The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1743-9132.htm
Monetary policy, bank leverage
and liquidity
Monetary
policy, bank
leverage and
liquidity
Van Dan Dang
Department of Finance, Banking University of Ho Chi Minh City,
Ho Chi Minh City, Vietnam, and
Khac Quoc Bao Nguyen
School of Finance, University of Economics Ho Chi Minh City,
Ho Chi Minh City, Vietnam
619
Received 10 June 2020
Revised 14 July 2020
22 August 2020
Accepted 3 September 2020
Abstract
Purpose – The study explores how banks design their financial structure and asset portfolio in response to
monetary policy changes.
Design/methodology/approach – The authors conduct the research design for the Vietnamese banking
market during 2007–2018. To ensure robust findings, the authors employ two econometric models of static and
dynamic panels, multiple monetary policy indicators and alternative measures of bank leverage and liquidity.
Findings – Banks respond to monetary expansion by raising their financial leverage on the liability side and
cutting their liquidity positions on the asset side. Further analysis suggests that larger banks’ financial
leverage is more responsive to monetary policy changes, while smaller banks strengthen the potency of
monetary policy transmission toward bank liquidity. Additionally, the authors document that lower interest
rates induce a beneficial effect on the net stable funding ratio (NSFR) under Basel III guidelines, implying that
banks appear to modify the composition of liabilities to improve the stability of funding sources.
Originality/value – The study is the first attempt to simultaneously examine the impacts of monetary policy
on both sides of bank balance sheets, across various banks of different sizes under a multiple-tool monetary
regime. Besides, understanding how banks organize their stable funding sources and illiquid assets amid
monetary shocks is an innovation of this study.
Keywords Bank leverage, Bank liquidity, Monetary policy, Net stable funding ratio
Paper type Research paper
1. Introduction
Fundamentally, monetary policy is implemented to stabilize the price level and support
economic growth reasonably. Using multiple monetary tools, central banks could achieve
various transmission targets through the banking system. However, after witnessing the
incidents and consequences of the global financial crisis, one attributes monetary policy to be
one of the critical constituents adding to the instability of the whole financial sector (Taylor,
2011). In this regard, monetary policy could induce numerous adverse effects on bank risk,
forming the bank risk-taking channel, mainly due to changing banks’ risk tolerance and
perception originated by interest rate fluctuations (Adrian and Shin, 2010; Borio and Zhu,
2012). More seriously, practical concerns have been raised, and central banks are called for
tightening monetary policy to restrain banks’ risk-taking desires (Acharya and Naqvi, 2012;
Diamond and Rajan, 2012). Therefore, the impact of monetary policy on bank risk-taking
becomes an important research topic that deserves more investigations.
The impact of monetary policy on bank risk-taking has recently been extensively
discussed in the literature, attracting much attention from scholars and policymakers.
However, the insight on this nexus has been limited in content and context, as reflected in two
important directions. First, many studies establish the bank risk-taking channel’s
functioning from the perspective of the credit portfolio quality and bank-level financial
JEL Classification — E52, E58, G21
International Journal of Managerial
Finance
Vol. 17 No. 4, 2021
pp. 619-639
© Emerald Publishing Limited
1743-9132
DOI 10.1108/IJMF-06-2020-0284
IJMF
17,4
620
stability. Meanwhile, only a few studies focus on how monetary policy drives separate sides
of bank balance sheets. Particularly, the stability of funding is not yet considered. Second, the
existing research is mostly interested in advanced economies, highlighted by a near-zero or
negative interest rate environment (Altunbas et al., 2014; Delis and Kouretas, 2011; Jimenez
et al., 2014; Maddaloni and Peydro, 2011). It should be noted that these economies differ
considerably from emerging markets in mature levels, regulatory frameworks and especially
the working of monetary policy [1].
To fill the gap in the literature, we empirically examine the effects of monetary policy on
bank leverage and liquidity. Theories predict that monetary policy may influence both sides
of bank balance sheets. For example, on the liability side, monetary expansion lowers banks’
funding costs and thus promotes higher leverage (Dell’Ariccia et al., 2014; Valencia, 2014) [2].
