results of the main
empirical model is shown in Table 5. The specifications of the empirical model with White
standard errors is as follows:
Interest Rate DerivativesitAssetsit
= αit +Compliance Costit
Assetsit∗ Regulatory Certainty∗[Loans Held to Maturityit
Assetsit+
Loans Held for SaleitAssetsit
+Loans SolditAssetsit
+ Swap Rate]
+ εit
The ”Hedging” dependent variable represents interest rates derivatives not used for trading
scaled by total assets. ”Trading” likewise represents interest rates derivatives used for trading
scaled by assets. ”Total” represents the combined amount of Hedging and Trading interest
rates derivatives scaled by assets. The trading dependent variable should not show significant
relationships with the covariates and interactions for hedging risk because by definition in
the Call Reports, trading interest rate derivatives are for speculation and trading. Loans
Held to Maturity represent the safest loans on a bank’s balance sheet while Loans Held for
Sale and Loans Sold have greater interest rate risk and require hedging. Swap rates are 10
year interest rate swaps. When swap rates fall, corporate clients demand interest rate swaps
in order to replace their floating interest rate payments with low fixed interest rates, hedging
their interest rate liabilities. Compliance burden is calculated using a unique dataset from
Dahl et al. (2016a) and is high for firms with high compliance costs. From Table 4 that
compliance burden has a highly negative relationship with bank size. Regulatory certainty
is a dummy variable with a value of 1 and represents clarity on Dodd Frank’s risk weightings
for derivatives, which were finally implemented in 2015 Q1.
Hedging various classes of mortgages on the balance sheet represent stabilization of core lend-
ing operations of a commercial bank. Selling fixed interest rate swaps to corporate clients
9
represent a business line that hedges corporate client’s interest rate risk instead of a bank’s
own interest rate risk. If the cost of uncertainty is higher for banks with higher compliance
burden then there should be a positive interaction between Regulatory certainty and Com-
pliance Burden. Banks with high compliance burden will see a disproportionately larger
increase in use of hedging after risk weightings of interest rate derivatives are finalized for
reporting in 2015 Q1. Table 5 confirms this hypothesis as the two way interaction coefficient
between Regulatory Certainty and Compliance Burden is positive and economically and sta-
tistically significant with a value of 17.8 and a t-statistic of 2.53. Table 5 also shows that
even in times of uncertainty, banks regardless of compliance cost will hedge their portfolio
for residential mortgages held for sale, as the coefficient for Loans Held for Sale (LHS) is .758
and has a t-statistic of 3.04. The lack of significance for the three way interactions between
Certainty * Compliance * LHS/LS shows that banks prioritize resources to hedging their
portfolio regardless of whether there is regulatory uncertainty or not. The results shown in
the ”Hedging” panel data regression generally holds for the ”Total” panel data regression,
showing that banks derivative use revolve around hedging activity and not trading.
Conclusion
The costs of uncertainty has been highlighted as aggregate declines in investment, output,
and employment Baker et al. (2016). This paper contributes to the findings of Gissler
et al. (2016) and Bordo et al. (2016) regarding the costs of regulatory uncertainty on bank
hedging practices and the disproportionate impact on smaller firms with higher compliance
costs, fewer resources, and limited ability to raise capital in the equity markets. Evidence
suggests that the tradeoff for firms dealing with uncertainty is to divert resources to core
operations and to pursue fewer growth opportunities until the costs of uncertainty have
subsided. Banks as repositories of interest rate risk Gorton and Rosen (1995) and have
acute needs for risk management practices to hedge loan portfolio risks, which comprise core
operations. Banks with higher compliance costs disproportionately decreased hedging when
specifics about Dodd Frank’s regulations were not known, and disproportionately increased
its use after risk weightings were known and implemented. Greater detail in banking data,
especially in core operations and off balance sheet items, as well as an unusually long period
of uncertainty, allows for further research into the cross sectional disparities of the cost of
uncertainty.
The results of this paper suggest that Dodd Frank’s long period of uncertainty in assigning
10
risk weightings to hedging products have disproportionately harmed banks that were largely
not responsible for the global financial crisis and were well capitalized throughout the reces-
sion. From a policy standpoint, these findings support the benefits of tailored policies that
reduce the costs of uncertainty for ”good” banks, while also addressing systematic issues
arising from ”TBTF” banks.
11
Appendix
Table 1: Summary Statistics for All Banks During UncertaintyData is from Call Reports (2010 Q4 - 2014 Q4). Ratios are scaled by total assets and are rep-resented as percentages. Regulatory uncertainty centers around the risk weighting of assets forbank assets and liabilities which were unknown before Dodd Frank was fully implemented in 2015 Q1.
Statistic N Mean Median Pctl(25) Pctl(75) St. Dev.
