the transmission of negative interest rates · 2 1. introduction negative nominal interest rates...
TRANSCRIPT
THE TRANSMISSION OF NEGATIVE INTEREST RATES:
EVIDENCE FROM SWISS BANKS*
CHRISTOPH BASTEN§ AND MIKE MARIATHASAN
†
DECEMBER 2018
Studying monthly supervisory data and the differential exposure of
Swiss banks to a foreign-induced negative interest rate policy, we
identify the transmission of negative rates as special in the following
ways: First, rates stop being transmitted to depositors, which also
breaks the transmission to mortgage rates. Second, banks only divert
some of their liquid assets into riskier and longer-maturity assets, and
otherwise retire longer-term liabilities. This implies an expansionary
effect on mortgages, but a contraction – and more deposit-dependence
– of the balance sheet. Banks ultimately remain profitable, also through
fees, but become more exposed to credit, market, and interest rate risk.
JEL Codes: E43, E44, E52, E58, G20, G21
Keywords: negative interest rate policy, monetary policy transmission,
deposit rates, bank profitability, credit risk, interest rate risk
* We would like to thank Andreas Barth (discussant), Christoph Bertsch, Jef Boeckx, Frederic Boissay, Diana Bonfim, Martin Brown, Raymond Chaudron (discussant), Jean-Pierre Danthine, Hans Degryse, Olivier De Jonghe, Narly Dwarkasing, Robin Döttling, Jens
Eisenschmidt (discussant), Mara Faccio, Leonardo Gambacorta, Hans Gersbach, Denis Gorea, Christian Gourieroux, Iftekhar Hasan
(discussant), Florian Heider, Johan Hombert, Robert Horat, Matthias Jüttner, Catherine Koch (discussant), Frederic Malherbe, Klaas Mulier, Philip Molyneux, Emanuel Mönch, Friederike Niepmann, Steven Ongena, Jonas Rohrer, Kasper Roszbach, Farzad Saidi, Glenn Schepens,
Eva Schliephake, Bernd Schwaab (discussant), Piet Sercu, Enrico Sette, Joao Sousa, Johannes Ströbel, Ariane Szafarz, Dominik Thaler,
Lena Tonzer, Benoit d’Udekem, Greg Udell, Xin Zhang, as well as seminar/conference participants at ACPR-Banque de France, FINMA, Danmarks Nationalbank, National Bank of Belgium, Norges Bank, SNB, Sveriges Riksbank, Université Libre de Bruxelles, Université de
Neuchâtel, Université Paris-Nanterre/EconomiX, the Annual CEBRA Meeting 2017, the CESifo Area Conference on Macro, Money &
International Finance, the 14th Christmas Meeting of German Economists Abroad, the 16th CREDIT Conference, the ECB Workshop on Monetary Policy in Non-Standard Times, EFA 2017, the 3rd EUI Alumni Conference, the CEPR-ESSFM 2018, the 18th FDIC/JFSR Annual
Fall Research Conference, the FINEST Winter Workshop, the 6th Research Workshop in Financial Economics (University of Bonn), the
SSES Annual Congress 2018, and the 10th Swiss Winter Conference on Financial Intermediation for their valuable comments. All remaining errors are our own. Work using supervisory data was completed while C. Basten worked for the Swiss Financial Market Supervisory
Authority (FINMA). The authors are grateful for this opportunity and for the thoughtful comments from Michael Schoch and Christian
Capuano. Any views expressed in this paper remain the sole responsibility of the authors and need not reflect the official views of FINMA.
§ University of Zürich, Department of Banking & Finance, Email: [email protected]; † KU Leuven, Department of Accounting,
Finance & Insurance; Email: [email protected]
Work in progress. Please cite or circulate only with the authors’ permission.
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1. Introduction
Negative nominal interest rates have long been considered impossible.1 Research has therefore
focused on understanding monetary policy transmission at or above the zero lower bound
(ZLB), while paying less attention to the dynamics when rates go negative. Following Denmark
in 2012, however, central banks in the Euro Area, Japan, Sweden, and Switzerland have
recently adopted negative rates as instruments of unconventional monetary policy.2 This has
made it necessary and empirically possible to also investigate the transmission channels below
zero. Using detailed supervisory data from Switzerland, we contribute to this investigation by
providing novel evidence on banks’ response to negative interest rate policies (NIRP).
We study a largely unanticipated decision by the Swiss National Bank (SNB) to cut its deposit
facility rate from zero to -75 basis points (bps) – the lowest in all currency areas – and to exempt
bank-specific fractions of reserves. Specifically, and presumably to target the marginal rather
than the total cost of reserves, the SNB continued to charge no interest on central bank deposits
below twenty times each bank’s minimum reserve requirement (MRR).3 This policy design
provides us with a measure of adverse NIRP-exposure for each bank (total minus exempted
central bank deposits prior to implementation in January 2015) and enables us to identify the
effects of the rate cut by comparing the behaviour of differentially affected banks over time.4
We identify three differences with the transmission under positive rates. First, a reluctance to
transmit negative rates to depositors, which is compensated for with fees and an incomplete
downward transmission to lending rates. Second, despite an expansionary effect on mortgages
1 Paul Krugman, for instance, wrote as late as 2013 that “the zero lower bound isn’t a theory, it’s a fact”
(https://krugman.blogs.nytimes.com/2013/10/15/five-on-the-floor/; accessed: September 14, 2017). 2 Eggertson et al. (2017) go as far as calling negative interest rate policies (NIRP) a “radical new policy experiment”. 3 Aggregate reserves at the time were equal to 24 times the sum of all banks’ minimum reserve requirements (MRR), so that marginal reserves
were affected independent of how total reserves were distributed across banks. 4 For the identification of adverse NIRP exposure, it does not matter whether banks initially held reserves above or below their exemption. Higher reserves above the exemption have a direct cost of -75 bps, while higher reserves below the threshold have an opportunity cost of
similar size (banks essentially loose a profitable arbitrage opportunity by which they borrow on the interbank market, at negative rates, and
lend to the central bank for free). We discuss the assumptions underlying our identification in more detail in Section 5.
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and a rebalancing of the portfolio towards riskier assets, a contractionary effect on the balance
sheet as a whole. And third, an increase in maturity mismatch and therefore interest rate risk.5
Since banks’ willingness to retain their depositors constrains deposit rates at zero, the rate cut
does not reduce banks’ funding costs as much as under positive rates. This creates pressure on
profit margins and explains the weaker transmission to lending rates and the higher fee income.
The contractionary effect on the balance sheet, instead, occurs first, because the rapid
transmission of the reserve rate quickly turns other liquid assets into unattractive substitutes
and second, because allocating reserves to other asset classes is particularly difficult for banks
that are more exposed to the NIRP. Their breaking of the transmission to loan rates means that
they either attract riskier or fewer borrowers, as higher rates either reflect a higher risk-
premium or reduced demand, or a combination of both. A reallocation to other assets therefore
becomes unattractive earlier for these banks. Since continuing to lend to the central bank or the
interbank market is – different from positive rate environments – a loss-making strategy as
well, they therefore prefer to cut outstanding liabilities. Since the ZLB on deposit rates further
implies that banks cannot easily adjust their deposit intake, the contraction of the balance sheet
is achieved by not rolling over or repurchasing outstanding long-term liabilities. The result is
a shorter average maturity of the remaining liabilities and – together with the reallocation from
liquid assets to mortgages – a stronger maturity mismatch.
Through these adjustments, adverse NIRP exposure does not ultimately harm banks’
profitability. It does, however, impair financial stability: Portfolio rebalancing leads to
additional credit and market risk, and thus to a reduction in risk-weighted capital. The stronger
maturity mismatch, instead, implies more exposure to (traditional measures of) interest rate
risk and a reduced liquidity coverage. Lowering policy rates below zero, in other words, does
5 Drechsler et al. (2018) have recently argued that maturity mismatch in banks does not need to imply interest rate risk if the rate-invariant
cost of maintaining a deposit franchise is taken into consideration. We are sympathetic to this view and believe that the notion of a deposit
franchise is helpful for the interpretation of our results. Here, however, we refer to “interest rate risk” as it is traditionally measured.
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not only lead to an overall contraction of banks’ balance sheets, it also requires a careful
balancing of any (perceived) benefits with potentially conflicting financial stability objectives.
In understanding how our results differ from the transmission of rate cuts above zero, we find
banks’ reluctance to introduce negative deposit rates to play an important role. We do not
empirically investigate the origins of this reluctance, but our preferred explanation is banks’
concern that deposit funding becomes particularly elastic around zero.6 To protect their deposit
franchise (Drechsler et al., 2018) and the relationship with valuable clients, banks seem willing
to maintain non-negative deposit rates – even if this implies higher funding costs and a greater
maturity mismatch.
2. Related Literature
With the ZLB on deposit rates as an additional constraint in mind, we can relate our findings
to the existing literature on monetary policy transmission through banks. Traditionally, this
literature has focused on the bank-lending channel (e.g., Bernanke & Gertler, 1995; Kashyap
& Stein, 2000; Jimenez et al., 2012), which predicts a contractionary effect on credit supply
from monetary tightening; at least in circumstances in which banks are unable to raise
additional deposits, or alternative external financing. We find that the rate cut into negative
territory has an expansionary effect on mortgage lending by more NIRP-exposed banks, but a
contractionary effect on the size of the balance sheet. More recently, the focus has instead been
on lending standards and the riskiness of banks’ credit supply. Evidence on the “risk-taking”
channel is abundant and generally suggests that a rate cut induces banks to lend more to ex-
ante riskier borrowers (e.g., Ioannidou et al., 2014; Jimenez et al., 2014).7 Bonfim & Soares
(2018) provide an updated summary of this literature and distinguish between risk-taking that
6 Recent commentary (Cecchetti & Schoenholtz, 2016; Danthine, 2016) also suggests that deposits rates are constrained by the return on cash. 7 Dell’Ariccia et al. (2016) analyse a rate hike and find a symmetric effect: ex-ante risk-taking by banks decreases as short-term rates increase.
5
is motivated by a search for yield and incentives for risk-shifting.8 This distinction provides
helpful guidance. When deposit rates are downward flexible – e.g., because banks have
invested in their deposit franchise and face a relatively inelastic supply of deposit funding
(Drechsler et al., 2018) – a reduction of the policy rate is likely to affect banks’ funding costs
faster than the return on longer-term assets. This increases the value of banks’ franchise in the
short-term and generates incentives to protect it by taking fewer risks (Dell’Ariccia & Marquez,
2013; Dell’Ariccia et al., 2014). When the cost of deposit funding is constrained by the ZLB,
instead, this franchise value effect is mitigated, and we expect stronger risk-taking incentives.
At the same time, a rate cut also reduces the return to safe and liquid short-term assets, creating
incentives for risk-taking in pursuit of higher returns. In principle, these incentives exist both
under positive and under negative rates, although banks might find it harder – under negative
rates – to expand their lending (since the transmission to loan rates is incomplete). In summary,
this suggests that the additional constraint on deposit rates should increase risk-taking
incentives, while limiting the feasibility and scale of a credit expansion. This matches our
evidence that banks become riskier in response to adverse NIRP-exposure and they do not
reallocate all of their liquidity to the mortgage market.
The special role of non-negative deposit rates is also a recurrent element in the emerging
literature on the transmission of negative policy rates. Heider et al. (forthcoming), in fact,
exploit it for identification and compare changes in the lending behaviour of banks with
different deposit ratios. They study the effect of the European Central Bank’s NIRP specifically
on the syndicated loan market and find that banks with higher pre-NIRP deposit ratios lend
more to riskier borrowers after the policy rate was gradually lowered from zero to -20bps. Our
findings are consistent with their observation that the credit expansion is directed towards
8 They also discuss incentives for risk-taking during prolonged periods with low interest rates (e.g., Gambacorta, 2009; Acharya & Naqvi,
2012), which are different from our analysis of banks’ response to a rate cut.
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riskier borrowers.9 In addition, however, we also show that banks’ balance sheets become
riskier overall, and that market, liquidity and interest rate risk increase along with credit risk.
Eggertson et al. (2017) add a ZLB on deposit rates to their macro-model and also use deposit
dependence to study the Swedish case, using bank-level information.10 Like us, they observe
an impaired transmission to banks’ loan rates and explain it with banks’ incentives to keep
profit margins stable. Bottero et al. (2018), instead, argue that the retail deposit channel is
inactive in their sample, because compressed deposit margins are offset with higher fees.11
They observe an expansionary effect on the supply of credit to Italian firms, and attribute this
to a portfolio rebalancing channel, i.e. a search for yield by banks that are confronted with
negative interest rates on their central bank reserves. The offsetting effect on fees is also present
in our analysis and we too identify a strong rebalancing of banks’ portfolios towards riskier
assets. In addition, however, we also show that the ZLB on deposit rates is not only relevant
because it squeezes banks’ margins, but also because it forces banks that are unable to
reallocate all of their reserves to alternative assets to deleverage, and – specifically – to reduce
their longer-term liabilities.12 This causes the maturity mismatch to increase and exposes banks
to additional interest rate risk. It also interferes with banks’ liquidity coverage ratio and implies
that the expansionary effect on loans (mortgages in our case), is accompanied by an effect on
the overall balance sheet that is actually contractionary.13 Finally, we also show that banks
profits are not only preserved with fees, but also with an interrupted transmission to loan rates.
9 We conduct our analysis at the bank-level and do not observe borrower characteristics directly. We do, however, observe an relatively
stronger growth in mortgages and a more impaired downward transmission to mortgage rates among banks that are more exposed to the NIRP,
and after controlling for demand through Google search volumes (see Sections 4 and 5 for more detail). To the extent that relatively higher rates either reflect a higher risk-premium or imply higher default probabilities for borrowers with otherwise identical characteristics, this is
consistent with the expansion being directed towards riskier borrowers. 10 Urbschat (2018) also uses banks’ deposit-dependence for identification to study the response by German banks. 11 Comparing evidence on bank profitability from 27 countries, Lopez et al. (2018) also conclude that “high deposit banks do not seem
disproportionately vulnerable to negative rates”. 12 This effect relates to Demiralp et al. (2017), who find that negative rates seem to induce banks to reduce the intake of wholesale funding. 13 While a contractionary effect of low (not necessarily negative) interest rates has been predicted by Brunnermeier & Koby (2017), their
model could not explain the expansion in mortgage lending that we also observe. Since their mechanism relies on banks’ being capital
constrained, it is likely not applicable in our sample of very well capitalized banks.
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3. The Swiss Context
3.1. The Negative Interest Rate Policy (NIRP)
Prior to January 2015, monetary policy in Switzerland was conducted primarily via open
market operations. The SNB defined upper and lower bounds for the target interbank rate and
injected or extracted liquidity from the market to navigate the 3-month CHF LIBOR within
these bounds. It paid no interest on central bank reserves. On December 12, 2008, the lower
target bound was reduced to zero, while the upper bound was subsequently lowered from 100
bps to 75 bps on March 12, 2009, and to 25 bps on August 03, 2011. On December 18, 2014
the SNB then moved the lower bound to -75 bps and announced a return of -25 bps on banks’
sight deposit account balances for January 22, 2015. On January 15, 2015, this rate
announcement was then corrected to -75 bps and the target bounds for the LIBOR rate were
lowered to -125 bps and -25 bps respectively. Presumably to ensure interbank transmission
while limiting the strain on the system at large, the SNB further chose to exempt all central
bank reserves below “20 times the minimum reserve requirement for the reporting period 20
October 2014 to 19 November 2014 (static component), minus any increase/plus any decrease
in the amount of cash held (dynamic component)“.14 Importantly, this ultimately bank-specific
exemption was designed to manage aggregate liquidity and not to target specific banks.
The Swiss NIRP also differs, for example from the Euro Zone, as it was primarily motivated
by the exchange rate. Since 2011, the SNB had continuously acquired assets in foreign currency
to moderate pressure on the Swiss Franc, and to defend an exchange rate of 1.2 CHF vis-à-vis
the Euro. Despite having communicated a renewed commitment to this exchange rate on
December 18, the SNB then decided to unpeg the Franc on January 15.15 As a consequence,
14 http://www.snb.ch/en/mmr/reference/pre_20141218/source/pre_20141218.en.pdf 15 Some commentators have attributed this decision to concerns that a further expansion of the SNB's holdings of foreign-currency assets
could at some point cause huge losses and thereby erode its equity and credibility. Others, instead, have posited that even negative equity need
not be an issue for a central bank.