On the asset side, lower interest rates boost the “search for yield” incentive if banks have a
fixed revenue target, thus encouraging them to reduce the holdings of liquid assets (Borio and
Zhu, 2012; Rajan, 2006). The empirical literature studying these mechanisms with banking
data is still limited. Therefore, an in-depth analysis on the simultaneous effects of monetary
policy on bank leverage and liquidity is the first attempt contributing to the literature stream
on the banking channels of monetary policy transmission.
When examining the effects of monetary policy on bank risk-taking behavior, prior
documents have commonly taken into account the difference in the bank financial strength as
vital moderators (Gambacorta and Marques-Ibanez, 2011; Kashyap and Stein, 2000; Khan
et al., 2016; Kishan and Opiela, 2006). In this stream, bank size is the most widely considered
factor. The rationale is that prior analysis relies on the capacity of increasing external
funding to explain bank behavior during monetary policy fluctuations. It hypothesizes that
large banks may be less affected by monetary shocks since they have lower external funding
costs. Therefore, motivated by this stylized fact, we further pay attention to the moderating
role of bank size in the current relationships investigated. Our findings could affirm or
contradict existing speculations, thereby enriching the bank risk-taking channel with new
perspectives.
Additionally, taking a step further in this study, we use an indicator describing bank
funding stability in relation to asset illiquidity to investigate a novel impact of monetary policy
on bank behavior. Angeloni et al. (2015) theorize that relaxing monetary policy may modify the
composition of bank liabilities, particularly reconstructing stable funding sources. To this end,
we employ the net stable funding ratio (NSFR) introduced by Basel III guidelines. In the
empirical studies of monetary transmission through the banking system, the NSFR indicator
was used in regressions of the bank lending channel, but only to estimate how changes in bank
liquidity risk alter the potency of the transmission (Giordana and Schumacher, 2013), not
concentrating on how monetary policy may itself affect this indicator. Hence, understanding
how banks organize their stable funding in relation to illiquid assets during monetary policy
shocks, based on Basel III guidelines, is a key contribution of this study.
We conduct our research design for the Vietnamese banking market, gathering data from
2007 to 2018. Based on multiple aspects, Vietnam offers a favorable laboratory to examine the
present issue. First, the economy of Vietnam heavily depends on banks and their lending
activities, which makes the effectiveness of monetary policy transmission through the
banking sector more pronounced. Second, the Vietnamese banking sector has experienced
major reforms in recent years, featured by the expansion of bank assets, increased equity and
improved liquidity. During this process, large banks have always dominated the market
(Batten and Vo, 2016; Le, 2017). Third, the monetary policy in Vietnam exhibits unique
characteristics. In principle, reserve requirements and base interest rates are assumed as the
primary tools of monetary policy but remain invariant for a prolonged time. In practice, the
State Bank of Vietnam (SBV) applies a series of policy rates and administrative controls to
regulate the interest rate framework, which has never been close to zero-bound.
We organize the remainder of this study as follows. Section 2 reviews the related literature.
Section 3 exhibits the relevant background of the Vietnamese banking industry and
monetary policy regime. Section 4 presents the empirical strategy, including the variable
construction, model specification and econometric technique. Section 5 reports and discusses
the estimation results, and Section 6 concludes and indicates some implications.
2. Related literature
Generally, the literature on the bank risk-taking channel of monetary policy transmission
agrees that monetary expansion exerts a negative effect on banks’ risk perception and risk
tolerance (Adrian and Shin, 2010; Borio and Zhu, 2012). Examining bank balance sheets,
scholars establish two primary routes through which monetary policy could drive bank risktaking. The first operates on the liability side of bank balance sheets. A drop in interest rates
reduces funding costs, which is a crucial determinant of bank funding structure, hence
encouraging banks to produce higher financial leverage. This theory is modeled by
Dell’Ariccia et al. (2014) and Valencia (2014), confidently concluding that monetary policy
induces banks to modify their leverage, which, in turn, adjusts bank risk-taking levels.