Total Assets ($M) 10,045 2,766.76 1,048.85 496.75 2,413.26 6,887.99Interest Rates Derivative Ratio 10,045 9.03 2.68 0.88 8.79 18.60Hedging Rates Ratio 10,045 7.62 1.90 0.51 6.64 17.27Trading Rates Ratio 10,045 1.42 0.00 0.00 0.00 6.94Fixed Swaps Ratio 10,045 0.73 0.00 0.00 0.00 2.69Residential Inventory Ratio 10,045 15.93 13.05 8.39 19.84 11.23Residential Held Ratio 10,045 2.55 0.45 0.14 1.54 6.34Residential Sold Ratio 10,045 10.47 2.60 1.03 7.38 25.72Compliance Cost Ratio 10,045 0.10 0.07 0.04 0.11 0.14Tier 1 Capital ($M) 10,045 257,660.90 97,258 45,624 226,598 615,228.50Risk Weighted Assets ($M) 10,045 1,890,294.00 715,925 351,735 1,677,713 4,914,440.00Tier 1 Ratio 10,045 0.14 0.13 0.12 0.15 0.05
Table 2: Summary Statistics for All Banks During certaintyData is from Call Reports (2015 Q1 - 2017 Q3). Ratios are scaled by total assets and arerepresented as percentages. Regulatory certainty is after the risk weighting of assets forbank assets and liabilities were known when Dodd Frank was fully implemented in 2015 Q1.
Statistic N Mean Median Pctl(25) Pctl(75) St. Dev.
Total Assets ($M) 6,885 4,873.42 1,471.60 725.92 3,872.48 13,037.24Interest Rates Derivative Ratio 6,885 10.40 3.59 1.05 11.08 20.02Hedging Rates Ratio 6,885 8.33 2.36 0.67 7.88 18.11Trading Rates Ratio 6,885 2.08 0.00 0.00 0.00 8.94Fixed Swaps Ratio 6,885 1.09 0.00 0.00 0.37 2.88Residential Inventory Ratio 6,885 17.16 13.93 9.10 21.16 12.39Residential Held Ratio 6,885 2.30 0.31 0.10 1.20 6.48Residential Sold Ratio 6,885 8.63 1.69 0.67 5.28 23.56Compliance Cost Ratio 6,885 0.09 0.06 0.03 0.09 0.24Tier 1 Capital ($M) 6,885 457,279.50 143,141 70,929 375,931 1,178,064.00Risk Weighted Assets ($M) 6,885 3,688,787.00 1,100,667 534,141 2,968,841 10,232,543.00Tier 1 Ratio 6,885 0.14 0.13 0.11 0.14 0.04
12
Table 3: Logistic Regression: Using Loans as a Predictor of Bank Hedging UsageUsing a cross section of Call Report data for 2017 Q3, logistic regression results find that residentialmortgages held for investment is a significant indicator of whether or not a bank hedges their portfoliousing interest rate derivatives. Interest rate derivatives are reported on Schedule RC-L of the Call Re-ports as Derivatives and Off Balance Sheet Items. Residential mortgages and Commercial and IndustrialLoans are reported on Schedule RC of the Call Reports as Balance Sheet Items. Residential mortgagesare defined as loans and leases that a reporting bank has the intent and ability to hold both for the fore-seeable future or until maturity or payoff and for the immediate future before sale, representing interestrate risk on a bank’s balance sheet. This table also shows that Commercial and Industrial loans are alsoa significant indicator in a bank being a user of interest rate derivatives. Tier 1 Ratio is reported on theSchedule RC-R of the Call Reports a Regulatory Capital item. These results control for a bank’s Tier 1Ratio defined as Tier 1 Capital
Risk Weighted Assetswhich is a proxy for a bank’s financial health. Residential mortgages
and C&I loans represent an interest rate risk on a bank’s balance sheet that require hedging. Resultsare similar for bank observations in other quarters.
Dependent variable:
Bank as User of Interest Rate Hedging
(1) (2) (3) (4)
Residential Mortgages 0.96∗∗∗ 0.85∗∗∗
(7.23) (6.62)
Tier 1 Ratio −0.10∗∗∗ −0.09∗∗∗
(−11.73) (−11.00)
CI Loans 1.00∗∗∗ 0.69∗∗∗
(6.25) (5.11)
Constant −0.16∗∗∗ 1.50∗∗∗ −0.10∗∗ 1.44∗∗∗
(−3.52) (10.68) (−2.30) (10.41)
Observations 2,681 2,681 2,681 2,681Log Likelihood -1,786.31 -1,659.04 -1,795.43 -1,681.02Akaike Inf. Crit. 3,576.63 3,324.08 3,594.86 3,368.03
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
13
Figure 1. Use of Hedging Across Various Banks by Size and TypeScatterplot chart of the log of total assets on the x-axis with the log of
(Interest Rate Derivatives
Residential Mortgages
)on the y-axis
for 16,930 bank quarter observations from 2010 Q4-2017 Q3. Scaling was used for ease of exposition.
14
Table 4: Pooled OLS on Compliance CostCompliance data is obtained from a unique dataset in Dahl et al. (2016a). The compliance cost ratio isregressed against log scaled assets, log scaled total residential mortgages, log scaled interest rate deriva-tives, and Tier 1 Ratio. Results show that assets size is highly negatively significant with compliancecost ratio. The results of this pooled OLS show that firms with higher compliance costs are smallerbanks, even when controlling for balance sheet loans, off balance sheet derivatives, and financial health.