8
the introduction of the NIRP was accompanied by an appreciation of the Swiss currency from
1.20 CHF/EUR in December 2014 to 1.04 CHF/EUR in April 2015 (Figure 1). For an export-
dependent economy, this appreciation was an adverse shock, and exports fell by 3.9% between
2014 Q4 and 2015 Q1. Possibly aided by schemes to subsidize a temporary reduction of
working hours and the international price-setting power of many Swiss exporters, however,
they quickly recovered and economic growth remained largely unaffected.
That monetary policy was largely exogenous to domestic credit growth in Switzerland, that the
SNB’s decision to charge negative interest on reserves had no precedent in Swiss monetary
policy, and that the NIRP was implemented with relatively short notice between December
2014 and January 2015 supports our identification. The simultaneous unpegging of the
exchange rate, instead, constitutes a challenge that we address in more detail in Sections 4 and
5. In short, we first remark that economic growth – as a proxy for credit demand – did not react
strongly, as a large share of Swiss exports are highly specialized products with relatively
inelastic demand. Second, and more importantly, we focus our analysis on domestically owned
commercial banks, with insignificant foreign-currency assets or liabilities, and irrelevant
foreign-currency income or costs.16 Third, although our measure of banks’ NIRP-exposure is
unlikely to be correlated with exposure to the exchange rate, we also explicitly control for
proximity to the border, as a proxy for a possible exchange rate dependence of banks’ clients.
3.2. The Transmission to Interest Rates and Margins
Before discussing our identification further, we complete our description of the Swiss case by
documenting the evolution of key interest rates from July 2013 to June 2016: Figure 2
illustrates the evolution of the Swiss monetary policy target between July 2013 and June 2016,
and the corresponding interest rates for overnight (SARON), 3-month and 12-month interbank
16 This is different for Switzerland-specific wealth management banks, which we therefore analyze separately.
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(LIBOR) loans, as well as federal government bonds with one-year maturity. All short-term
rates drop to a level around -75 bps immediately in January 2015. The 3-month LIBOR rate
and the overnight lending rate stay close to the target, while the return on one-year government
bonds is more volatile and initially even below the target. Consistent with a standard yield
curve, the return on 12-month interbank loans, instead, is on average higher than the target rate.
The main take-away, for our purposes, is the immediate transmission of the negative reserve
rate to assets that may serve as close substitutes.17 The return on longer-term assets, instead,
exhibits a weaker reaction to the introduction of the negative policy rate (Figure 3).
Government bonds, covered bonds, cantonal bonds, and bank bonds with an 8-year maturity
continue an almost uninterrupted downward trend that approaches -75 bps only around June
2016.18 In view of the effect on banks’ balance sheets, these trends suggest that safer financial
assets with longer maturities became relatively more attractive after the policy change. The
imperfect pass through to the return on bank bonds, however, which remains positive until June
2016, also suggests an effect on banks’ funding costs. While we analyse these effects – at the
bank-level – in our regression analysis, Figures 4 through 7 already illustrate the implications
for banks’ profit margins. On the liability side of the balance sheet, the ZLB on deposit rates
is evidently at work. The sight deposit rate (Figure 4) approaches 1 bp after the policy change,
while banks are only able to earn a return close to -75 bps on reserves or close substitutes (e.g.,
SARON or 3-month LIBOR). The sight deposit margin consequently drops from -3 bps to -75
bps between December 2014 February 2015.19 This policy-induced drop in interest margins
would not occur in positive rate environments in which banks can reduce the return on deposits.
17 Recall, that banks could not hold cash instead of reserves, as any changes in their cash balances were also charged negative interest rates,
under the dynamic component of the Swiss NIRP. 18 A notable exception is the return on non-financial corporation (NFC) bonds with the same 8-year maturity, which does not drop further after
January 2015 and subsequently approaches 1% from below. 19 During the same period, the margin for demand deposits drops from -17 bps to -99 bps.
10
Even in the aggregate, however, we observe that banks do not bear the full cost of decreasing
deposit margins; instead, they also disrupt the transmission to loan rates. Figure 5 depicts the
margin between the average adjustable rate mortgage (with rates resetting based on the 3-
months CHF LIBOR every 3 months, and a contract duration of 3 years) and the 3-month CHF
LIBOR rate itself. While the LIBOR rate dropped to -75 bps after January 2015, banks kept
the rate received on adjustable rate mortgages largely unchanged, which implied an increase in
the corresponding margin from 118 bps in December 2014 to 203 bps in February 2015. Over
the same period, the margin on 10-year fixed-rate mortgages similarly jumped from 122 bps to
174 bps (Figure 6).20 Since the average bank in our sample invests about 70% of its balance
sheet in mortgages, this suggests an important compensation for squeezed liability margins.
In anticipation of our econometric identification, different characteristics of the Swiss case
matter. First, the quick succession of events and the lack of a precedent implied that banks
could not easily foresee (and adjust to) the implementation of the NIRP. Second, the ultimately
bank-specific exemption did not target individual banks. Third, the simultaneous unpegging of
the exchange rate challenges our identification of the effects of NIRP-exposure. Fourth, the
pass-through to the interbank market remains intact for negative interest rates. Fifth, banks’
deposit margins are squeezed, but there appears to be an offsetting effect on asset margins.
Below, we explain how we integrate these elements into our econometric analysis.
20 Consistent with evidence in Bech & Malkhozov (2016), Figure 6 also shows that the interest rate on 10-year mortgages itself initially
increased after January 2015.
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4. Data
Our panel comprises symmetric pre- and post-treatment periods and is constructed from
comprehensive monthly balance sheet information that FINMA and the SNB jointly collect for
supervisory purposes. It begins 18 months before the introduction of the NIRP in Switzerland
(July 2013) and ends 18 months thereafter (June 2016). The original sample comprises all
“[b]anks whose balance sheet total and fiduciary business combined exceed CHF 150 million
and whose balance sheet total amounts to at least CHF 100 million”.21 Of the 237 banks that
originally satisfy these criteria, we retain 50 domestically-owned commercial and 46 wealth
management (WM) banks and focus – in our interpretation – primarily on the commercial
banks.22 These banks account for about 40% of all banks’ Swiss assets and have negligible
fractions of their assets and liabilities denominated in foreign currencies.23 With deposit and
mortgage ratios of around 70% they also have a relatively transparent business model, which
allows us to identify the transmission channels more clearly and contributes to the external
validity of our findings. WM banks, instead, are more exposed to foreign currencies and
operate a business model that is fairly specific to the legal environment in Switzerland. As the
second largest group in our original sample we nonetheless retain them for key regressions, to
illustrate that our main insights are robust to a potential confounding from the exchange rate.
Our focus on commercial and WM banks notably eliminates the two large universal banks UBS
and Credit Suisse, as their Swiss commercial banks did not yet report separate financial
statements to the authorities at the time, as well as all trading banks, which do not hold
21 http://snb.ch/en/emi/MONAX 22 What we call “commercial banks” are banks that satisfy FINMA’s definition of “retail banks”, which requires them to generate at least 55%
of their income from balance-sheet effective activities (primarily net interest income and fees on loans) on average during the three years
preceding June 2013 (i.e., the last month before the start of our pre-treatment period; income shares, however, are stable over time so that the group composition would not change if we chose a different selection date). 68 banks from the original sample satisfy this definition and we
drop the 18 among them that are foreign-owned (because the relationships with their owners may expose them to the simultaneous exchange
rate shock; robustness tests show that results are qualitatively robust if we include the foreign-owned banks). FINMA uses this classification for internal peer group analysis. It is different from the SNB’s definition of “retail banks”, which takes into account banks’ ownership structure.
We believe that a classification that is purely based on banks’ business models is preferable for our purposes; especially, since the FINMA
classification also provides us with a larger sample size. We worked with the SNB’s definition in an earlier version of this paper and found our results to be qualitatively robust. Other banks – including WM banks – generate significantly larger shares of their income from advisory
fees and trading income. 23 The pooled sample averages are 2.73% (assets) and 4.38% (liabilities) respectively
12
meaningful amounts of deposits, loans or mortgages.24 Finally, we also have to drop
cooperative banks, as they hold reserves at shared clearing banks and not individually at the
SNB, and – for consistency – eliminate all banks that are not present during the 36 months of
our baseline period and the 36 months of an earlier (positive rate) period for which we report
comparative results in Table 9.25
Table 1 provides pooled summary statistics for the sample of commercial banks; Table 2
provides statistics for the pre- and post-treatment periods, separately for banks that experience
treatment intensity below (Panel A) and at or above (Panel B) the sample median.26 Summary
statistics are provided for different balance sheet items, income, measures of risk-taking, and
bank capitalization. Table 1 shows that the average bank in our sample invests 72.78% of total
assets in mortgages, 8.49% in uncollateralized loans, and 4.70% in financial assets. Liquid
assets amount to 8.34% and are dominated by central bank reserves (7.77% of total assets). On
the liability side, deposit funding constitutes the largest fraction (67.59%), followed by bond
funding (13.04%).27 The sample banks hold few assets in foreign currency (2.73%) and raise
95.62% (= 100% - 4.38%) of their funding in CHF. On average, they exceed their risk-weighted
capital requirement by 8.21% of risk-weighted assets and hold a weakly negative net position
on the interbank market (-0.86% of total assets). The share of required equity that is attributed
to credit risk amounts to 94% and is significantly higher than the share that can be attributed
to market (1%) or operational risk (6%).28 In short, we focus on simple commercial banks that
24 Nucera et al. (2017) suggest that traditional commercial banks, with incomes that are not as diversified as those of large universal banks,
are more affected by NIRPs. While this means that we are likely to observe stronger effects than in the full sample, it also means that we are able to observe the effects more clearly. 25 Our results are qualitatively robust to including foreign-owned banks. The earlier period lasts from February 2010 to January 2013 and is
centered around a previous rate cut; our results are robust to running the regressions in a sample that is not balanced across the two periods. 26 We do not report figures and summary statistics for groups above and below the exemption threshold, to maintain equal group size. As can
be seen in Figure 8, the group of banks with positive levels of exposed reserves is smaller, implying that subsample statistics are less likely to
provide reliable values. 27 Medium Term Notes, which we use to illustrate the effect on longer-term borrowing costs, account for about 3.7% of total assets. 28 We use those values that FINMA and SNB collect and report for regulatory purposes. Notice that required equity is calculated before
deductions, so that individual fractions (or the sum of different fractions) can exceed 100%.
13
deal primarily with local households and firms. They are well capitalized and their main
exposure stems from traditional services, such as credit provision and maturity transformation.
In Table 2, we consider the change in sample characteristics from the period before January
2015 to the period of and after that month. We observe that the average bank held more liquid
assets, as well as fewer claims on other banks and a lower share of cash in foreign currency. It
also generated less net interest and fee income, invested in safer portfolios and more strongly
exceeded its regulatory capital requirement. Because of the simultaneous exchange rate shock
and because banks were differently exposed to the NIRP, however, we cannot necessarily
attribute these changes to the effect of negative interest rates. To isolate the marginal effect of
adverse NIRP-exposure, we need to compare changes of banks with different degrees of
exposure over time. The difference between banks with exposed reserves below (Panel A) and
at or above (Panel B) the median provides first insights. We observe an increase in average
SNB reserves in both Panels, but a stronger change in Panel B (from 4.06% to 9.14%, compared
to a change from 8.30% to 9.59%); at the same time, the net position on the interbank market
changes from -0.35% to -2.75 in Panel A, and from 0.16% to -0.51% in Panel B. For banks
below the median, this reflects the existence of an arbitrage opportunity. Their “spare capacity”
allows these banks to deposit more interest-exempt reserves at the central bank while receiving
a negative rate on loans from other banks. When exposed reserves are above the median,
instead, the group consists of banks with both positive and negative levels of exposed reserves
so that effects might cancel out. It is therefore plausible that the lower panel exhibits a
qualitatively identical, but weaker change in ratios. The same pattern is present in other key
variables as well: below-median banks reduce the share of mortgages on their balance sheet
from 74.81% to 72.89%, for example, while above-median banks do not reduce it significantly.
14
To capture potentially differential changes in mortgage demand, we complement our
supervisory data with Google Trend data for the topic “Mortgage”.29 These data are available
at the cantonal (state) level and in 7-day intervals; the sample is normalized to 100 by the
highest search volume (which comes from the canton “Obwalden” in the 7 days preceding
December 2013).30 We match these search volumes to banks by using weighted sums across
cantons, with weights equal to banks’ allocation of mortgages across cantons.31 In this way, we
obtain a bank-specific measure that captures differential changes in mortgage demand across
cantons, as well as banks potentially differential exposure to these changes (depending, for
instance, on their pre-existing branch network, name recognition among potential customers,
or local expertise). Figure 7 plots search volumes for all of Switzerland over time. Consistent
with the interpretation as a proxy for demand, it exhibits a distinct increase in search volumes
after the rate cut in January 2015, i.e. after mortgages became cheaper for households.32
While summary statistics are indicative, they do not offer conclusive evidence. To gauge the
effects of adverse NIRP-exposure more carefully, we proceed with our regression analysis.
29 We use “Hypothek”, which is German for “Mortgage”. Topic searches automatically include relevant related search terms; these include – in particular – identical search terms in English or either of the country’s languages. We obtain our data from the Swiss Google Trends site. 30 To match the frequency of our balance sheet data, we sum up the weekly data to obtain monthly frequencies. 31 Note that we do not observe the issuance of new mortgages. We therefore calculate the weight based on the stock and cantonal distribution of mortgages on banks’ balance sheets (prior to the introduction of the NIRP). 32 In our Online Appendix we provide robustness checks that do not include Google Search volumes; as expected, it turns out that controlling
for mortgage demand is not essential for our results.
15
5. Empirical Strategy
5.1. Model
Our goal is to identify the effect of adverse NIRP-exposure on banks’ investment, lending and
funding choices, and to understand implications for income and risk-taking. To this end, we
rely on a difference-in-difference (DiD) design with a continuous rather than binary measure
of treatment intensity. The treatment period is characterised by a dummy variable (Postt) that
is equal to one from January 2015, and equal to zero before.33 Our baseline measure of
treatment intensity is equal to average SNB reserves prior to December 2014 minus the bank-
specific exemption, in percent of a bank's average total assets.34 We refer to this measure as
“Exposed Reserves” (ERi):
𝐸𝑅𝑖 =𝑆𝑁𝐵 𝑅𝑒𝑠𝑒𝑟𝑣𝑒𝑠̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅
𝑖 − 𝑆𝑁𝐵 𝐸𝑥𝑒𝑚𝑝𝑡𝑖𝑜𝑛𝑖
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅𝑖
Denoting a generic dependent variable in period t by Yi,t, our benchmark regression then takes
the following form:
𝑌𝑖,𝑡 = 𝛼 + 𝛽 ⋅ 𝐸𝑅𝑖 + 𝛾 ⋅ 𝑃𝑜𝑠𝑡𝑡 + 𝛿 ⋅ (𝐸𝑅𝑖 × 𝑃𝑜𝑠𝑡𝑡) + 휀𝑖,𝑡. (1)
The coefficient of interest, δ, captures the effect of adverse NIRP-exposure on Yi,t. A plausible
control group in a DiD setup with binary treatment variable would require us to observe banks
that are completely unaffected by the SNB’s policy. Such banks do not exist. With a continuous
treatment variable, instead, we can identify the effect of a “more adverse NIRP-exposure”,
without explicitly observing unaffected banks (i.e., banks with ERi = 0). This identification
assumes that the effect we are after is independent of ERi being positive or negative. To see
33 We use quarterly data when we analyse the effect on risk-taking and bi-annual data when we study bank income. Treatment dummies in
these cases are equal to one for all quarters (semesters) following and including 2015Q1 (2015H1), and zero before. 34 Rather than using the values of SNB Reserves and Total Assets in December 2014, we take average values over the entire pre-treatment
period. While our results are robust across measures, we believe that the average measure is better suited to capture banks’ actual NIRP-
exposure (as opposed to a potentially random fluctuation in reserves in December 2014).
16
that this is indeed the case, it is instructive to notice that one unit more of positive ERi requires
banks to pay 75 bps more interest to the central bank, while a unit more when ERi is negative
means that banks lose the opportunity to borrow one unit on the interbank market (at
approximately -75 bps) that they could lend to the SNB for free. As long as the transmission to
the interbank market is intact (and fast), the cost – in both cases – is 75 bps.