Angeloni et al. (2015) also elaborate that monetary expansion may alter the composition of
bank liabilities, particularly reconstructing the stable funding sources.
The second route acts through the asset side of bank balance sheets. In this vein, the
impact of monetary policy is shown in the form of asset portfolio reallocation. Monetary
expansion signals the declined yields of safe assets. These declines lower bank returns, thus
leading banks to raise their demand for higher yield risky assets to substitute highly liquid
and low-risk assets (Dell’Ariccia et al., 2014). This mechanism is more conspicuous in case the
performance target of banks is sticky, which makes bank managers have more incentives to
move toward riskier credit segments, proposing the so-called “search for yield” incentive
(Borio and Zhu, 2012; Diamond and Rajan, 2005). Moreover, during monetary policy
expansion, banks tend to prefer long-term loans (Diamond and Rajan, 2006). As a result, bank
illiquidity rises.
It should be taken into consideration that bank risk-taking on the liability side initiates
and magnifies bank risk-taking on the asset side. Precisely, with higher leverage that implies
bank risk-shifting to other creditors, the preference of banks for risky investments grows
given the bounded losses (Angeloni et al., 2015; Valencia, 2014). However, monetary policy
easing could also generate alternative impacts. Smith (2002) theorizes that higher interest
rates elevate banks’ opportunity costs or, in other words, cash holding costs, which
discourages banks from holding liquid assets. Kane (1989) claims that banks would find risky
assets and the “gambling for resurrection” strategy more appealing after increased interest
rates cause a decline in their net worth. In another vein, increased interest rates could lower
the franchise value via improved bank profits, thus mitigating the moral hazard problem and
motivating banks to take more risks (Gan, 2004). Overall, based on the above discussion, how
monetary policy influences bank risk-taking on both sides of the balance sheet is theoretically
ambiguous and remains a significant empirical issue.
The available empirical literature on the impacts of monetary policy on bank leverage and
liquidity risk is somewhat limited. In particular, we are aware of a few studies that pay
attention to such impacts. Using time-series data in the USA from 1980 to 2008 with multiple
VAR strategies, Angeloni et al. (2015) document that monetary policy easing could cultivate
bank risk-taking in the funding structure (captured by the proxies of non-core bank liabilities)
and the asset side (measured by the soundness of bank borrowers). de Moraes et al. (2016)
confirm the working of the bank risk-taking channel in Brazil in the sense that banks respond
to monetary expansion by reducing the levels of capital adequacy ratio (CAR). An expanded
version of this work performed by de Moraes and de Mendonça (2019) completes the prior
Monetary
policy, bank
leverage and
liquidity
621
IJMF
17,4
622
research effort when indicating that lower interest rates make banks more leveraged.
Exploring how monetary policy in European countries affects bank liquidity, Lucchetta
(2007) provides some mixed results. The author finds that the investments in liquid assets are
negatively linked with risk-free interest rates but positively with interbank interest rates.
Peydro et al. (2017) investigate how banks in Italy construct their asset portfolio after
monetary shocks. They reveal that banks tend to prefer security holdings to lending activities
during monetary expansion, which is, however, captured by the central bank’s liquidity
injection. Given the different approaches with conflicting results in the existing literature, our
study is the first to simultaneously examine how bank leverage and liquidity react to
monetary policy changes.
3. Relevant background of the Vietnamese banking industry and monetary
policy
Over the last decades, the banking industry has always occupied a dominant position in the
Vietnamese financial system. It is generally considered as the key driver of economic growth.
Recent remarkable reforms have changed and opened up the Vietnamese banking industry,
especially after the country entered the World Trade Organization (WTO) in 2007. This event
led to increased participation of foreign banks in the banking market (Batten and Vo, 2016;
Le, 2017). In this regard, additional reforms have been made to promote the efficiency and
competitiveness of domestic banks. Despite multiple changes, large domestic banks still
dominate the banking system (Stewart et al., 2016). Simultaneously, the segment of foreign
banks is considerably small compared to domestic counterparts (Dang, 2019a).