Dependent variable:
Compliance Costs/Assets
(1) (2) (3) (4)
log(Total Assets) −4.79∗∗∗ −7.89∗∗∗ −7.52∗∗∗ −8.84∗∗∗
(−46.35) (−39.14) (−37.00) (−43.29)
log(Residential Mortgages) 3.49∗∗∗ 3.17∗∗∗ 2.27∗∗∗
(17.86) (16.14) (11.66)
Tier 1 Ratio 35.50∗∗∗ 40.48∗∗∗
(11.55) (13.45)
log(Interest Rate Derivatives) 2.13∗∗∗
(28.07)
Observations 16,863 16,863 16,863 16,863R2 0.11 0.13 0.14 0.17Adjusted R2 0.11 0.13 0.14 0.17Residual Std. Error 17.24 17.08 17.01 16.63F Statistic 2,148.33∗∗∗ 1,253.92∗∗∗ 886.99∗∗∗ 893.30∗∗∗
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
15
Table 5: Firm Fixed Effects Panel Data Regression with Three Way InteractionsUsing Panel Data from Call Reports (2010 Q4-2017 Q3) and compliance costs from the Conference of State Bank Supervisors I use the followingempirical model:
Interest Rate Derivativesit
Assetsit= αit +
Compliance CostitAssetsit
∗ Regulatory Certainty∗[Loans Held to MaturityitAssetsit
+Loans Held for Saleit
Assetsit
+Loans Soldit
Assetsit+ Swap Rate
]+ εit
Loans Held to Maturity represent the safest loans on a bank’s balance sheet while Loans Held for Sale and Loans Sold have greater interestrate risk and require hedging. Swap rates are 10 year interest rate swaps. When swap rates are low, demand for swaps goes up as corporateclients hedge their floating interest rate liabilities with fixed rate swaps. Compliance burden is a continuous variable and is high for firms withhigh compliance costs. Regulatory certainty is a dummy variable with a value of 1 when Dodd Frank’s risk weightings for derivatives wereimplemented in 2015 Q1. The three way interaction of Certainty * Compliance * Swap Rate shows that banks with high compliance costsbenefit from regulatory certainty and generate more hedging business with corporate clients when interest rates fall. The two way interactionbetween Certainty * Compliance shows us that banks with high compliance costs hedge more during times of regulatory certainty. Derivativesused for trading by definition are not used for hedging purposes. Hedging + Trading = Total Interest Rate Derivatives. T-statistics are inparenthesis and are estimated using White standard errors.
Interest Rate Derivatives/Total Assets:Total Hedging Trading
Compliance Burden 52.145*** 54.535*** -2.39(4.24) (4.45) (-0.84)
Regulatory Certainty * Compliance 22.119*** 17.871** 4.248(3.51) (2.53) (0.92)
Loans Held to Maturity (LHM) -0.066 -0.077 0.011(-0.76) (-0.75) (0.22)
Loans Held for Sale (LHS) 0.907*** 0.758** 0.15(3.91) (3.04) (1.68)
Loans Sold (LS) 0.192** 0.19** 0.002(3.08) (2.92) (0.12)
Swap Rate -0.007* -0.004 -0.003*(-2.3) (-1.43) (-2.47)
Compliance * LHM -49.73 -51.219 1.489(-1.94) (-1.83) (0.12)
Certainty * Compliance * LHM -11.803 2.504 -14.307(-0.51) (0.08) (-0.85)
Compliance * LHS -70.922 -74.353 3.43(-1.38) (-1.5) (0.27)
Certainty * Compliance * LHS 61.548 69.768 -8.22(1.19) (1.39) (-1.22)
Compliance * LS 4.212 4.838 -0.625(0.66) (0.7) (-0.23)
Certainty * Compliance * LS -5.777 -6.434 0.657(-1.29) (-1.36) (0.61)
Compliance * Swap Rate -17.575*** -18.336*** 0.761(-4.78) (-4.9) (0.73)
Certainty * Compliance * Swap Rate -9.068** -8.135** -0.933(-2.74) (-2.6) (-0.98)
R2 0.47 0.49 0.01F-Statistic 21.55 15.75 1.66Observations 16,930 16,930 16,930Groups (Bank Fixed Effects) 1,040 1,040 1,040Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
16
Figure 2. Time Series Chart of Ratio of all Banks (Non-TBTF) from 2010Q4-2017Q3Time Series chart of 1
n
∑ni=1
Residential MortgagesitTotal Assetsit
, 1n
∑ni=1
Interest Rate DerivativesitTotal Assetsit
, 1n
∑ni=1
Interest Rate DerivativesitResidential Mortgagesit
.
The large spike in 2014 Q3 in 1n
∑ni=1
Interest Rate DerivativesitTotal Assetsit
occured after first draft of the final riskweightings on interest rate derivatives was released in June 27, 2014 by the FDIC, Federal Reserve,and Office of the Comptroller of the Currency. Comments were accepted until August 2014, and afterthousands of responses by FDIC financial institutions, a final draft was subsequently delayed until 2015Q1.
17
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