Building on Model (1), we consider several extensions: First, for our main regression, we
saturate the model with bank and time fixed effects (FE) to control for time-invariant, bank-
specific heterogeneity and for period-specific factors. Next, we allow banks to react differently
to their NIRP-exposure if they are headquartered in a canton with a foreign border (Borderi is
a dummy variable that is equal to one in this case). This is primarily to capture any potential
confounding from the simultaneous effect on the exchange rate, which – for the locally active
banks that we consider – has a primarily geographic dimension. Finally, we add Google search
volumes (Mortg.Demandi,t) to control for bank-level variation in the demand for mortgages:35
𝑌𝑖,𝑡 = 𝛿(𝐸𝑅𝑖 × 𝑃𝑜𝑠𝑡𝑡) + �̂�(𝐸𝑅𝑖 × 𝐵𝑜𝑟𝑑𝑒𝑟𝑖 × 𝑃𝑜𝑠𝑡𝑡) + �̂�(𝐵𝑜𝑟𝑑𝑒𝑟𝑖 × 𝑃𝑜𝑠𝑡𝑡)
+�̂� ⋅ 𝑀𝑜𝑟𝑡𝑔. 𝐷𝑒𝑚𝑎𝑛𝑑𝑖,𝑡 + 𝐹𝐸𝑖 + 𝐹𝐸𝑡 + 𝑢𝑖,𝑡 (2)
Next, to capture not only the average treatment effect for the post-treatment period, we also
estimate monthly effects by interacting our treatment variable with indicators for 35 of the 36
sample months, using the first month of the entire sample as the reference date:
𝑌𝑖,𝑡 = 𝛼′ + ∑ 𝛿′𝑠 ⋅ (𝐸𝑅𝑖 × 𝐹𝐸𝑠)
06
16
𝑠=08
13
+ 𝐹𝐸𝑖 + 𝐹𝐸𝑡 + 𝑒𝑖,𝑡. (3)
35 Since we do not know exactly how Google generates its search volumes for topic searches, we also provide robustness checks that do not
feature any measure of mortgage demand (in the Appendix). As expected, it turns out that changes in demand do not covary in any meaningful
way with our exposure measure, so that coefficients are largely unchanged.
17
The coefficients of interest (δ’s) provide evidence of the difference in the intertemporal change
in Yi,t between our initial sample date (July 2013) and each subsequent month. Over the 17 pre-
treatment months this constitutes an implicit placebo test, which should return insignificant
interaction effects under the parallel trend assumption. Over the 18 post-treatment months,
instead, Model (3) provides additional insights into the evolution of the negative rate effect
over time. We estimate our models using ordinary least squares and cluster our standard errors
at the bank level (Bertrand et al., 2004).
5.2. Identification
An important identifying assumption is that – absent the NIRP – the time trends in our outcome
variables would be parallel for banks with different levels of exposed reserves. To support this
assumption, we first plot the histogram of our treatment variable in Figure 8, which confirms
that neither banks nor the SNB targeted any specific cut-off level. In addition, we also provide
graphic illustrations of pre- and post-treatment trends in key balance sheet variables (Figures
9, 11 and 13) and of the coefficients on the interaction terms from Model (3) (Figures 10, 12
and 14). Consistent with the assumption that pre-treatment growth rates are not systematically
different, Figures 9, 11 and 13 exhibit parallel trends over the first 18 months of our sample.
In addition, Figures 10, 12 and 14 suggest that growth differences between more and less
exposed banks are insignificant during the pre-treatment, but not the post-treatment, period.
Overall, we conclude that this visual evidence supports our identifying assumption. The same
evidence also suggests that we do not necessarily need to include additional control variables
in our regression, so that we can abstract, among other things, from concerns related to the
estimation of dynamic panel models.36
36 Recall that we do nonetheless include bank and period fixed effects in Model (2) and that we also control for whether a bank is headquartered
in a canton with a foreign border, as well as for the time-variation in Google search volumes to capture changes in mortgage demand.
18
A second challenge to our identification arises from the removal of the exchange rate peg that
occurred simultaneously with the implementation of negative interest rates in Switzerland. The
unpegging came as a surprise to financial markets and lead to heavy losses among currency
traders betting on a depreciation of the CHF. Their losses transmitted to direct brokers, both
foreign and domestic, who had financed the currency traders’ bets with Lombard Loans.37 The
parallel trend assumption would then be violated if these losses were systematically related to
the level of exposed reserves, e.g. because direct brokers have on average lower deposit ratios.
In response to this challenge, we do not include direct brokers in our sample and focus entirely
on domestically owned commercial banks. We further ensure isolation from exchange rate
effects by eliminating from our sample the two big universal banks (UBS and Credit Suisse),
which are more heavily engaged in foreign borrowing and lending than retail banks, and by
separately controlling for WM banks and for commercial banks that are headquartered in
border locations. For commercial banks themselves, the exchange rate shock may initially have
been expected to matter insofar as the more export-oriented of the corporate clients may have
suffered from reductions in competitiveness. In hindsight, these clients have by and large coped
well, aided inter alia by tax-financed schemes to support shorter working hours as well as by
the international price setting power of many Swiss exporters.38 Even if changes in
competitiveness did play a role, however, our conclusions would only be affected if the
resulting differences in corporate credit demand would be systematically related to the level of
banks’ negative NIRP-exposure.39
37 See, for example, “Swiss central bank moves to negative deposit rate” (Financial Times; 18.12.2014) and “Swiss franc storm claims scalp
of top forex broker” (Financial Times; 20.01.2015), where the latter is referring to a UK entity. 38 To avoid bankruptcies or heavy losses of employers, as well as lay-offs in the face of temporarily lower demand for a firm’s products,
“short-term work” schemes have employees work only e.g. 50% of regular hours but receive 80% of their full wage, where the difference is
paid by the government. For the government this is cheaper than the unemployment benefits due if the person were laid off entirely. See for example https://www.ch.ch/en/short-time-work/. 39 For additional robustness, we also ran our models using various alternative treatment definitions (unreported, but available on request).
First, we considered the difference between total liquid assets required of each bank in 2015 and the Swiss exemption threshold (scaled by total assets), as an additional measure of banks’ dependence on, and need for, liquid assets and hence central bank reserves. Second, we also
considered the deposit ratio (the fraction of total assets financed through deposits) as in Heider et al. (forthcoming), to capture banks’ exposure
to the NIRP indirectly, i.e. by assuming the limited downward flexibility of their deposit rates. Both alternatives yield consistent results.
19
6. Results
Having argued that our empirical setup allows us to develop causal insights into banks’
responses to adverse NIRP-exposure, we now proceed to discuss our results. When the central
bank lowers the return on reserves, we first expect banks to reallocate resources to comparable
assets. Since changes in cash holdings are also charged negative interest, these are primarily
loans to the interbank market and liquid assets in non-CHF currencies. Next, we expect a
reallocation towards riskier and longer-term assets, and – once this seizes to be profitable – a
reduction in banks’ liabilities and thus the size of the balance sheet. We further expect the
transmission to deposit and lending rates to be interrupted, and so a tendency for banks to cut
longer-term financing rather than their deposit intake. In the remainder of this section, we test
these expectations empirically and analyse implications for bank profits and financial stability.
6.1. Central Bank Reserves & Liquid Assets
Table 3 shows how banks that are more adversely exposed to the NIRP respond to the
introduction of negative interest rates by reallocating their central bank reserves towards the
Swiss interbank market and towards liquid assets in non-CHF currencies. They also show how
more exposed banks ultimately – and despite this reallocation – reduce their liquid asset
holdings. The results are consistent with the lower/negative return on central bank reserves and
illustrate the transmission to assets that can serve as close substitutes. They are also robust to
controlling for changes in mortgage demand and banks’ exposure to the exchange rate, and
present for commercial as well as WM banks.40 Throughout, our estimates are also robust to
model specifications that do not include fixed effects or Google search volumes (Table 3A).
40 We see that WM reduce their central bank reserves and liquid asset holdings, and that they increase their exposure to non-CHF liquid assets, although the effects are smaller in magnitude than for commercial banks. We also see that WM banks do not seem to move their liquidity to
the interbank market, suggesting that their specific business model provides them with an easier access to alternative assets. Because of these
differences, and because we are worried that the observed response by WM banks may be confounded by the simultaneous exchange rate effect and affected by the special relationships with their clients, we will primarily focus on commercial banks. Similar to the results in Table
3, however, the evidence for WM that we present in the remainder of the paper suggests that our main conclusions apply beyond the more
focused sample of commercial banks.
20
6.2. Loans & Investments
Monetary policy transmission, in normal times, would predict an expansionary effect of a rate
cut and incentives to invest in riskier – and potentially more profitable – assets. Consistent with
these predictions, Table 4 shows that relatively more NIRP-exposed banks do indeed exhibit
relatively higher growth in mortgages after December 2014, and a stronger exposure to
financial and non-CHF assets.41 In some contrast to these expansionary dynamics, however,
we also observe that banks are either unable or unwilling to reallocate all of their liquid assets
towards riskier and longer-term assets. As a consequence, they end up with a contraction of
their balance sheet, which – because loans, mortgages and investments in financial and foreign
currency assets are not proportionally reduced – is accompanied by an increase in the balance
sheet shares of riskier assets. That is, we find an expansion of banks’ activities in the mortgage
market that is consistent with the risk-taking channel in normal times. In addition, however,
we also observe that banks do not allocate all of their excess liquidity towards other assets and
– in some contrast to the expansionary effects on credit – reduce the size of their balance sheet.
While this may limit risk-taking in terms of new loans, it increases banks’ overall exposure to
riskier assets. Because growth in corporate lending and investments in financial assets are not
reduced proportionally with the reduction in liquid and total assets, these asset categories end
up accounting for a larger share of the balance sheet. This not only increases the average
riskiness of banks’ portfolios, but also their average maturity.
At the balance sheet level, one could argue that our findings are consistent with the results in
Heider et al. (forthcoming), who simultaneously identify a contraction in lending (in balance
sheet size, in our case) and an expansion towards riskier borrowers (towards the mortgage
41 The effect on non-CHF assets is robust across specifications within the sample of commercial banks, but not when we estimate it in the
sample that also includes the WM banks. We therefore interpret this result more cautiously.
21
market, in our case). At the same time, our findings also seem to be consistent with Bottero et
al. (2018), who observe an expansionary effect on credit supply.
As before, our results are robust to controlling for changes in mortgage demand and banks’
exposure to the exchange rate. Since WM banks are not very active in corporate credit and
mortgage markets and have clients and relationships that are different from “banks” as they are
typically thought of in the literature on monetary policy transmission, our focus remains on
commercial banks. Table 4 nonetheless confirms that WM banks shrink their balance sheet as
well, suggesting that they too are unable or unwilling to redistribute all of the excess liquidity.
6.3. Interest Rates
Figures 2 and 3 illustrate the downward transmission of the SNB’s negative reserve rate to
different asset classes, as well as the ZLB on deposit rates. In Table 5, we investigate this
transmission further by studying the effect of adverse NIRP exposure on the reference
borrowing and lending rates that banks are required to report to the SNB and FINMA. Because
WM banks do not report meaningful lending rates, the table focuses on the subsample of
commercial banks. We find that a more adverse exposure to the NIRP induces banks to lower
their (short- and longer-term) borrowing rates less than banks with a relatively weaker
exposure.42 Since these banks are more adversely exposed to the interest rate change, this is
potentially surprising, as one might have expected them to have stronger incentives to cut
funding costs more. That this is not the case may have multiple reasons. A first hypothesis is
that these banks were already paying lower rates before the introduction of the NIRP, so that
they would be more constrained in their ability to cut rates before hitting the ZLB. When we
control for banks’ pre-NIRP sight deposit rate in column (3), however, we find that this does
not seem to influence banks’ response. A second hypothesis is that more adversely exposed
42 The downward transmission is visible in the negative coefficient on our Post dummy that we report in our specification without fixed effects
(see Online Appendix). Here we focus on the differential effect on interest rates set by banks that are more adversely exposed to the NIRP.
22
banks are also banks that rely more on deposit funding and that therefore have stronger
incentives to protect their deposit franchise (Drechsler et al., 2018). When we control for
banks’ deposit ratio in December 2014, it seems indeed to be the case that the higher deposit
ratio is driving the relatively higher sight deposit rate, while the coefficient on more adverse
NIRP exposure turns negative (and insignificant). This suggests that different proxies of banks’
“exposure to negative interest rates” may lead to different predictions about the transmission
to household deposit rates. A higher pre-NIRP deposit ratio (as used in Heider et al.,
forthcoming) captures a more valuable deposit franchise and thus incentives to protect it by
lowering deposit rates less. Once one controls for this effect, a bank that has relatively more of
its reserves exposed to the central bank’s negative interest rate, instead, has an incentive to
lower the deposit rate in order to reduce its funding costs.
With the interrupted downward transmission to the deposit rate in mind, it is then consistent
that more adversely exposed banks – in an effort to preserve their profit margins – also fail to
fully transmit negative rates to the mortgage market.43 Across the entire yield curve, we find
them to lower their rates less than banks that are less affected (with some indication of a
stronger effect for longer maturities). In general, the effects on reference rates are robust to
controlling for changes in mortgage demand and banks’ exposure to the exchange rate.
Notably, however, it seems to be the case that the adverse NIRP exposure does not translate
into a differential effect on deposit and mortgage rates for banks that are headquartered in
cantons with a foreign border. Consistent with Drechsler et al. (2018), this may be because
these banks face stronger competition, which would then lead to a less valuable deposit
franchise and thus to weaker incentives to protect it. As discussed before, the fact that more
adversely affected banks increase mortgage growth more, while lowering mortgage rates less,
43 Since variable mortgage rates that are tied to the LIBOR can always be refinanced at the LIBOR rate, and are – in addition – more transparent
and less frequently used, it is plausible that we do not find more adversely exposed banks to increase their margins on this category.
23
suggests that credit flows to riskier borrowers (either because the higher rate reflects a higher
risk-premium, or because the higher interest rate makes borrowers ceteris paribus riskier).
In Table 9, we also compare the effect to banks’ response to an earlier (smaller) rate cut in
August 2011. Since the SNB did not pay interest on reserves and there was no exemption from
interest rates at the time, we use as treatment variable the pre-treatment sum of a bank’s total
reserves and its net interbank position (NIB), net of the minimum reserve requirement (and
scaled by total assets). To facilitate the comparison, we also use the pre-NIRP sum of ER and
NIB, net of the negative rate exemption (and scaled by total assets) for our negative rate episode
and include only period and bank fixed effects as control variables. Consistent with the crucial
role of the ZLB on deposit rates, we find that more exposed banks cut their borrowing and
lending rates more in 2011, i.e. under positive rates, while we continue to observe that more
NIRP-exposed banks reduce their rates less in response to the rate cut in January 2015.
6.4. Funding Mix
We have established that adverse NIRP exposure induces banks to reallocate some of their
central bank reserves to assets that are close substitutes, but also to hold fewer liquid assets. In
Table 4, we further investigated whether central bank reserves are also allocated to other asset
classes and what the reduction in liquid asset holdings implied for the composition and
riskiness of banks’ portfolios. In Table 6, we now proceed to analyse the implications for
banks’ funding mix, and in combination with Table 3, for effects on the maturity mismatch.
Since banks reduce the size of their balance sheet (by reducing their liquid asset holdings), it
must be that they either stop rolling over their pre-existing liabilities or start repurchasing
outstanding bonds or shares. We find indeed that banks, which are more adversely exposed to
the Swiss NIRP, reduce their financing via bonds and medium term notes
(“Kassenobligationen”), i.e. via funding sources with longer maturities, for which they can
24
control volumes via repurchases or reduced issuance. Consistent with the interrupted
downward transmission to deposit rates we see no effect of adverse NIRP-exposure on the
growth of banks’ deposit financing. Similarly, we find no evidence that banks are reducing the
size of their balance sheet by buying back shares. Comparable to the effects on the asset side
of the balance sheet, however, the reduction in bond financing and the absence of a proportional
reduction in deposit and common equity implies an increase in banks’ deposit and tier 1 ratios.
As before, the results are robust to controlling for changes in mortgage demand and banks’
exposure to the exchange rate. Although one might expect that they are in a better position to
transmit negative interest rates to their (often high net worth) depositors, we also find evidence
that the reduction in longer-term financing is present among WM banks (although the effect
seems to be smaller in magnitude).