The credit boom during 2007–2009 in Vietnam has caused numerous financial
consequences. Many banks suffered from heavy non-performing loan burdens and serious
liquidity problems after this period. This situation led to intense interventions and tighter
regulations by the SBV. Since 2010, the SBV announced the fundamental principles of
international guidelines on banking supervision. Banks are thus required to build up their
capital buffers to meet the minimum capital requirement. This generates considerable
implications for banks in improving efficiency and restructuring funding to survive in an
increasingly competitive environment.
The SBV sets a framework of various monetary policy targets. There is no main target
precisely defined in terms of controlling inflation, boosting economic growth and stabilizing
the macroeconomic environment (Dang and Dang, 2020). With this setting, the SBV
implements multiple monetary policy tools to fulfill its mandate, highlighting the unique
characteristics of the Vietnamese monetary policy regime. In principle, the SBV can use
traditional monetary tools, such as the reserve requirement and the base interest rate, to
regulate the banking market. These used to be powerful tools to adjust credit supply and
establish a limit for lending rates, particularly during the early operation periods of the SBV.
However, the role of the reserve requirement and the base interest rate has diminished
recently. As a result, these traditional tools remain fixed for a prolonged time.
In practice, the SBV regularly alters policy rates and implements administrative controls
to influence the interest rate framework. Regarding policy rates, the SBV has charged
commercial banks by refinance rates (for short-term loans) and rediscount rates (for
discounted valuable papers). Over the years, these policy rates were adjusted frequently to
large extents. For administrative controls, the SBV imposes ceilings on deposit rates and
floors on lending rates. The SBV introduced and removed these ceiling and floors on interest
rates repeatedly. Additionally, open market operations are also a key tool for the SBV to
pursue its monetary targets. Using this tool, the SBV could inject/withdraw liquidity directly
into/from the financial market. Because of the distinction in nature, the influential mechanism
of this tool on the banking market, compared to the interest-based tools, is not heterogeneous.
Figure 1 displays the yearly average evolution of bank leverage, liquidity and monetary
policy interest rates in the Vietnamese banking market over time. In general, bank leverage
and liquidity positions progressed in two different directions. During 2007–2018, bank
leverage posited an upward trend, while liquidity positions exhibited a downward
momentum on average. At the same time, short-term lending rates and rediscount rates
showed similar changes. After a period of alternating increases and decreases from 2007 to
2011, both interest rates began to decline continuously from 2011 to 2018. In sum, the
movements tend to show an opposite path between interest rates and bank leverage. In
contrast, most of the time, it is possible to recognize interest rates and bank liquidity
following a similar path.
Monetary
policy, bank
leverage and
liquidity
623
4. Methodology and data
4.1 Variables
There has been no consensus on how to gauge monetary policy in the existing literature so
far. Given this context and the relevant background of the monetary regime in Vietnam
Bank leverage and rediscount rates
14.00
12.00
10.00
8.00
6.00
4.00
2.00
-
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Rediscount rates (%)
Leverage
Bank liquidity and lending rates
30.00
25.00
20.00
15.00
10.00
5.00
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Lending rates (%)
Liquidity (%)
Figure 1.
Bank leverage,
liquidity positions and
monetary policy
interest rates
IJMF
17,4
624
(presented in Section 3), we use short-term lending rates, on average, in the market as the first
monetary policy indicator. Changes in this indicator well capture the adjustments in
monetary policy (e.g. Chen et al., 2017; Yang and Shao, 2016). Besides, we also consider two
types of policy rates mentioned above, namely, refinance rates and rediscount rates, which
may reasonably describe the monetary policy stance initiated by the SBV. Three indicators of
monetary policy are valuable complements that reinforce our estimation’s robustness. In this
regard, the general mechanism for these selected indicators is that a decrease in interest rates
implies an expansionary monetary policy.
We construct the measures of bank leverage and liquidity as follows. For bank leverage,
we consider the ratio of total assets to equity capital, which is a traditional measure. Higher
leverage implies that a bank depends more on liabilities rather than equity. Next, following
standard practice, we simply define bank liquidity by the ratio of liquid assets to total assets,
in which liquid assets include cash and interbank deposits. The higher the ratio is, the more
liquid a bank is, or, alternatively speaking, the less liquidity risk a bank faces.
Finally, we follow two strands of literature on the determinants of bank liquidity (or
liquidity risk) and bank leverage (or capitalization) to incorporate key control variables. To
this end, we include bank size (natural logarithm of total assets), bank return (return on
average assets) and bank risk (loan loss provisions/gross loans) to account for differences in
bank profiles. For the effect of bank size on bank leverage, larger banks may be more
leveraged as they are better recognized by the market (Gropp and Heider, 2010). As to the
impact of bank profits, theories show that more profitable banks could diminish the
asymmetric information problem (Mankiw, 1986), which lowers the cost of issuing equity
(Myers and Majluf, 1984). Hence, these banks may hold a larger capital buffer. Based on the
trade-off theory, risky banks may prefer a low debt ratio to restrict overall risk exposures
(Bradley et al., 1984).
As commonly suggested in the literature, the “too big to fail” hypothesis explains the
moral hazard and excessive risk-taking behavior of large banks (Dietrich et al., 2014). So, one
could expect these banks to hold less liquid assets than smaller banks. According to the
“search for yield” hypothesis, more profits may discourage banks from taking more risks by
investing in illiquid and high-yield assets (Rajan, 2006). Regarding the impact of credit risk on
bank liquidity, prior documents indicate that if bank asset portfolios deteriorate, depositors
will quickly claim back their money (Diamond and Rajan, 2001). This situation displays a
close correlation between credit risk and liquidity risk.
We also include the macroeconomic factors, namely, the economic cycle (growth rate of
GDP) and the inflation (annual inflation rate). These variables control the effect of the
economy, which is time-variant but constant to banks. In economically prosperous periods,
banks might enhance the capital level, thanks to better earning capacity (Shim, 2013), and
gain more incentives to substitute their assets by loans due to the growing borrowing
demand from the market (Dietrich et al., 2014). Besides, increased interest rates during
inflationary periods are likely to influence bank behavior in terms of funding and
investments (Adesina, 2019).
4.2 Sample data
We employ the data of Vietnamese commercial banks for this study. For each bank, we hand
collect the data from annual financial reports on its website from 2007 to 2018. We exclude
banks that fail to publish at least five continuous audited financial reports. Our sample
constitutes an unbalanced panel with 349 bank-year observations from 30 banks, covering
about 90% of the banking system’s total assets in Vietnam. Policy rates are extracted from
the SBV, whereas lending rates and other macro data are obtained from the International
Financial Statistics. After building variables, we winsorize them at 2.5 and 97.5% to curtail
the effects of extreme outliers.
4.3 Model specification and econometric method
To investigate the impacts of monetary policy on bank leverage and liquidity, we specify a
dynamic linear model specification as follows:
Balance structurei;t ¼ α0 þ α1 3 Balance structurei;t–1 þ α2 3 MPt–1 þ α3 3 Banki;t–1
Monetary
policy, bank
leverage and
liquidity
þ α4 3 Macrot–1 þ ui;t
(1)
where the dependent variable is either bank liquidity or bank leverage of bank i in year t. MP
denotes monetary policy indicators of main interest. Bank and Macro represent bank-level
and macroeconomic controls, respectively, while ui;t is the idiosyncratic error. All variables
are lagged by one period to mitigate the potential endogeneity bias and reflect that banks do
not react to economic decisions immediately. By inserting a lagged dependent variable, we
expect bank liquidity and bank leverage to be persistent over time since the balance sheet
structure could not be modified promptly.