6.5. Financial Stability
We have shown that a stronger adverse exposure to the SNB’s negative interest rate policy
induced banks to invest in riskier assets and to shorten their balance sheets by reducing their
longer-term liabilities. In Table 7, we examine the financial stability implications of these
observations. We find that the – almost mechanic – increase in the unweighted CET1 ratio
(Table 6) does not – as one might suspect – imply that banks are more resilient; once changes
in the riskiness of their assets are taken into account, the regulatory capital cushion, i.e. the
difference between banks’ actual and required regulatory capital ratio (CET1 capital relative
to risk-weighted assets), ends up being smaller among more exposed banks. In addition to the
increase in mortgage lending and the reallocation towards corporate loans (Table 4), this is also
due to an increase of risk in the banks’ trading book. Value adjustments and the market risk
share of required equity are higher after December 2014, among banks that are more adversely
25
exposed to the NIRP.44 At the same time, and consistent with the previous findings that banks
lengthen the average maturity of their assets (Table 4) while shortening the average maturity
of their liabilities (Table 6), we also find a heightened exposure to liquidity risk and maturity
mismatch: banks with more exposure to the rate cut end up with lower LCR ratios, as the most
important category of high-quality liquid assets are the – now costly – central bank reserves,
and with relatively higher regulatory measures of interest rate risk. The results are robust to
controlling for changes in mortgage demand and banks’ exposure to the exchange rate.45
Instead, it seems to be the case that WM banks are taking less risk in response to NIRP exposure
(presumably because the perception of stability is even more important for their business
model) and – mirroring our results on interest rates in Table 5 – that banks, which are
headquartered in cantons with a foreign border are less affected.
6.6. Profitability
Banks with relatively higher exposed reserves face costs in comparison with their competitors:
directly, because they pay a negative interest rate on their reserves (for ER > 0) or lose access
to a profitable arbitrage opportunity (for ER < 0), and indirectly, because the inability or
unwillingness to transmit negative rates to depositors and the corresponding reduction of
(relatively cheaper) long-term borrowing increases average funding costs. Implicit in our
analysis has thus been the assumption that banks’ profitability suffered from the introduction
of negative reserve rates, and that the risk-taking and contraction of the balance sheet that we
previously identified was motivated by an intention to preserve/restore profitability. In Table
8, we therefore investigate the effect on banks’ income. We show that banks that were more
negatively exposed to the Swiss NIRP ended up equally (in the case of WM banks) or even
44 The market risk share of required equity capital can be interpreted as a proxy for the regulatory assessment of how relevant market risk exposure is, relative to exposure to credit and operational risk 45 The coefficient on RWA/TA does not remain significant when we control for mortgage demand and exchange rate exposure, but the – more
relevant – implications for the regulatory capital cushion prevail.
26
more (in the case of commercial banks) profitable than less exposed banks. Consistent with the
interrupted downward transmission to mortgage rates (Table 5), this net effect on profitability
stems from a higher net interest income and is driven by interest income from loans that seems
to have (over-)compensated for the imperfect downward adjustment of interest expenses.
Consistent, for example with Lopez et al. (2018), profitability is further protected by a
relatively higher fee income, only a fraction of which is generated from lending-related fees.
The effect on profitability is robust across different specifications and seems to be driven by
those banks that are unaffected by the simultaneous exchange rate shock; WM and commercial
banks in a border location, however, also succeed in avoiding reductions in profitability.
7. Conclusion
To investigate the effects of negative interest rate policies on banks’ behaviour, we exploit a
comprehensive supervisory dataset from Switzerland and a policy that – although presumably
designed with aggregate liquidity in mind – provided us with bank-specific measures of NIRP
exposure. The monthly frequency of our balance sheet data allows us to show visually that this
measure is not correlated with relevant bank characteristics, so that the parallel trends
assumption is plausibly satisfied, and we can draw causal inference from a DiD analysis.
Although we have access to the full range of supervisory data on the universe of Swiss banks
we focus our analysis on locally active commercial banks, which are fairly isolated from a
contemporaneous change in the CHF-EUR exchange rate, and which operate a transparent
business model that is not Switzerland-specific. This allows us to obtain insights on the
transmission of negative monetary policy rates that are likely to have external validity; a
consideration that seems to be corroborated by the fact that our key insights also hold for the
second largest group in our original sample: Swiss wealth management banks.
27
Our findings cover balance sheet structures, as well as bank-level information on interest rates,
profitability and risk-taking. We demonstrate how banks move liquidity away from their
central bank accounts and toward firstly the interbank market, secondly liquid assets
denominated in other currencies, and thirdly asset classes with lower liquidity and higher risk.
If they cannot reallocate all their liquid assets, banks shrink their balance sheets, primarily
through reductions in longer-term liabilities (as opposed to reductions in deposit funding).
The balance sheet restructuring, in combination with higher fee income and an incomplete
downward transmission to mortgage rates allowed banks to stay profitable, while the partial
substitution of liquid assets denominated in CHF with those denominated in other currencies
may have contributed to the SNB's objective of weakening the Swiss Franc. Importantly, from
a policy perspective, it also seems to have had adverse consequences for financial stability:
regulatory capital eroded, primarily because banks portfolios became riskier and banks’
maturity mismatch worsened. This contributed to a reduction in the liquidity coverage ratio
and to (traditional measures of) interest rate risk. Similar to the dynamics under positive rates,
the rate cut had an expansionary effect on the mortgage market, where credit was allocated to
riskier borrowers; the effect on banks’ entire balance sheet, however, was contractionary.
To conclude, we show that banks’ transmission of monetary policy – in particular to deposit
and loan rates – is broken when central bank rates turn negative. While this implies that the
known effects on new credit are potentially smaller than under positive rates, we illustrate how
the rebalancing of banks’ portfolios and the restructuring of their liabilities causes credit, as
well as, market, liquidity, and interest rate risk to increase. While the NIRP in Switzerland has
contributed to the objective of restoring the interest rate differential between the Euro and CHF,
banks’ incentives to replace costly reserves and to compensate for squeezed liability margins
thus also triggered increases in risk-taking. These side effects are at odds with financial stability
objectives and Basel III and need be weighed carefully against possible benefits of low rates.
28
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Drechsler, I., Savov, A., & Schnabl, P. (2018). Banking on Deposits: Maturity Transformation
without Interest Rate Risk. Mimeo.
Eggertson, G., Juelsrud, R. E., & Wold, E. G. (2017). Are Negative Nominal Interest Rates
Expansionary? Presentation: ECB Workshop on Monetary Policy in Non-Standard Times.
FINMA (2016), Annual Report 2015.
Heider, F., Saidi, F., & Schepens, G. (forthcoming). Life Below Zero: Bank Lending Under
Negative Policy Rates. Review of Financial Studies.
Ioannidou, V., Ongena, S., & Peydro, J.-L. (2014). Monetary policy, risk-taking and pricing:
evidence from a quasinatural experiment. Review of Finance, 19 (1), 95–144.
Jimenez, G., Ongena, S., Peydro, J.-L., & Saurina, J. (2012). Credit Supply and Monetary
Policy: Identifying the Bank Balance-Sheet Channel with Loan Applications. American
Economic Review, 102 (5), 2301-2326.
Jimenez, G., Ongena, S., Peydro, J.-L., & Saurina, J. (2014). Hazardous Times for Monetary
Policy: What Do Twenty-Three Million Bank Loans Say About the Effects of Monetary Policy
on Credit Risk-Taking? Econometrica, 82 (2), 463–505.
Kashyap, A. K. & Stein, J. (2000). What Do a Million Observations on Banks Say About the
Transmission of Monetary Policy? American Economic Review, 90 (3), 407-428.
30
Lopez, J. A., Rose, A. K. & Spiegel, M. M. (2018). Why have negative nominal interest rates
had such a small effect on bank performance? Cross country evidence. NBER WP 25004.
Nucera, F., Lucas, A., Schaumburg, J. & Schwaab, B. (2017). Do negative interest rates make
banks less safe? Economics Letters, 159, 112-115.
Urbschat, F. (2018). The Good, the Bad, and the Ugly: Impact of Negative Interest Rates and
QE on the Profitability and Risk-Taking of 1600 German Banks. CESifo Working Paper 7358
31
APPENDIX
I. Figures
Figure 1. Exchange Rates
Notes: The Figure illustrates the evolution of the exchange rates between the Swiss Franc (CHF) and the Euro (EUR) (CHF Exchange Rate (1 EUR)) and between
CHF and the US dollar (USD) (CHF Exchange Rate (1 USD)) between July 2013 and June 2016. The vertical line identifies the beginning of the treatment period
(01/2015). Source: SNB data portal.
Figure 2. Short-Term Borrowing Rates
Notes: The Figure illustrates the evolution of short-term interest rates and the Swiss National Bank’s (SNB’s) policy target between July 2013 and June 2016.
SARON is the average Swiss overnight lending rate; LIBOR (CHF, 3m) and LIBOR (CHF, 12m) are the three- and twelve-month LIBOR rates; Fed. Gov. Bonds
(1y) is the return on Swiss Government Bonds with a one-year maturity. The shaded area is the region between the SNB’s upper and lower bound for 3-month
LIBOR rate, and the grey line is the mean of the upper and lower bound. The vertical line identifies the beginning of the treatment period (01/2015). Source: SNB
data portal.
32
Figure 3. Long-Term Borrowing Rates
Notes: The Figure illustrates the evolution of long-term interest rates and the SNB’s policy target between July 2013 and June 2016. Fed. Gov. Bonds (8y) is the
return on Swiss Government Bonds with an eight-year maturity; Canton Bonds (8y) and Covered Bonds (8y) are the average return on Swiss Canton Bonds and
Covered Bonds with an eight-year maturity. Bank Bonds (8y) and NFC Bonds (8y) are the average return on the bonds of commercial banks and non-financial
corporations respectively. The shaded area is the region between the SNB’s upper and lower bound for 3-month LIBOR rate. The vertical line identifies the
beginning of the treatment period (01/2015). Source: SNB data portal.
Figure 4. Liability Margin (sight deposits)
Notes: The Figure illustrates the evolution of banks’ overnight liability margin between July 2013 and June 2016. Sight Deposit Rate is the average cost of deposit
funding; SARON is the average Swiss overnight lending rate. Liability Margin (overnight) is the difference between SARON and Sight Deposit Rate. The vertical
line identifies the beginning of the treatment period (01/2015). Source: SNB data portal.
33
Figure 5. Asset Margin (3-year, adjustable rate mortgages)
Notes: The Figure illustrates the evolution of banks’ short-term asset margin between July 2013 and June 2016. LIBOR (CHF, 3m) is the three month LIBOR
rate; Mortgage Rate (3y, LIBOR) is the average 3-year, adjustable mortgage rate, indexed to the 3-month LIBOR rate. Asset Margin (LIBOR) is the difference
between Mortgage Rate (3y, LIBOR) and LIBOR (CHF, 3m). The vertical line identifies the beginning of the treatment period (01/2015). Source: SNB data portal.
Figure 6. Asset Margin (10-year, fixed-rate mortgages)
Notes: The Figure illustrates the evolution of banks’ long-term asset margin between July 2013 and June 2016. Interest Rate Swap (10y) is the swap rate on ten
year fixed-rate mortgages; Mortgage Rate (10y) is the average 10-year, fixed-rate mortgage rate. Asset Margin (10y) is the difference between Mortgage Rate
(10y) and Interest Rate Swap (10y). The vertical line identifies the beginning of the treatment period (01/2015). Source: SNB data portal and Bloomberg.
34
Figure 7. Mortgage Demand
Figure 8. Treatment Intensities
0
.02
.04
.06
.08
Den
sity
-20 0 20 40 60ExposedReserves_TA
35
Figure 9. Interbank Transmission (trends)
Notes: The Figure illustrates the evolution of banks’ average liquid assets (upper panel) and net interbank position (lower panel), both as a fraction of total assets,
between July 2013 and July 2016. The evolution is depicted separately for those 25 banks in our baseline sample with exposed reserves over total assets in
December 2014 (our main treatment) above/below the sample median. The vertical line identifies the beginning of the treatment period (01/2015).
Figure 10. Interbank Transmission (by month)
Notes: The Figure illustrates the evolution of banks’ SNB reserves (upper panel) and net interbank position (lower panel), both as a fraction of total assets, between
July 2013 and July 2016, as predicted by our monthly regression coefficients. In contrast to the sample averages displayed in Figure 9, the regression coefficients
are obtained after controlling for the main/baseline effect each month has had on banks with no exposed reserves. The dotted lines and shaded area show the 95%
and 90% confidence interval respectively, based on standard errors clustered by bank. The vertical line identifies the beginning of the treatment period (01/2015).
36
Figure 11. Lending (trends)
Notes: The Figure illustrates the evolution of banks’ average mortgages (upper panel) and other loans (lower panel), both as a fraction of total assets, between
July 2013 and July 2016. The evolution is depicted separately for those 25 banks in our baseline sample with exposed reserves over total assets in December 2014
(our main treatment) above/below the sample median. The vertical line identifies the beginning of the treatment period (01/2015).
Figure 12. Lending (by month)
Notes: The Figure illustrates the evolution of banks’ mortgages and other loans (lower panel), both as a fraction of total assets, between July 2013 and July 2016,
as predicted by our monthly regression coefficients. In contrast to the sample averages displayed in Figure 11, the regression coefficients are obtained after
controlling for the main/baseline effect each month has had on banks with no exposed reserves. The dotted lines and shaded area show the 95% and 90% confidence
interval respectively, based on standard errors clustered by bank. The vertical line identifies the beginning of the treatment period (01/2015).
37
Figure 13. Borrowing (trends)
Notes: The Figure illustrates the evolution of banks’ average deposit funding (upper panel) and bond funding (lower panel), both as a fraction of total assets,
between July 2013 and July 2016. The evolution is depicted separately for those 25 banks in our baseline sample with exposed reserves over total assets in
December 2014 (our main treatment) above/below the sample median. The vertical line identifies the beginning of the treatment period (01/2015).
Figure 14. Borrowing (by month)
Notes: The Figure illustrates the evolution of banks’ deposit funding and bond funding (lower panel), both as a fraction of total assets, between July 2013 and
July 2016, as predicted by our monthly regression coefficients. In contrast to the sample averages displayed in Figure 13, the regression coefficients are obtained
after controlling for the main/baseline effect each month has had on banks with no exposed reserves. The dotted lines and shaded area show the 95% and 90%
confidence interval respectively, based on standard errors clustered by bank. The vertical line identifies the beginning of the treatment period (01/2015).
38
II. Tables
Table 1. Summary Statistics (Pooled)
Notes: The Table shows summary statistics for our pooled sample, covering the 50 domestically-owned retail banks that feature in our baseline sample over
respectively 36 months (balance sheet positions), 6 semesters (income) and 12 quarters (capitalization and risk-taking measures).