The dynamic panel is subject to biased and inconsistent estimation if we regress it by
least-squares methods. Therefore, we utilize the system generalized method of moments
(GMM) estimator to solve the problems raised (Arellano and Bover, 1995; Blundell and Bond,
1998). This approach combines regression equations in differences and levels in a system,
and applies the lagged values of regressors in levels and first differences as instruments. In
this regard, the GMM estimator could deal with the lagged dependent variable, unobserved
fixed effects, independent endogenous regressors, heteroskedasticity and autocorrelation in
the regression model. We follow Roodman (2009) to gain more efficient estimates by
performing the two-step procedure and restricting the proliferation of instruments, which
may cause the “too many instruments” problem. Finally, we need two diagnostic tests to
justify the usage of the GMM estimator: (1) the Hansen test for over-identifying restrictions,
which validates the appropriateness of instruments; (2) the Arellano–Bond test for the
autocorrelation in residuals, in which we need to ensure the absence of the second-order
autocorrelation.
5. Results and discussions
After presenting the descriptive statistics, this section first presents the baseline estimates on
the impacts of monetary policy on bank liquidity and leverage in Vietnam. Then, we further
examine the heterogeneous effects across banks of different sizes. In the next subsection, we
repeat our estimation with the NSFR rule under Basel III, which speaks to the stability of
liabilities and liquidity of assets in a combined equation. This subsection is our outstanding
innovation in this study. Finally, we check the sensitivity of our obtained results in additional
different routes.
5.1 Descriptive statistics
Table 1 outlines the descriptive statistics for our research sample. Bank liquidity ranging
from 6.609% to 34.108% is distributed with a mean of 17.287% and a standard deviation of
9.120% points. This reveals a substantial variation in the levels of liquidity positions across
banks. A similar pattern is noted for bank leverage as well, which ranges from 5.451 to 16.331,
with an average value of 10.672 and a standard deviation of 3.481. Regarding monetary
policy, we could realize sizable standard deviations and wide ranges of distribution for three
indicators, thereby indicating the considerably fluctuated evolution of interest rates in
Vietnam over time.
We also report the correlation matrix in Table 2. All coefficients of independent variables
are small, except for those between monetary policy indicators and the inflation rate. Hence,
625
IJMF
17,4
Variable
Definition
Monetary policy indicators
i_lend
Short-term lending rates
i_refin
Refinance rates by the SBV
i_redis
Rediscount rates by the SBV
626
Bank liquidity and leverage
NSFR
Net stable funding ratio under
Basel III guidelines
Liquidity Cash plus interbank deposits/total
assets
Leverage Total assets/equity
Bank-level controls
Return
Return on average assets
Size
Natural logarithm of total assets
Risk
Loan loss provisions/gross loans
Table 1.
Summary statistics
Mean
S.D.
Min
Max
p25
p75
10.660
8.226
6.066
3.367
2.582
2.718
6.960
6.250
3.500
16.954
15.000
13.000
7.117
6.500
4.500
13.471
10.000
7.000
120.839
13.388
99.653
140.834
109.605
132.068
17.287
9.120
6.609
34.108
9.630
24.187
10.672
3.481
5.451
16.331
7.933
14.961
0.800
13.859
1.254
0.505
0.477
0.455
0.108
13.162
0.723
1.655
14.603
2.189
0.388
13.411
0.890
1.209
14.245
1.525
Macroeconomic controls
GDP
Growth rate of GDP
6.186
0.602
5.398
7.076
5.421
6.679
Inflation
Annual inflation rate
7.919
5.251
3.244
18.676
3.520
9.094
Note(s): The research sample covers the period from 2007 to 2018, with 349 bank-year observations from 30
Vietnamese commercial banks
in the regression stage, we exclude the inflation variable from our models to avoid severe
multicollinearity [3]. Another noteworthy preliminary result is excessively high correlation
coefficients between three types of interest rates, which justify our choice of alternative
monetary policy indicators.
5.2 Baseline estimation results
In Table 3, we present our baseline estimation results. The Hansen test offers no evidence
against the validity of the instruments, and the AR(2) test displays that there is no secondorder autocorrelation. These results ensure the consistency of the GMM estimator.
Additionally, we also observe the significant persistence of the lagged dependent
variables, justifying the use of a dynamic model. This result supports the notion that the
structure of bank balance sheets possesses a part of the explanation in itself.
In columns 1–3 (Table 3), the coefficients of monetary policy indicators are statistically
significant with a negative sign. This result reveals that banks enlarge their financial
leverage when interest rates drop. In columns 4–6 (Table 3), the coefficients on interest rates
are positive and statistically significant in all regressions of bank liquidity. This finding
supports the view that banks reduce the holdings of liquid assets in the event of monetary
policy easing; in other words, they invest more in risky assets that expose them to higher
liquidity risks. Our patterns are proved with three types of interest rates employed.
Quantitatively, the magnitude of coefficients in columns 1–3 (Table 3) indicates that a
decrease of one percentage point in interest rates would increase the leverage of banks by
approximately 0.075–0.097 units, depending on the types of interest rates considered.
Likewise, looking at columns 4–6 (Table 3), we could infer that a one percentage point
decrease in interest rates would cause bank liquidity positions to drop by nearly 0.346–
0.582% points. These results highlight the economic plausibility our analysis, albeit the
impact of monetary policy on bank leverage is quite small compared to that on bank liquidity.
i_lend
i_refin
i_redis
NSFR
Liquidity
Leverage
Return
Size
Risk
GDP
Inflation
i_refin
1.00
0.98
0.11
0.35
0.19
0.28
0.17
0.12
0.32
0.91
i_lend
1.00
0.91
0.88
0.06
0.46
0.27
0.36
0.29
0.08
0.39
0.95
1.00
0.10
0.32
0.17
0.24
0.15
0.10
0.29
0.92
i_redis
1.00
0.22
0.18
0.16
0.10
0.04
0.09
0.03
NSFR
1.00
0.22
0.39
0.38
0.27
0.06
0.46
Liquidity
1.00
0.31
0.74
0.21
0.20
0.24
Leverage
1.00
0.15
0.16
0.02
0.35
Return
1.00
0.43
0.15
0.28
Size
1.00
0.17
0.02
Risk
Inflation
1.00
GDP
1.00
0.32
Monetary
policy, bank
leverage and
liquidity
627
Table 2.
Correlation coefficients
matrix
IJMF
17,4
Lagged dependent
variable
628
i_lend
i_refin
(1)
Leverage
(2)
Leverage
(3)
Leverage
(4)
Liquidity
(5)
Liquidity
(6)
Liquidity
0.842***
0.803***
0.800***
0.400***
0.393***
0.391***
(0.036)
(0.037)
(0.027)
0.346***
(0.061)
(0.028)
(0.027)
(0.040)
0.097***
(0.024)
0.075***
(0.027)
0.542***
(0.069)
0.080***
0.582***
(0.028)
(0.072)
Size
0.378
0.773***
0.746***
1.726***
1.685***
1.605***
(0.246)
(0.205)
(0.202)
(0.508)
(0.473)
(0.467)
Return
0.025
0.289***
0.300***
2.209***
2.101***
2.071***
(0.127)
(0.094)
(0.096)
(0.532)
(0.491)
(0.505)
Risk
0.205*
0.279**
0.274**
2.695***
2.755***
2.742***
(0.123)
(0.116)
(0.124)
(0.331)
(0.328)
(0.322)
GDP
0.349***
0.298***
0.270**
0.469*
0.908***
1.145***
(0.082)
(0.093)
(0.106)
(0.242)
(0.282)
(0.282)
Observations
319
319
319
319
319
319
Banks
30
30
30
30
30
30
Instruments
26
26
26
26
26
26
AR(1) test
0.006
0.007
0.007
0.000
0.000
0.000
AR(2) test
0.488
0.502
0.525
0.987
0.814
0.920
Hansen test
0.127
0.174
0.157
0.128
0.129
0.123
Note(s): The regression results are obtained using the two-step system GMM estimator in the dynamic panel.