Variable Obs Banks Periods Mean SD Min Max
Exposed SNB Reserves/TA 1800 50 -5.76 4.30 -12.94 8.75
(Exposed SNB Res + Net IB Pos) / TA 1800 50 -5.92 5.91 -23.41 13.67
Deposits / TA 1800 50 47.60 10.86 24.94 69.61
2015 LCR Req. - Neg. Rate Exemption 1764 49 -0.06 0.03 -0.15 0.00
TA (yoy growth) 1800 50 36 5.12 4.35 -27.01 23.44
All SNB Reserves: % of TA 1800 50 36 7.77 4.17 0.04 27.51
Liquid Assets: % of TA 1800 50 36 8.34 4.09 0.12 28.06
Claims on Banks: % of TA 1800 50 36 2.94 2.41 0.09 14.48
Net Interbank Pos: % of TA 1800 50 36 -0.86 4.39 -16.92 10.07
Loan Assets: % of TA 1800 50 36 8.49 4.23 1.58 22.29
Mortgage Assets: % of TA 1800 50 36 72.78 9.72 32.39 88.69
Fin. Assets: % of TA 1800 50 36 4.70 2.71 0.56 18.42
Deposit Funding: % of TA 1800 50 36 67.59 7.58 39.11 95.99
Bond Funding: % of TA 1800 50 36 13.04 5.58 0.00 25.58
Dues to Banks: % of TA 1764 49 36 3.92 5.04 0.00 24.37
Cash Bond Funding: % of TA 1800 50 36 3.71 3.89 0.00 16.00
FX Share Total Assets 1800 50 36 2.73 3.33 0.01 17.57
FX Share Total Liabilities 1800 50 36 4.38 5.31 0.00 27.75
RWA Density 600 50 12 0.46 0.12 0.02 1.13
Credit Risk Share of Req. Equity 600 50 12 0.94 0.21 0.65 2.56
Market Risk Share of Req. Equity 600 50 12 0.01 0.03 0.00 0.23
OpRisk Share of Req. Equity 600 50 12 0.06 0.02 0.04 0.20
IRR: Bank Ass CHF 600 50 12 -0.06 0.04 -0.19 0.08
IRR: Bank Ass FX 600 50 12 0.06 0.03 0.00 0.20
IRR: Avg. Ass 600 50 12 -0.05 0.04 -0.12 0.11
IRR: 2y Ass 600 50 12 -0.10 0.04 -0.20 0.04
CET1 / TA 600 50 12 7.69 1.58 4.02 12.33
CET1 / RWA 600 50 12 15.66 3.01 8.37 23.72
CET1/RWA - B3 Requirement 600 50 12 8.21 3.04 0.57 16.32
Int Earned on Loans, % of TA 300 50 6 1.56 0.26 0.84 2.38
Int Earned, % of TA 300 50 6 1.65 0.27 0.89 2.47
Int Paid, % of TA 300 50 6 0.51 0.17 0.06 0.98
Net Int Inc, % of TA 300 50 6 1.13 0.18 0.61 1.78
Loan Fees, bps(1/100%) of TA 300 50 6 1.62 2.48 0.03 17.61
All Fees, bps(1/100%) of BusVol 300 50 6 19.70 9.05 0.00 59.24
Net Fee Inc, bps(1/100%) of BusVol 300 50 6 16.52 7.91 -1.57 46.92
Gross Profit, % of BusVol 300 50 6 0.43 0.24 0.00 0.97
39
Table 2. Summary Statistics by Binary Treatment Group and Treatment Period
Obs Banks Periods Mean sd Min Max Obs Banks Periods Mean SD Min Max Post-Pre Pval
Exposed SNB Reserves/TA 25 25 1 -8.64 1.83 -12.94 -6.34
(Exposed SNB Res + Net IB Pos) / TA 25 25 1 -9.03 4.74 -23.41 -1.06
Deposits / TA 25 25 1 44.91 8.29 28.91 61.72
2015 LCR Req. - Neg. Rate Exemption 25 24 1 -0.07 0.03 -0.15 -0.01
All SNB Reserves: % of TA 450 25 18 4.06 1.90 0.05 10.79 450 25 18 9.14 3.12 0.75 16.32 5.09 0.00
Liquid Assets: % of TA 450 25 18 4.74 1.86 0.38 11.24 450 25 18 9.67 3.06 1.62 17.12 4.93 0.00
Claims on Banks: % of TA 450 25 18 3.19 2.22 0.15 9.62 450 25 18 2.23 1.60 0.09 13.96 -0.96 0.00
Net Interbank Pos: % of TA 450 25 18 -0.35 4.41 -13.53 8.64 450 25 18 -2.75 4.80 -16.92 10.07 -2.39 0.00
Loan Assets: % of TA 450 25 18 10.28 4.32 2.75 22.29 450 25 18 8.95 3.65 2.58 18.40 -1.33 0.00
Mortgage Assets: % of TA 450 25 18 74.81 7.37 53.24 88.69 450 25 18 72.89 8.03 47.76 87.70 -1.92 0.00
Fin. Assets: % of TA 450 25 18 5.05 1.89 1.34 11.72 450 25 18 4.51 2.08 0.56 11.46 -0.54 0.00
Deposit Funding: % of TA 450 25 18 67.20 5.15 56.84 83.21 450 25 18 64.75 6.28 51.46 85.84 -2.45 0.00
Bond Funding: % of TA 450 25 18 14.22 4.75 0.00 21.63 450 25 18 15.67 5.03 0.00 25.58 1.45 0.00
Dues to Banks: % of TA 450 25 18 3.55 3.98 0.00 13.76 450 25 18 5.14 5.32 0.00 24.37 1.59 0.00
Cash Bond Funding: % of TA 450 25 18 3.51 3.18 0.07 12.64 450 25 18 2.89 2.89 0.03 11.72 -0.62 0.00
FX Share Total Assets 450 25 18 3.05 3.28 0.10 16.82 450 25 18 2.32 2.81 0.07 16.66 -0.73 0.00
FX Share Total Liabilities 450 25 18 4.03 3.36 0.03 16.78 450 25 18 4.44 4.61 0.04 24.45 0.40 0.13
RWA Density 150 25 6 0.47 0.12 0.06 0.97 150 25 6 0.45 0.14 0.02 0.96 -0.03 0.10
Credit Risk Share of Req. Equity 150 25 6 0.96 0.20 0.86 1.88 150 25 6 0.97 0.21 0.86 1.89 0.01 0.73
Market Risk Share of Req. Equity 150 25 6 0.00 0.00 0.00 0.03 150 25 6 0.00 0.01 0.00 0.03 0.00 0.15
OpRisk Share of Req. Equity 150 25 6 0.06 0.02 0.04 0.14 150 25 6 0.06 0.02 0.04 0.13 0.00 0.28
IRR: Bank Ass CHF 150 25 6 -0.05 0.04 -0.16 0.08 150 25 6 -0.06 0.05 -0.19 0.05 -0.01 0.16
IRR: Bank Ass FX 150 25 6 0.06 0.03 0.00 0.17 150 25 6 0.06 0.04 0.01 0.20 0.01 0.11
IRR: Avg. Ass 150 25 6 -0.05 0.03 -0.10 0.05 150 25 6 -0.04 0.04 -0.10 0.06 0.00 0.27
IRR: 2y Ass 150 25 6 -0.09 0.03 -0.16 0.01 150 25 6 -0.10 0.04 -0.16 -0.02 -0.01 0.00
CET1 / TA 150 25 6 8.01 1.87 4.60 12.33 150 25 6 7.87 1.78 4.68 12.29 -0.14 0.50
CET1 / RWA 150 25 6 15.77 3.38 9.28 23.72 150 25 6 16.36 3.14 10.07 23.29 0.59 0.12
CET1/RWA - B3 Requirement 150 25 6 8.31 3.39 2.28 16.32 150 25 6 8.89 3.15 3.07 16.00 0.59 0.12
Int Earned on Loans, % of TA 75 25 3 1.70 0.19 1.37 2.05 75 25 3 1.46 0.22 1.03 1.93 -0.24 0.00
Int Earned, % of TA 75 25 3 1.79 0.20 1.44 2.20 75 25 3 1.53 0.24 1.04 2.13 -0.26 0.00
Int Paid, % of TA 75 25 3 0.61 0.15 0.33 0.98 75 25 3 0.43 0.16 0.06 0.75 -0.18 0.00
Net Int Inc, % of TA 75 25 3 1.19 0.17 0.90 1.62 75 25 3 1.10 0.16 0.89 1.56 -0.08 0.00
Loan Fees, bps(1/100%) of TA 75 25 3 1.79 3.16 0.15 17.61 75 25 3 1.41 2.28 0.04 13.13 -0.38 0.41
All Fees, bps(1/100%) of BusVol 75 25 3 21.59 8.86 9.01 49.94 75 25 3 19.56 7.90 8.07 41.97 -2.02 0.14
Net Fee Inc, bps(1/100%) of BusVol 75 25 3 19.26 8.81 6.02 46.92 75 25 3 17.48 7.85 6.19 39.83 -1.78 0.19
Gross Profit, % of BusVol 75 25 3 0.58 0.15 0.28 0.93 75 25 3 0.36 0.27 0.00 0.83 -0.22 0.00
Obs Banks Periods Mean sd Min Max Obs Banks Periods Mean SD Min Max Post-Pre Pval
Exposed SNB Reserves/TA 25 25 1 -2.88 4.14 -6.26 8.75
(Exposed SNB Res + Net IB Pos) / TA 25 25 1 -2.80 5.29 -12.89 13.67
Deposits / TA 25 25 1 50.29 12.38 24.94 69.61
2015 LCR Req. - Neg. Rate Exemption 25 25 1 -0.06 0.03 -0.12 0.00
All SNB Reserves: % of TA 450 25 18 8.30 4.76 0.04 27.51 450 25 18 9.59 3.79 2.27 22.06 1.29 0.00
Liquid Assets: % of TA 450 25 18 8.86 4.70 0.12 28.06 450 25 18 10.11 3.71 2.33 22.50 1.25 0.00
Claims on Banks: % of TA 450 25 18 3.29 2.66 0.30 11.52 450 25 18 3.06 2.84 0.13 14.48 -0.23 0.21
Net Interbank Pos: % of TA 450 25 18 0.16 3.74 -9.44 10.03 450 25 18 -0.51 3.94 -11.86 6.73 -0.67 0.01
Loan Assets: % of TA 450 25 18 7.65 4.11 1.58 20.70 450 25 18 7.09 4.08 2.14 19.66 -0.56 0.04
Mortgage Assets: % of TA 450 25 18 71.80 11.19 36.52 86.31 450 25 18 71.63 11.32 32.39 85.90 -0.17 0.82
Fin. Assets: % of TA 450 25 18 4.84 3.41 1.05 18.42 450 25 18 4.41 3.11 0.63 16.88 -0.43 0.05
Deposit Funding: % of TA 450 25 18 69.13 8.29 43.76 92.31 450 25 18 69.27 9.04 39.11 95.99 0.14 0.81
Bond Funding: % of TA 450 25 18 10.94 5.48 0.00 21.19 450 25 18 11.31 5.58 0.00 22.85 0.37 0.31
Dues to Banks: % of TA 432 24 18 3.32 5.10 0.00 20.42 432 24 18 3.65 5.47 0.00 22.78 0.32 0.37
Cash Bond Funding: % of TA 450 25 18 4.49 4.68 0.00 15.94 450 25 18 3.96 4.37 0.00 16.00 -0.54 0.08
FX Share Total Assets 450 25 18 2.94 3.79 0.01 17.57 450 25 18 2.61 3.34 0.03 17.01 -0.33 0.17
FX Share Total Liabilities 450 25 18 4.44 6.15 0.00 24.42 450 25 18 4.60 6.52 0.00 27.75 0.17 0.69
RWA Density 150 25 6 0.47 0.12 0.34 1.05 150 25 6 0.45 0.09 0.37 1.13 -0.02 0.05
Credit Risk Share of Req. Equity 150 25 6 0.94 0.22 0.71 1.85 150 25 6 0.91 0.20 0.65 2.56 -0.03 0.22
Market Risk Share of Req. Equity 150 25 6 0.02 0.04 0.00 0.23 150 25 6 0.02 0.04 0.00 0.21 0.00 0.44
OpRisk Share of Req. Equity 150 25 6 0.06 0.02 0.04 0.20 150 25 6 0.06 0.02 0.04 0.18 0.00 0.08
IRR: Bank Ass CHF 150 25 6 -0.05 0.04 -0.14 0.05 150 25 6 -0.06 0.04 -0.15 0.04 -0.01 0.02
IRR: Bank Ass FX 150 25 6 0.06 0.03 0.00 0.15 150 25 6 0.07 0.04 0.00 0.15 0.01 0.04
IRR: Avg. Ass 150 25 6 -0.05 0.04 -0.12 0.10 150 25 6 -0.05 0.05 -0.11 0.11 0.00 0.34
IRR: 2y Ass 150 25 6 -0.10 0.05 -0.18 0.04 150 25 6 -0.12 0.05 -0.20 0.03 -0.02 0.00
CET1 / TA 150 25 6 7.32 1.22 4.02 9.27 150 25 6 7.56 1.28 4.53 11.67 0.24 0.09
CET1 / RWA 150 25 6 14.85 2.52 8.37 19.20 150 25 6 15.64 2.75 9.35 21.20 0.79 0.01
CET1/RWA - B3 Requirement 150 25 6 7.43 2.58 0.57 12.11 150 25 6 8.22 2.80 1.95 14.20 0.79 0.01
Int Earned on Loans, % of TA 75 25 3 1.63 0.27 0.86 2.25 75 25 3 1.47 0.28 0.84 2.38 -0.16 0.00
Int Earned, % of TA 75 25 3 1.73 0.27 0.97 2.31 75 25 3 1.54 0.28 0.89 2.47 -0.19 0.00
Int Paid, % of TA 75 25 3 0.58 0.15 0.29 0.86 75 25 3 0.43 0.15 0.06 0.76 -0.15 0.00
Net Int Inc, % of TA 75 25 3 1.15 0.18 0.68 1.56 75 25 3 1.10 0.19 0.61 1.78 -0.04 0.15
Loan Fees, bps(1/100%) of TA 75 25 3 1.64 2.25 0.03 11.73 75 25 3 1.63 2.12 0.03 10.87 -0.01 0.98
All Fees, bps(1/100%) of BusVol 75 25 3 19.31 10.39 6.16 59.24 75 25 3 18.32 8.75 0.00 45.67 -0.99 0.53
Net Fee Inc, bps(1/100%) of BusVol 75 25 3 14.87 7.20 0.00 34.14 75 25 3 14.46 6.80 -1.57 27.98 -0.40 0.72
Gross Profit, % of BusVol 75 25 3 0.48 0.16 0.16 0.97 75 25 3 0.31 0.25 0.00 0.83 -0.18 0.00
Panel B: ER >= Median
Panel A: ER < MedianJuly 2013 - December 2014 January 2015 - June 2016 Diff
July 2013 - December 2014 January 2015 - June 2016 Diff
40
Table
3.
Cen
tral
Ban
k R
eser
ves
& L
iqu
id A
sset
s
The
sam
ple
cover
s 50 d
om
esti
call
y-o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e ex
pre
ssed
in
per
centa
ges
of
tota
l as
sets
, i.
e. a
s Y
t/T
At,
or
in y
ear-
on-y
ear
gro
wth
rat
es, i.
e. a
s (Y
t - Y
t-1)/
Yt-
1. In
colu
mns
(7),
(8),
(13),
and (
14),
the
dep
enden
t var
iable
is
the
fore
ign c
urr
ency
shar
e of
inte
rban
k
loan
s or
liquid
ass
ets,
i.e
. eq
ual
to Y
FX
t/(Y
FX
t +
YC
HF
t).
Post
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to
expose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on,
scal
ed b
y t
ota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to
one
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
Mort
g.
Dem
and i
s eq
ual
to G
oogle
sea
rch v
olu
mes
for
the
topic
“M
ort
gag
e”.
Robust
nes
s ch
ecks
wit
hout
fixed
eff
ects
and w
ithout
sea
rch v
olu
mes
are
avai
lable
in t
he
Onli
ne
Appen
dix
.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
(13
)(1
4)
(15
)(1
6)
SN
B
Res
erv
es/
TA
SN
B
Res
erv
es/
TA
SN
B
Res
erv
es
(gro
wth
)
SN
B
Res
erves
(gro
wth
)
Net
Inte
rban
k
Po
siti
on
/
TA
Net
Inte
rban
k
Posi
tio
n/
TA
FX
shar
e
of
Inte
rban
k
Loan
s
FX
sh
are
of
Inte
rban
k
Lo
ans
Net
Inte
rban
k
Posi
tion
(gro
wth
)
Net
Inte
rban
k
Posi
tio
n
(gro
wth
)
Liq
uid
Ass
ets/
TA
Liq
uid
Ass
ets/
TA
FX
sh
are
of
Liq
uid
Ass
ets
FX
sh
are
of
Liq
uid
Ass
ets
Liq
uid
Ass
ets
(gro
wth
)
Liq
uid
Ass
ets
(gro
wth
)
(A)
Po
st ×
ER
-0.5
4*
**
-0.5
2*
**
-9.3
3*
*0.7
50
.24*
**
0.2
0*
*-0
.17
-0.5
31
.91
-19
.43
-0.5
3*
**
-0.5
1*
**
0.4
9*
*0.2
6*
**
-7.9
4*
**
-5.6
1*
*
(0.0
7)
(0.0
5)
(4.0
1)
(6.3
0)
(0.0
7)
(0.0
8)
(0.3
8)
(0.6
3)
(5.6
0)
(42
.59
)(0
.07)
(0.0
5)
(0.1
9)
(0.0
7)
(2.3
0)
(2.5
6)
(B)
Po
st ×
ER
× W
M0
.19
8.4
5*
-0.2
1**
0
.13
-1
.44
0
.19
-0
.35
*
7.4
7*
**
(0.1
3)
(4
.41
)(0
.10)
(0
.39
)
(5.6
3)
(0
.13)
(0
.20
)
(2.4
7)
(C)
Po
st ×
WM
-0.6
2
14
.92
-3.0
7
-1.6
5
-35
.67
0
.46
-3
.50
-7
.67
(1.7
4)
(6
5.7
5)
(2.7
7)
(3
.59
)
(36
.58
)
(1.6
4)
(2
.36
)
(35.4
9)
Po
st ×
ER
× B
ord
er-0
.02
-5
.75
0.0
50.3
2
22
.90
-0.0
10.2
7-1
.71
(0.0
9)
(7
.82)
(0.1
0)
(0.7
4)
(5
1.0
2)
(0.0
9)
(0.2
3)
(3
.52
)
Po
st ×
Bord
er0
.32
-4
1.5
7*
-0.4
3-1
.65
57
.22
0.2
6-1
.10
-2
4.7
7
(0.3
8)
(2
3.9
6)
(0.4
7)
(2.8
8)
(5
3.0
2)
(0.3
9)
(0.7
3)
(2
0.4
3)
Mo
rtg
. D
eman
d-0
.01
**
0.0
50
.02*
**
-0.0
1
-5.3
4-0
.01
**
0.0
0
-0.1
7
(0.0
1)
(0
.62)
(0.0
1)
(0.0
4)
(3
.90
)(0
.01)
(0.0
1)
(0
.27
)
(A)
+ (
B)
-0.3
50
--0
.88
2-
0.0
25
--0
.03
6-
0.4
75
--0
.33
7-
0.1
44
--0
.46
7-
Pv
al0
.002
-0.6
31
-0
.72
9-
0.6
69
-0.3
70
-0
.003
-0.0
37
-0
.60
6-
Ban
ks
All
Com
.A
llC
om
.A
llC
om
.A
llC
om
.A
llC
om
.A
llC
om
.A
llC
om
.A
llC
om
.
Obs.
3,4
56
1,8
00
3,4
04
1,8
00
3,4
56
1,8
00
3,3
96
1,8
00
3,3
42
1,7
64
3,4
56
1,8
00
3,3
96
1,8
00
3,4
12
1,8
00
R2
0.2
40
.57
0.0
30.0
00
.04
0.2
80
.01
0.0
10
.01
0.0
00
.20
0.5
60
.08
0.1
80
.05
0.0
2
Nr.
of
ban
ks
96
50
96
50
96
50
96
50
94
49
96
50
96
50
96
50
Ban
k F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
Tim
e F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
Sta
nd
ard
err
ors
clu
ster
ed b
y b
ank
. **
* p
<0
.01,
**
p<
0.0
5, *
p<
0.1
41
Table
4. L
oan
s an
d I
nves
tmen
ts
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
201
6 (
36 m
onth
s). In
colu
mns
(1)
to (
9),
and (
13)
and (
14),
the
dep
enden
t var
iable
s ar
e ex
pre
ssed
in p
erce
nta
ges
of
tota
l as
sets
, i.
e. a
s Y
t/TA
t, or
in y
ear-
on-y
ear
gro
wth
rat
es,
i.e.
as
(Yt
- Y
t-1)/
Yt-
1.
In c
olu
mns
(10)
to (
12)
they
are
equal
to t
he
dif
fere
nce
bet
wee
n a
sset
s an
d l
iabil
itie
s in
non-C
HF
curr
enci
es,
scal
ed b
y t
ota
l as
sets
. P
ost
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd
equal
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t
var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent dif
fere
nce
bet
wee
n tota
l S
NB
res
erves
and the
regula
tory
exem
pti
on, sc
aled
by tota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is loca
ted in a
can
ton that
shar
es a
bord
er w
ith a
fore
ign c
ountr
y. M
ort
g. D
eman
d is
equal
to G
oogle
sea
rch v
olu
mes
for
the
topic
“M
ort
gag
e”. R
obust
nes
s
chec
ks
wit
hout
fixed
eff
ects
and w
ithout
sea
rch v
olu
mes
are
avai
lable
in t
he
Onli
ne
Appen
dix
.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11)
(12
)(1
3)
(14
)
Lo
ans/
TA
Lo
ans/
TA
Lo
ans
(gro
wth
)
Mo
rtg
ages
/ T
A
Mort
gag
es
/ T
A
Mo
rtgag
es
(gro
wth
)
Fin
anci
al
Ass
ets/
TA
Fin
anci
al
Ass
ets/
TA
Fin
anci
al
Ass
ets
(gro
wth
)
FX
(Ass
sets
-
Lia
bilit
ies)
/ T
A
FX
(Ass
sets
-
Lia
bil
itie
s)
/ T
A
FX
(Ass
sets
-
Lia
bilit
ies)
/ T
A
To
tal
Ass
ets
(gro
wth
)
To
tal
Ass
ets
(gro
wth
)
(A)
Po
st ×
ER
0.1
4**
*0
.12
**
*0
.39
0.1
6**
*0.1
7*
**
0.1
3*
*0.0
6*
**
0.0
5*
0.3
20
.08
0.1
2**
0.0
5**
-0.3
9*
**
-0.1
6*
(0.0
2)
(0.0
3)
(0.4
0)
(0.0
5)
(0.0
5)
(0.0
6)
(0.0
2)
(0.0
2)
(0.4
1)
(0.0
8)
(0.0
5)
(0.0
2)
(0.0
9)
(0.0
9)
(B)
Post
× E
R ×
WM
-0.0
40
.25*
*
(0.1
1)
(0.1
2)
(C)
Post
× W
M-2
.24
-0.6
7
(1.4
4)
(2.9
1)
Po
st ×
ER
× B
ord
er-0
.03
-0.3
0-0
.06
-0.1
8-0
.01
1.3
40
.04
-0.2
3
(0.0
5)
(0.6
8)
(0.0
9)
(0.1
1)
(0.0
4)
(1.2
9)
(0.0
9)
(0.1
4)
Po
st ×
Bo
rder
-0.5
9*
**
-13
.22*
-0.4
5-1
.10
-0.4
0*
*1
8.3
7*
-0.6
4-1
.21
**
(0.2
1)
(6.7
4)
(0.3
2)
(0.7
2)
(0.1
6)
(10
.45
)(0
.45)
(0.5
5)
Mort
g.
Dem
and
0.0
0**
-0.0
0-0
.01
0.0
00
.00
-0.0
30
.00
0.0
1*
(0.0
0)
(0.0
3)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
3)
(0.0
0)
(0.0
1)
(A)
+ (
B)
--
--
--
--
-0.0
43
--
-0.1
37
-
Pv
al-
--
--
--
--
0.5
08
--
0.0
82
-
Ban
ks
Co
m.
Co
m.
Com
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.A
llC
om
.C
om
.A
llC
om
.
Ob
s.1
,800
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
3,3
96
1,8
00
1,8
00
3,4
12
1,8
00
R2
0.2
90
.33
0.0
40
.13
0.1
40
.05
0.1
10
.14
0.0
60
.06
0.1
10
.15
0.0
80.0
7
Nr.
of
ban
ks
50
50
50
50
50
50
50
50
50
96
50
50
96
50
Ban
k F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
YQ
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Sta
ndar
d e
rrors
clu
ster
ed b
y b
ank
. *
**
p<
0.0
1, *
* p
<0
.05
, *
p<
0.1
42
Table
5.
Inte
rest
Rat
es
The
sam
ple
cover
s up t
o 5
0 d
om
esti
call
y o
wned
com
mer
cial
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s). T
he
dep
enden
t var
iable
s ar
e eq
ual
to i
nte
rest
rat
es t
hat
ban
ks
are
requir
ed
to r
eport
to
the
SN
B (
actu
al len
din
g r
ates
may
var
y w
ith c
ust
om
er c
har
acte
rist
ics)
. C
olu
mns
(1)
to (
6)
show
borr
ow
ing r
ates
and c
olu
mns
(7)
to (
12)
show
rat
es f
or
var
iable
and f
ixed
rat
e m
ort
gag
es
wit
h d
iffe
rent
mat
uri
ties
. P
ost
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on, sc
aled
by tota
l as
sets
. B
ord
er is
a dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is loca
ted
in a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
Mort
g.
Dem
and i
s eq
ual
to G
oog
le s
earc
h v
olu
mes
for
the
topic
“M
ort
gag
e”.
I.S
DR
an
d I
.DE
P a
re b
anks’
sig
ht
dep
osi
t ra
tes
and d
eposi
t
rati
os
in D
ecem
ber
2014. R
obust
nes
s ch
ecks
wit
hout
fixed
eff
ects
and w
ithout
sea
rch v
olu
mes
are
avai
lable
in t
he
Onli
ne
Appen
dix
.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
(13
)(1
4)
Sig
ht
Dep
.
Rat
e
(SD
R)
Sig
ht D
ep.
Rat
e
(SD
R)
Sig
ht D
ep.
Rat
e
(SD
R)
Sig
ht
Dep
.
Rat
e
(SD
R)
Med
ium
Ter
m N
ote
(8y)
Med
ium
Ter
m N
ote
(8y
)
LIB
OR
C3
F3
LIB
OR
C3 F
3
Fix
ed r
ate
mo
rtg
age
(5y
)
Fix
ed r
ate
mort
gag
e
(5y
)
Fix
ed r
ate
mo
rtg
age
(10
y)
Fix
ed r
ate
mort
gag
e
(10
y)
Fix
ed r
ate
mo
rtg
age
(15
y)
Fix
ed r
ate
mort
gag
e
(15y)
Po
st ×
ER
0.0
3**
*0
.03*
**
0.0
2*
-0.0
30
.08*
**
0.1
0**
*0
.00
-0.0
10.0
4*
**
0.0
5**
*0.0
7*
**
0.0
8**
*0.0
6*
**
0.1
0**
*
(0.0
0)
(0.0
1)
(0.0
1)
(0.0
5)
(0.0
1)
(0.0
1)
(0.0
0)
(0.0
1)
(0.0
0)
(0.0
0)
(0.0
1)
(0.0
0)
(0.0
1)
(0.0
0)
Po
st ×
ER
× I
.SD
R0
.00
(0.0
3)
Po
st ×
I.S
DR
0.3
0
(0.2
7)
Po
st ×
ER
× I
.DE
P
0.0
0*
(0
.00)
Po
st ×
I.D
EP
0
.00
(0
.00)
Po
st ×
ER
× B
ord
er
-0.0
3*
**
-0.0
2-0
.05
**
-0.0
8*
**
-0
.00
-0
.05
**
*
-0.0
8*
**
-0
.04*
*
(0
.01)
(0.0
2)
(0.0
2)
(0.0
1)
(0
.01)
(0
.00)
(0
.01
)
(0.0
1)
Po
st ×
Bo
rder
-0
.28
**
*-0
.18
-0.4
2*
**
-0.5
1*
**
-0
.15
-0
.35
**
*
-0.5
4*
**
-0
.01
(0
.07)
(0.1
1)
(0.1
5)
(0.0
8)
(0
.12)
(0
.03)
(0
.03
)
(0.0
3)
Mort
g.
Dem
and
0
.00
0.0
00
.00
0.0
0
-0.0
0*
0
.00
0
.00
0.0
1
(0
.00)
(0.0
0)
(0.0
0)
(0.0
0)
(0
.00)
(0
.00)
(0
.00
)
(0.0
0)
Ob
s.1
,360
1,3
60
1,3
32
1,3
60
1,2
53
1,2
53
51
251
21
,28
01,2
80
1,1
90
1,1
90
17
11
71
R2
0.3
90
.50
0.5
20
.51
0.7
00
.82
0.0
20
.12
0.3
60
.48
0.4
10
.52
0.3
50.3
8
Nr.
of
ban
ks
41
41
37
41
40
40
19
19
39
39
36
36
66
Ban
k F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
YQ
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Sta
ndar
d e
rrors
clu
ster
ed b
y b
ank
. *
** p
<0.0
1,
**
p<
0.0
5, *
p<
0.1
43
Tab
le 6
. F
un
din
g M
ix
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e ex
pre
ssed
in
per
centa
ges
of
tota
l as
sets
, i.
e. a
s Y
t/T
At,
or
in y
ear-
on-y
ear
gro
wth
rat
es,
i.e.
as
(Yt -
Yt-
1)/
Yt-
1.
Post
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on,
scal
ed b
y t
ota
l as
sets
.
Bord
er i
s a
dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
Mort
g.
Dem
and i
s eq
ual
to G
oogle
sea
rch v
olu
mes
for
the
topic
“M
ort
gag
e”. R
obust
nes
s ch
ecks
wit
hout
fixed
eff
ects
and w
ithout
sea
rch v
olu
mes
are
avai
lable
in t
he
Onli
ne
Appen
dix
.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
(13
)(1
4)
Dep
osi
t
Fun
din
g/
TA
Dep
osi
t
Fu
nd
ing
/
TA
Dep
osi
t
Fu
ndin
g
(gro
wth
)
Med
ium
Ter
m
No
tes/
TA
Med
ium
Ter
m
Note
s/ T
A
Med
ium
Ter
m
No
tes/
TA
Med
ium
Ter
m N
.
(gro
wth
)
Bo
nd
Fu
nd
ing/
TA
Bon
d
Fu
nd
ing
/
TA
Bo
nd
Fun
din
g/
TA
Bon
d
Fu
nd
ing
(gro
wth
)
CE
T1/
TA
CE
T1
/ T
AC
ET
1
(gro
wth
)
(A)
Po
st ×
ER
0.2
2**
*0
.17
*0
.03
0.0
3*
0.0
8*
**
0.0
8**
*-0
.61
**
-0.1
0*
*-0
.14
**
*-0
.20*
**
-0.1
10.0
1*
0.0
2*
**
0.0
1
(0.0
6)
(0.0
9)
(0.0
9)
(0.0
2)
(0.0
2)
(0.0
2)
(0.2
7)
(0.0
4)
(0.0
3)
(0.0
4)
(0.5
6)
(0.0
1)
(0.0
1)
(0.1
0)
(B)
Post
× E
R ×
WM
-0.0
3*
0.0
8*
(0.0
2)
(0.0
4)
(C)
Post
× W
M0
.37
**
*0
.11
(0.1
2)
(0.4
6)
Po
st ×
ER
× B
ord
er0
.08
-0.0
6-0
.05
*1
.08
*0.1
2*
*-0
.74
0.0
3*
0.0
6
(0.1
3)
(0.1
6)
(0.0
3)
(0.5
9)
(0.0
6)
(0.5
9)
(0.0
2)
(0.1
7)
Po
st ×
Bo
rder
0.1
1-2
.11
**
-0.3
9*
**
3.8
80
.22
-0.9
90.4
4*
**
1.5
1
(0.5
8)
(0.9
5)
(0.1
4)
(2.4
0)
(0.2
9)
(1.2
7)
(0.1
3)
(1.2
4)
Mort
g.
Dem
and
-0.0
00
.01
0.0
0**
0.0
2-0
.01
0.0
0-0
.00*
*-0
.00
(0.0
0)
(0.0
1)
(0.0
0)
(0.0
5)
(0.0
0)
(0.0
3)
(0.0
0)
(0.0
1)
(A)
+ (
B)
--
-0
.000
--
--0
.01
2-
--
--
-
Pv
al-
--
0.0
00
--
-0.0
10
--
--
--
Ban
ks
Co
m.
Co
m.
Co
m.
All
Com
.C
om
.C
om
.A
llC
om
.C
om
.C
om
.C
om
.C
om
.C
om
.
Ob
s.1
,800
1,8
00
1,8
00
3,3
96
1,8
00
1,8
00
1,7
28
3,4
56
1,8
00
1,8
00
1,7
29
600
60
06
00
R2
0.1
50
.15
0.0
50
.32
0.2
30
.27
0.0
20
.15
0.1
90
.21
0.0
50
.02
0.1
40
.01
Nr.
of
ban
ks
50
50
50
96
50
50
48
96
50
50
49
50
50
50
Ban
k F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
YQ
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Sta
ndar
d e
rrors
clu
ster
ed b
y b
ank
. *
**
p<
0.0
1, **
p<
0.0
5, *
p<
0.1
44
Tab
le 7
. F
inan
cial
Sta
bil
ity
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e dif
fere
nt
regula
tory
and s
uper
vis
ory
mea
sure
s of
finan
cial
sta
bil
ity
. P
ost
is
a dum
my v
aria
ble
that
is
equal
to o
ne
fro
m J
anuar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuo
us
trea
tmen
t var
iable
is
equal
to
expose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on,
scal
ed b
y t
ota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to
one
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
Mort
g.
Dem
and i
s eq
ual
to G
oogle
sea
rch v
olu
mes
for
the
topic
“M
ort
gag
e”.
Robust
nes
s ch
ecks
wit
hout
fixed
eff
ects
and w
ithout
sea
rch v
olu
mes
are
avai
lable
in t
he
Onli
ne
Appen
dix
.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11)
(12
)(1
3)
(14
)(1
5)
RW
A/T
AR
WA
/TA
RW
A/T
A
Val
ue
Ad
just
men
ts
Val
ue
Adju
stm
en
ts
Mar
ket
Ris
k S
har
e
of
Req
.
Eq
uit
y
Mar
ket
Ris
k S
har
e
of
Req
.
Equ
ity
IRR
(2
y)
IRR
(2y
)IR
R
(Ban
k)
IRR
(Ban
k)
Reg
.
Cap
ital
Cu
shio
n
Reg
.
Cap
ital
Cush
ion
LC
RL
CR
(A)
Po
st ×
ER
0.3
8**
0.3
6**
*0
.28
0.0
2*
*0.0
4*
**
0.0
2*
*0.0
2**
*0
.18
***
0.2
1**
*0.1
0*
**
0.1
4**
*-0
.07
**
*-0
.05*
*-0
.01
-0.0
5*
**
(0.1
5)
(0.1
0)
(0.2
1)
(0.0
1)
(0.0
1)
(0.0
1)
(0.0
1)
(0.0
4)
(0.0
6)
(0.0
4)
(0.0
5)
(0.0
2)
(0.0
2)
(0.0
2)
(0.0
1)
(B)
Po
st ×
ER
× W
M-0
.31
*
(0.1
7)
(C)
Po
st ×
WM
35
.63
**
*
(0.8
4)
Post
× E
R ×
Bo
rder
0.0
3-0
.02
-0.0
3-0
.24
**
*-0
.15*
0.0
7*
*0
.09
(0.2
9)
(0.0
2)
(0.0
4)
(0.0
9)
(0.0
8)
(0.0
3)
(0.0
7)
Post
× B
ord
er-0
.89
0.1
2-0
.31
-1.9
1**
*-0
.94*
1.0
2**
*0
.38
(1.8
7)
(0.1
1)
(0.3
3)
(0.4
7)
(0.4
7)
(0.2
3)
(0.4
7)
Mo
rtg
. D
eman
d0
.03
-0.0
0-0
.00
0.0
10
.00
-0.0
1*
**
0.0
0
(0.0
5)
(0.0
0)
(0.0
0)
(0.0
1)
(0.0
1)
(0.0
0)
(0.0
0)
(A)
+ (
B)
0.0
75
--
--
--
--
--
--
--
Pval
0.0
84
--
--
--
--
--
--
--
Ban
ks
All
Co
m.
Com
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.
Obs.
1,1
12
60
06
00
600
60
06
00
60
06
00
60
06
00
60
06
00
600
1,0
87
1,0
87
R2
0.0
40.0
20
.02
0.0
30.0
50
.01
0.0
20
.15
0.2
60
.05
0.0
80
.10
0.2
60.0
10
.07
Nr.
of
ban
ks
96
50
50
50
50
50
50
50
50
50
50
50
50
50
50
Ban
k F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
YQ
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Sta
ndar
d e
rrors
clu
ster
ed b
y b
ank
. *
**
p<
0.0
1,
** p
<0.0
5,
* p
<0
.1
45
Table
8. P
rofi
tabil
ity
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e ex
pre
ssed
in
per
centa
ges
of
tota
l busi
nes
s volu
me
(in c
olu
mns
1 to 3
), in p
erce
nta
ges
of
tota
l as
sets
(in
colu
mns
4 to 7
), o
r in
bas
is p
oin
ts r
elat
ive
to tota
l busi
nes
s volu
me
(in c
olu
mns
8 to 1
1).
Post
is
a dum
my
var
iable
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n
tota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on
, sc
aled
by t
ota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
Mort
g.
Dem
and i
s eq
ual
to G
oogle
sea
rch v
olu
mes
for
the
topic
“M
ort
gag
e”.
Robust
nes
s ch
ecks
wit
hout
fixed
eff
ects
and w
ithout
sea
rch v
olu
mes
are
avai
lable
in t
he
Onli
ne
Appen
dix
.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11)
Pro
fit/
Bu
sin
ess
Volu
me
Pro
fit/
Busi
nes
s
Vo
lum
e
Pro
fit/
Bu
sin
ess
Volu
me
Net
In
t.
Inco
me/
TA
Net
Int.
Inco
me/
TA
Int. E
arned
(Lo
ans)
/
TA
Int.
Ear
ned
(Loan
s)/
TA
Net
Fee
Inco
me/
Busi
nes
s
Vo
lum
e
Net
Fee
Inco
me/
Busi
nes
s
Vo
lum
e
Fee
Inc.
(Lo
ans)
/
Bu
sin
ess
Vo
lum
e
Fee
In
c.
(Loan
s)/
Busi
nes
s
Vo
lum
e
(A)
Post
× E
R0
.01
**
0.0
2*
**
0.0
3**
*0
.01
***
0.0
1**
*0.0
3*
**
0.0
3**
*0.1
7*
**
0.1
2*
*0
.04
*0
.02
**
*
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
5)
(0.0
5)
(0.0
2)
(0.0
0)
(B)
Post
× E
R ×
WM
-0.0
0
(0.0
0)
(C)
Post
× W
M0.0
4
(0.0
5)
Po
st ×
ER
× B
ord
er
-0.0
2**
*
-0.0
0
-0.0
2**
*
0.0
3
0.0
5
(0
.00)
(0
.00)
(0
.00)
(0
.12
)
(0.0
4)
Po
st ×
Bord
er
-0.1
7**
*
-0.0
2
-0.1
3**
*
-0.5
5
0.0
8
(0
.02)
(0
.02)
(0
.03)
(0
.79
)
(0.1
4)
Mo
rtg
. D
eman
d
0.0
0
-0.0
0*
*
0.0
0
0.0
0
-0.0
0
(0
.00)
(0
.00)
(0
.00)
(0
.01
)
(0.0
0)
(A)
+ (
B)
0.0
03
--
--
--
--
--
Pv
al0
.38
0-
--
--
--
--
-
Ban
ks
All
Com
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.C
om
.
Ob
s.56
53
00
30
03
00
30
03
00
30
03
00
30
03
00
30
0
R2
0.3
50
.18
0.2
40
.40
0.4
10
.61
0.7
20
.13
0.1
40
.10
0.1
2
Nr.
of
ban
ks
96
50
50
50
50
50
50
50
50
50
50
Ban
k F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
YQ
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Sta
nd
ard
err
ors
clu
ster
ed b
y b
ank
. **
* p
<0
.01,
** p
<0.0
5,
* p
<0
.1
46
Table
9.
Inte
rest
Rat
es
The
sam
ple
cover
s up t
o 5
0 d
om
esti
call
y o
wned
com
mer
cial
ban
ks
over
the
per
iods
Feb
ruar
y 2
010 t
o J
anuar
y 2
013 (
colu
mns
1 t
o 6
) an
d J
uly
2013 t
o J
une
2016 (
colu
mns
7 t
o 1
2).
The
dep
enden
t
var
iable
s ar
e eq
ual
to i
nte
rest
rat
es t
hat
ban
ks
are
requir
ed t
o r
eport
to t
he
SN
B (
actu
al l
endin
g r
ates
may
var
y w
ith c
ust
om
er c
har
acte
rist
ics)
. C
olu
mns
(1),
(2),
(7)
and (
8)
show
borr
ow
ing r
ates
and c
olu
mns
(3)
to (
6)
and (
9)
to (
12)
show
rat
es f
or
var
iable
and f
ixed
rat
e m
ort
gag
es w
ith d
iffe
rent
mat
uri
ties
. P
ost
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero
bef
ore
. T
he
conti
nuous
trea
tmen
t var
iable
s ar
e eq
ual
to t
he
pre
-tre
atm
ent
val
ues
of
SN
B r
eser
ves
+ n
et i
nte
rban
k m
arket
posi
tion (
NIB
), n
et o
f th
e m
inim
um
res
erve
requir
emen
t, s
cale
d b
y t
ota
l
asse
ts (
for
the
posi
tive
rate
per
iod, w
ithout
exem
pti
on),
and o
f E
R +
NIB
, net
of
the
SN
B e
xem
pti
on , s
cale
d b
y t
ota
l as
sets
(fo
r th
e neg
ativ
e ra
te p
erio
d).
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
Sig
ht D
ep.
Rat
e
(SD
R)
Med
ium
Ter
m N
ote
(8y)
LIB
OR
C3 F
3
Fix
ed r
ate
mort
gag
e
(5y)
Fix
ed r
ate
mo
rtg
age
(10y
)
Fix
ed r
ate
mort
gag
e
(15
y)
Sig
ht D
ep.
Rat
e
(SD
R)
Med
ium
Ter
m N
ote
(8y)
LIB
OR
C3 F
3
Fix
ed r
ate
mort
gag
e
(5y)
Fix
ed r
ate
mort
gag
e
(10y
)
Fix
ed r
ate
mo
rtg
age
(15
y)
Po
st ×
T-0
.00
-0.3
6*
**
-0.0
1-0
.41**
*-0
.51*
**
-0.6
1*
*0
.03*
**
0.0
8*
**
0.0
00
.04
**
*0
.07*
**
0.0
6*
**
(0.0
4)
(0.0
9)
(0.0
8)
(0.0
8)
(0.1
1)
(0.2
2)
(0.0
0)
(0.0
1)
(0.0
0)
(0.0
0)
(0.0
1)
(0.0
1)
TE
xce
ssR
+ N
IB
Exce
ssR
+ N
IB
Ex
cess
R
+ N
IB
Exce
ssR
+ N
IB
Ex
cess
R
+ N
IB
Exce
ssR
+ N
IBE
R +
NIB
ER
+ N
IBE
R +
NIB
ER
+ N
IBE
R +
NIB
ER
+ N
IB
Per
iod
Au
g 2
01
1A
ug 2
011
Au
g 2
01
1A
ug 2
011
Au
g 2
01
1A
ug 2
011
Jan
20
15
Jan 2
01
5Ja
n 2
015
Jan
201
5Ja
n 2
015
Jan
201
5
Ob
s.1
,34
91
,34
54
16
1,3
49
1,2
21
18
81
,39
61,2
89
538
1,3
16
1,2
26
17
1
R2
0.0
00.3
10
.00
0.3
80
.38
0.3
10
.42
0.7
00
.01
0.3
60
.40
0.3
5
Nr.
of
ban
ks
38
38
14
38
37
642
41
20
40
37
6
Ban
k F
EY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
esY
es
YQ
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Sta
nd
ard
err
ors
clu
ster
ed b
y b
ank
. **
* p
<0
.01
, *
* p
<0
.05,
* p
<0
.1
47
Table
3A
(R
ob
ust
nes
s).
Cen
tral
Ban
k R
eser
ves
& L
iquid
Ass
ets
The
sam
ple
cover
s 50 d
om
esti
call
y-o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e ex
pre
ssed
in
per
centa
ges
of
tota
l as
sets
, i.
e. a
s Y
t/T
At,
or
in y
ear-
on-y
ear
gro
wth
rat
es, i.
e. a
s (Y
t - Y
t-1)/
Yt-
1. In
colu
mns
(7),
(8),
(13),
and (
14),
the
dep
enden
t var
iable
is
the
fore
ign c
urr
ency
shar
e of
inte
rban
k
loan
s or
liquid
ass
ets,
i.e
. eq
ual
to Y
FX
t/(Y
FX
t +
YC
HF
t).
Post
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to
expose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on,
scal
ed b
y t
ota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to
one
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
(13
)(1
4)
(15
)(1
6)
SN
B
Res
erv
es/
TA
SN
B
Res
erv
es/
TA
SN
B
Res
erv
es
(gro
wth
)
SN
B
Res
erves
(gro
wth
)
Net
Inte
rban
k
Po
siti
on
/
TA
Net
Inte
rban
k
Posi
tio
n/
TA
FX
shar
e
of
Inte
rban
k
Loan
s
FX
sh
are
of
Inte
rban
k
Lo
ans
Net
Inte
rban
k
Posi
tion
(gro
wth
)
Net
Inte
rban
k
Posi
tio
n
(gro
wth
)
Liq
uid
Ass
ets/
TA
Liq
uid
Ass
ets/
TA
FX
sh
are
of
Liq
uid
Ass
ets
FX
sh
are
of
Liq
uid
Ass
ets
Liq
uid
Ass
ets
(gro
wth
)
Liq
uid
Ass
ets
(gro
wth
)
Po
st ×
ER
-0.5
4*
**
-0.5
1*
**
-9.1
0*
**
-1.8
50
.24*
**
0.1
9*
*-0
.17
-0.5
36
.76
1.3
3-0
.53
**
*-0
.51
***
0.4
9*
**
0.2
6*
**
-7.7
3*
**
-5.5
6*
*
(0.0
7)
(0.0
5)
(1.4
5)
(4.4
5)
(0.0
7)
(0.0
8)
(0.3
5)
(0.6
3)
(24
.92
)(8
.79
)(0
.07)
(0.0
5)
(0.0
6)
(0.0
7)
(1.3
6)
(2.5
3)
Po
st0
.08
-4
8.8
8*
**
-0.1
5
0.4
2
12
0.0
00
.06
-0
.11
-2
3.0
9*
(0.4
0)
(1
0.3
5)
(0.4
7)
(2
.48
)
(96
.17
)(0
.40)
(0
.21
)
(12.7
7)
ER
0.7
7**
*
-1.5
0-0
.03
-1
.33
**
*
1.9
00
.74
**
*
-0.5
4*
**
0
.30
(0.1
0)
(1
.14
)(0
.11)
(0
.25
)
(5.5
8)
(0.1
0)
(0
.05
)
(0.9
0)
Po
st ×
ER
× B
ord
er-0
.02
-7
.18
0.0
60.3
2
0.2
1-0
.01
0.2
7
-1.7
6
(0.0
9)
(5
.80)
(0.1
0)
(0.7
4)
(1
1.2
6)
(0.0
9)
(0.2
3)
(3
.52
)
Po
st ×
Bord
er0
.29
-4
6.8
1-0
.39
-1.6
7
23
.06
0.2
4-1
.10
-2
5.0
2
(0.3
8)
(3
0.6
8)
(0.4
7)
(2.8
8)
(3
3.0
3)
(0.3
9)
(0.7
3)
(2
0.6
3)
Obs.
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,7
64
1,7
64
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
R2
0.4
90
.57
0.0
30.0
10
.05
0.2
70
.05
0.0
10
.00
0.0
00
.47
0.5
60
.13
0.1
80
.02
0.0
2
Nr.
of
ban
ks
50
50
50
50
50
50
50
50
49
49
50
50
50
50
50
50
Ban
k F
EN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
es
Tim
e F
EN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
es
Sta
nd
ard
err
ors
clu
ster
ed b
y b
ank
. **
* p
<0
.01,
**
p<
0.0
5, *
p<
0.1
ONLINE APPENDIX (Additional Robustness Checks)
48
Table
4A
(R
obust
nes
s). L
oan
s an
d I
nv
estm
ents
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
201
6 (
36 m
onth
s). In
colu
mns
(1)
to (
9),
and (
12)
and (
13),
the
dep
enden
t var
iable
s ar
e ex
pre
ssed
in p
erce
nta
ges
of
tota
l as
sets
, i.
e. a
s Y
t/T
At,
or
in y
ear-
on-y
ear
gro
wth
rat
es,
i.e.
as
(Yt -
Yt-
1)/
Yt-
1.
In c
olu
mns
(10)
and (
11)
they
are
equal
to t
he
dif
fere
nce
bet
wee
n a
sset
s an
d l
iabil
itie
s in
non-C
HF
curr
enci
es,
scal
ed b
y t
ota
l as
sets
. P
ost
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd
equal
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t
var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent dif
fere
nce
bet
wee
n tota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on, sc
aled
by tota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
(13
)
Lo
ans/
TA
Lo
ans/
TA
Lo
ans
(gro
wth
)
Mo
rtg
ages
/ T
A
Mo
rtg
ages
/ T
A
Mo
rtg
ages
(gro
wth
)
Fin
anci
al
Ass
ets/
TA
Fin
anci
al
Ass
ets/
TA
Fin
anci
al
Ass
ets
(gro
wth
)
FX
(Ass
sets
-
Lia
bil
itie
s)
/ T
A
FX
(Ass
sets
-
Lia
bil
itie
s)
/ T
A
To
tal
Ass
ets
(gro
wth
)
To
tal
Ass
ets
(gro
wth
)
Po
st ×
ER
0.0
9*
**
0.1
2*
**
0.3
90
.13
**
0.1
7*
**
0.1
3*
*0
.03
0.0
5*
0.3
30
.08
**
0.0
5*
*-0
.39
**
*-0
.17
*
(0.0
3)
(0.0
3)
(0.4
0)
(0.0
7)
(0.0
5)
(0.0
6)
(0.0
3)
(0.0
2)
(0.4
1)
(0.0
4)
(0.0
2)
(0.0
9)
(0.0
9)
Po
st-0
.42
*-0
.28
-0.3
4*
*-0
.36
-1
.33
**
(0.2
3)
(0.3
4)
(0.1
5)
(0.3
0)
(0.5
2)
ER
-0.2
3-0
.88
**
0.0
1-0
.06
**
*0
.03
(0.1
4)
(0.4
0)
(0.1
5)
(0.0
2)
(0.1
1)
Po
st ×
ER
× B
ord
er-0
.03
-0.3
1-0
.06
-0.1
8-0
.01
1.3
40
.04
-0.2
2
(0.0
5)
(0.6
8)
(0.0
9)
(0.1
1)
(0.0
4)
(1.2
8)
(0.0
9)
(0.1
4)
Po
st ×
Bo
rder
-0.5
8*
**
-13
.22
*-0
.46
-1.1
0-0
.39
**
18
.33
*-0
.64
-1.1
9*
*
(0.2
1)
(6.7
5)
(0.3
2)
(0.7
1)
(0.1
6)
(10
.43
)(0
.45
)(0
.55
)
Ob
s.1
,80
01
,80
01
,80
01
,80
01
,80
01
,80
01
,80
01
,80
01
,80
01
,80
01
,80
01
,80
01
,80
0
R2
0.0
50
.33
0.0
40
.13
0.1
40
.05
0.0
10
.14
0.0
60
.02
0.1
50
.07
0.0
6
Nr.
of
ban
ks
50
50
50
50
50
50
50
50
50
50
50
50
50
Ban
k F
EN
oY
esY
esN
oY
esY
esN
oY
esY
esY
esY
esY
esY
es
YQ
FE
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Sta
nd
ard
err
ors
clu
ster
ed b
y b
ank
. *
**
p<
0.0
1,
**
p<
0.0
5,
* p
<0
.1
49
Table
5A
(R
obust
nes
s).
Inte
rest
Rat
es
The
sam
ple
cover
s up t
o 5
0 d
om
esti
call
y o
wned
com
mer
cial
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s). T
he
dep
enden
t var
iable
s ar
e eq
ual
to i
nte
rest
rat
es t
hat
ban
ks
are
requir
ed
to r
eport
to
the
SN
B (
actu
al len
din
g r
ates
may
var
y w
ith c
ust
om
er c
har
acte
rist
ics)
. C
olu
mns
(1)
to (
8)
show
borr
ow
ing r
ates
and c
olu
mns
(9)
to (
16)
show
rat
es f
or
var
iable
and f
ixed
rat
e m
ort
gag
es
wit
h d
iffe
rent
mat
uri
ties
. P
ost
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on, sc
aled
by tota
l as
sets
. B
ord
er is
a dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is loca
ted
in a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
(13
)(1
4)
(15
)(1
6)
Dem
and
Dep
. R
ate
Dem
and
Dep
. R
ate
Dem
and
Dep
. R
ate
Dem
and
Dep
. R
ate
Sig
ht D
ep.
Rat
e
(SD
R)
Sig
ht D
ep.
Rat
e
(SD
R)
Med
ium
Ter
m N
ote
(8y)
Med
ium
Ter
m N
ote
(8y
)
LIB
OR
C3
F3
LIB
OR
C3
F3
Fix
ed r
ate
mo
rtg
age
(5y
)
Fix
ed r
ate
mo
rtg
age
(5y
)
Fix
ed r
ate
mort
gag
e
(10y
)
Fix
ed r
ate
mo
rtgag
e
(10
y)
Fix
ed r
ate
mo
rtg
age
(15y
)
Fix
ed r
ate
mort
gag
e
(15
y)
Po
st ×
ER
0.0
0-0
.02*
**
-0.0
1*
*-0
.02
**
*0
.00
0.0
3*
**
0.0
1**
*0.1
0*
**
-0.0
1**
-0.0
1-0
.00
0.0
5*
**
0.0
00.0
8*
**
0.0
5*
**
0.0
9*
**
(0.0
0)
(0.0
1)
(0.0
0)
(0.0
1)
(0.0
0)
(0.0
1)
(0.0
0)
(0.0
1)
(0.0
1)
(0.0
1)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
1)
(0.0
0)
Po
st0
.10
**
*-0
.24
**
*-0
.55
**
*-0
.16*
**
-0.3
5*
**
-0.5
5*
**
-0.1
5*
(0.0
2)
(0.0
2)
(0.0
2)
(0.0
4)
(0.0
2)
(0.0
3)
(0.0
9)
ER
-0.0
0-0
.01
**
*-0
.01
**
*0
.01*
**
-0.0
0-0
.01
*-0
.00
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
1)
Po
st ×
ER
× B
ord
er
0.0
20.0
2
-0.0
3*
**
-0
.08
**
*
-0.0
0
-0.0
5*
**
-0
.08
**
*
-0.0
4*
*
(0
.01)
(0.0
1)
(0
.01
)
(0.0
1)
(0
.01
)
(0.0
0)
(0
.01)
(0
.01
)
Po
st ×
Bord
er
0.0
90.0
9
-0.2
7*
**
-0
.51
**
*
-0.1
5
-0.3
5*
**
-0
.54
**
*
0.0
1
(0
.09)
(0.0
9)
(0
.07
)
(0.0
8)
(0
.12
)
(0.0
3)
(0
.03)
(0
.04
)
Mo
rtg
. D
eman
d-0
.00
(0.0
0)
Obs.
1,3
60
1,3
60
1,3
60
1,3
60
1,3
60
1,3
60
1,2
53
1,2
53
512
51
21
,280
1,2
80
1,1
90
1,1
90
171
17
1
R2
0.1
00
.16
0.1
10.1
60
.36
0.5
00
.73
0.8
20
.06
0.1
00
.42
0.4
70
.50
0.5
20
.44
0.3
7
Nr.
of
ban
ks
41
41
41
41
41
41
40
40
19
19
39
39
36
36
66
Ban
k F
EN
oY
esY
esY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
es
YQ
FE
No
Yes
Yes
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Sta
nd
ard
err
ors
clu
ster
ed b
y b
ank
. **
* p
<0
.01,
**
p<
0.0
5, *
p<
0.1
50
Table
6A
(R
obust
nes
s).
Fu
nd
ing M
ix
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e ex
pre
ssed
in
per
centa
ges
of
tota
l as
sets
, i.
e. a
s Y
t/T
At,
or
in y
ear-
on-y
ear
gro
wth
rat
es,
i.e.
as
(Yt -
Yt-
1)/
Yt-
1.
Post
is
a dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on,
scal
ed b
y t
ota
l as
sets
.
Bord
er i
s a
dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11
)(1
2)
Dep
osi
t
Fu
ndin
g/
TA
Dep
osi
t
Fund
ing
/
TA
Dep
osi
t
Fund
ing
(gro
wth
)
Med
ium
Ter
m
Note
s/ T
A
Med
ium
Ter
m
Note
s/ T
A
Med
ium
Ter
m N
.
(gro
wth
)
Bo
nd
Fu
ndin
g/
TA
Bond
Fun
din
g/
TA
Bo
nd
Fund
ing
(gro
wth
)
CE
T1/
TA
CE
T1/ T
AC
ET
1
(gro
wth
)
Po
st ×
ER
0.2
5*
**
0.1
7*
0.0
30
.03
*0
.08**
*-0
.62
**
-0.1
0**
-0.2
0**
*-0
.11
0.0
6*
**
0.0
2**
*0.0
1
(0.0
9)
(0.0
9)
(0.0
9)
(0.0
2)
(0.0
2)
(0.2
7)
(0.0
4)
(0.0
4)
(0.5
6)
(0.0
1)
(0.0
1)
(0.1
0)
Po
st0
.26
-0.3
9*
**
0.3
60.3
8*
**
(0.5
5)
(0.1
2)
(0.2
7)
(0.1
1)
ER
0.0
80
.12
-0.4
7**
-0.0
8
(0.4
5)
(0.1
9)
(0.1
9)
(0.0
5)
Po
st ×
ER
× B
ord
er0.0
8-0
.06
-0.0
5*
1.0
8*
0.1
1*
-0.7
30
.03*
0.0
7
(0.1
3)
(0.1
6)
(0.0
3)
(0.5
9)
(0.0
6)
(0.5
9)
(0.0
2)
(0.1
7)
Po
st ×
Bord
er0.1
0-2
.10
**
-0.3
9*
**
3.9
10
.21
-0.9
90.4
4**
*1.5
1
(0.5
8)
(0.9
5)
(0.1
4)
(2.3
9)
(0.2
9)
(1.2
7)
(0.1
3)
(1.2
4)
Obs.
1,8
00
1,8
00
1,8
00
1,8
00
1,8
00
1,7
28
1,8
00
1,8
00
1,7
29
600
600
60
0
R2
0.0
20.1
50
.05
0.0
30
.27
0.0
20
.16
0.2
10.0
50.0
20
.14
0.0
1
Nr.
of
ban
ks
50
50
50
50
50
48
50
50
49
50
50
50
Ban
k F
EN
oY
esY
esN
oY
esY
esN
oY
esY
esN
oY
esY
es
YQ
FE
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Sta
ndar
d e
rrors
clu
ster
ed b
y b
ank
. **
* p
<0
.01,
**
p<
0.0
5, *
p<
0.1
51
Table
7A
(R
ob
ust
nes
s).
Fin
anci
al S
tab
ilit
y
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e dif
fere
nt
regula
tory
and s
uper
vis
ory
mea
sure
s of
finan
cial
sta
bil
ity
. P
ost
is
a dum
my v
aria
ble
that
is
equal
to o
ne
fro
m J
anuar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuo
us
trea
tmen
t var
iable
is
equal
to
expose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on,
scal
ed b
y t
ota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to
one
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er w
ith a
fore
ign c
ountr
y.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11)
(12
)(1
3)
(14
)
RW
A/T
AR
WA
/TA
Val
ue
Ad
just
men
ts
Val
ue
Ad
just
men
ts
Mar
ket
Ris
k S
har
e
of
Req
.
Eq
uit
y
Mar
ket
Ris
k S
har
e
of
Req
.
Eq
uit
y
IRR
(2y
)IR
R (
2y)
IRR
(Ban
k)
IRR
(Ban
k)
Reg
.
Cap
ital
Cu
shio
n
Reg
.
Cap
ital
Cu
shio
n
LC
RL
CR
Po
st ×
ER
0.2
30
.29
0.0
20
.04
**
*-0
.01
0.0
2**
*-0
.04
0.2
2*
**
-0.0
00
.14*
**
0.0
3-0
.05
**
0.0
4-0
.05
**
*
(0.2
8)
(0.2
0)
(0.0
2)
(0.0
1)
(0.0
8)
(0.0
0)
(0.1
1)
(0.0
6)
(0.0
8)
(0.0
5)
(0.0
5)
(0.0
2)
(0.0
6)
(0.0
1)
Po
st-1
.16
0.0
5-0
.29
-1.9
1**
-0.9
20
.87**
0.4
4
(1.8
4)
(0.1
4)
(0.6
8)
(0.8
1)
(0.5
7)
(0.3
6)
(0.4
5)
ER
0.0
2-0
.01
0.2
5*
**
0.1
3*
-0.0
2-0
.09*
*0
.12
**
*
(0.1
7)
(0.0
1)
(0.0
6)
(0.0
7)
(0.0
6)
(0.0
4)
(0.0
4)
Po
st ×
ER
× B
ord
er0
.01
-0.0
2-0
.03
-0.2
4*
**
-0.1
6*
0.0
8**
0.0
9
(0.2
9)
(0.0
2)
(0.0
4)
(0.0
9)
(0.0
8)
(0.0
3)
(0.0
7)
Po
st ×
Bo
rder
-0.8
90
.12
-0.3
1-1
.91*
**
-0.9
4*
1.0
2*
**
0.3
8
(1.8
7)
(0.1
1)
(0.3
3)
(0.4
7)
(0.4
7)
(0.2
3)
(0.4
7)
Ob
s.6
00
60
06
00
60
06
00
60
060
06
00
60
06
00
60
06
00
1,0
87
1,0
87
R2
0.0
10
.02
0.0
00
.05
0.1
30
.02
0.0
50
.26
0.0
10
.08
0.0
30
.25
0.3
00.0
7
Nr.
of
ban
ks
50
50
50
50
50
50
50
50
50
50
50
50
50
50
Ban
k F
EN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
esN
oY
es
YQ
FE
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Sta
ndar
d e
rrors
clu
ster
ed b
y b
ank
. *
**
p<
0.0
1, *
* p
<0
.05
, *
p<
0.1
52
Table
8A
(R
obu
stn
ess)
. P
rofi
tabil
ity
The
sam
ple
cover
s 50 d
om
esti
call
y o
wned
com
mer
cial
and 4
6 w
ealt
h m
anag
emen
t (W
M)
ban
ks
over
the
per
iod J
uly
2013 t
o J
une
2016 (
36 m
onth
s).
The
dep
enden
t var
iable
s ar
e ex
pre
ssed
in
per
centa
ges
of
tota
l busi
nes
s volu
me
(in c
olu
mns
1 a
nd 2
), i
n p
erce
nta
ges
of
tota
l as
sets
(in
colu
mns
3 t
o 6
), o
r in
bas
is p
oin
ts r
elat
ive
to t
ota
l busi
nes
s volu
me
(in c
olu
mns
7 t
o 1
0).
Post
is
a
dum
my v
aria
ble
that
is
equal
to o
ne
from
Jan
uar
y 2
015 a
nd e
qual
to z
ero b
efore
. T
he
conti
nuous
trea
tmen
t var
iable
is
equal
to e
xpose
d r
eser
ves
(E
R),
i.e
. to
the
aver
age
pre
-tre
atm
ent
dif
fere
nce
bet
wee
n t
ota
l S
NB
res
erves
and t
he
regula
tory
exem
pti
on, sc
aled
by t
ota
l as
sets
. B
ord
er i
s a
dum
my v
aria
ble
that
is
equal
to o
ne
if a
ban
k’s
hea
dquar
ter
is l
oca
ted i
n a
can
ton t
hat
shar
es a
bord
er
wit
h a
fore
ign c
ountr
y.
(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
Pro
fit/
Bu
sin
ess
Vo
lum
e
Pro
fit/
Bu
sines
s
Vo
lum
e
Net
In
t.
Inco
me/
TA
Net
In
t.
Inco
me/
TA
Int.
Ear
ned
(Loan
s)/
TA
Int.
Ear
ned
(Lo
ans)
/
TA
Net
Fee
Inco
me/
Bu
sin
ess
Vo
lum
e
Net
Fee
Inco
me/
Bu
sin
ess
Volu
me
Fee
Inc.
(Loan
s)/
Bu
sin
ess
Vo
lum
e
Fee
In
c.
(Lo
ans)
/
Bu
sin
ess
Volu
me
Po
st ×
ER
0.0
1**
0.0
3**
*0.0
1*
**
0.0
1**
*0.0
1*
**
0.0
3**
*0
.14
0.1
2*
*0
.06
0.0
2**
*
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.0
0)
(0.1
1)
(0.0
5)
(0.0
4)
(0.0
0)
Po
st-0
.17
**
*-0
.02
-0.1
4**
*-0
.29
0.1
3
(0.0
2)
(0.0
2)
(0.0
3)
(0.7
5)
(0.1
6)
ER
-0.0
1**
*-0
.01
-0.0
1-0
.71
**
-0.0
4
(0.0
0)
(0.0
1)
(0.0
1)
(0.2
9)
(0.1
3)
Po
st ×
ER
× B
ord
er-0
.02*
**
-0.0
0-0
.02*
**
0.0
30.0
5
(0.0
0)
(0.0
0)
(0.0
0)
(0.1
2)
(0.0
4)
Po
st ×
Bo
rder
-0.1
7*
**
-0.0
2-0
.13*
**
-0.5
50.0
8
(0.0
2)
(0.0
2)
(0.0
3)
(0.7
9)
(0.1
4)
Ob
s.30
03
00
30
03
00
30
03
00
30
03
00
30
03
00
R2
0.2
10
.23
0.0
40
.41
0.1
70
.72
0.1
30.1
40
.00
0.1
2
Nr.
of
ban
ks
50
50
50
50
50
50
50
50
50
50
Ban
k F
EN
oY
esN
oY
esN
oY
esN
oY
esN
oY
es
YQ
FE
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Sta
nd
ard
err
ors
clu
ster
ed b
y b
ank.
**
* p
<0
.01
, *
* p
<0.0
5,
* p
<0
.1