The dependent variables are bank leverage (total assets/equity, columns 1–3) and bank liquidity (liquid assets/
total assets, columns 4–6). The explanatory variables of interest are monetary policy indicators, including
lending rates (i_lend), refinance rates (i_refin) and rediscount rates (i_redis). Other controls are return/average
assets (Return), the natural logarithm of total assets (Size), loan loss provisions/gross loans (Risk) and the
growth rate of GDP (GDP). Diagnostic tests are displayed with p-values. Standard errors are reported in
parentheses and *, ** and *** indicate significance levels at 10%, 5% and 1%, respectively
i_redis
Table 3.
Baseline results
Overall, when the central bank eases monetary policy to stimulate the economy, banks
respond by raising their leverage on the liability side and cutting liquidity positions on the
asset side. Our consistent findings lend support to (1) the “search for yield” hypothesis which
predicts banks have more incentives to invest in riskier segments and thus reduce the
holdings of liquid assets as monetary policy is relaxed (Borio and Zhu, 2012; Rajan, 2006); and
(2) the model developed by Dell’Ariccia et al. (2014) and Valencia (2014) which concludes that
banks prioritize higher leverage due to the lower funding costs stemming from decreased
interest rates. Moreover, these empirical patterns potentially accord with the theory of
Dell’Ariccia et al. (2014) and Valencia (2014) that bank risks on the liability side initiate and
magnify those on the asset side, given the risks shifting from banks to creditors and the
bounded losses.
5.3 The asymmetric effects by bank size
In this subsection, we further analyze the impacts of monetary policy on bank leverage and
liquidity by taking into account the moderating role of bank size. To do this, we insert the
interaction terms between monetary policy indicators and bank size into the baseline
equation. Our concept here is that we anticipate some banks of different sizes to be more
involved in monetary policy transmission than others, so their heterogeneous reactions after
monetary policy shocks may offer more insights into the current topic.
Our analysis is motivated by the fact that the banking sector in Vietnam is dominated by
large banks (Batten and Vo, 2016; Le, 2017). Their dominant position is shown in various
aspects, especially the market share of credit and deposits. Under intense competition, small
banks signaled a deterioration in their credit quality recently (Dang, 2019b). Moreover,
considering bank size as one of the critical indicators of the banks’ financial strength, prior
authors claim that larger banks might easily govern loanable funds during monetary
changes by raising alternative external funds with lower costs (Kashyap and Stein, 1995).
Empirically testing this hypothesis, subsequent studies suggest that banks of different sizes
react differently to monetary policy adjustments. More precisely, larger banks’ lending and
risk-taking are less influenced by monetary shocks than smaller banks (e.g. Amidu and
Wolfe, 2013; Kishan and Opiela, 2006; Olivero et al., 2011). Additionally, thanks to competitive
advantages, large banks could experience higher marginal returns by restricting the entrance
of competitors, thus reducing their incentives to “search for yield” even when interest rates
drop (Koetter et al., 2012). Given these previous works revealing the heterogeneity in the bank
channels, it is of importance to investigate whether and how the relationship between
monetary policy and bank balance structures varies across banks of different sizes. By doing
this, our strategy enriches the literature strand that highlights the role of bank characteristics
in the transmission of monetary policy through the bank channels.
Table 4 reports the results of the asymmetric effects in the augmented model. We note that
the standalone effects of monetary policy on bank leverage and liquidity remained identical
as previously. Turning to the results of main interest, we observe that the interaction terms
enter most regressions negatively and significantly (in some detail, columns 2–3 in the
function of leverage and columns 4–6 in the specification of liquidity). These results suggest
that larger banks’ financial leverage is more responsive to monetary policy changes, while the
potency of monetary policy transmission toward bank liquidity is strengthened at smaller
banks. On the asset side, our result concurs with the vast prediction in the bank lending and
risk-taking channels that smaller banks are more sensitive to monetary shocks (Koetter et al.,
2012). In contrast, on the liability side, our finding challenges Kashyap and Stein’s (1995)
argument that large banks confront limited impacts from changes in interest rates. Hence, the
hypothesis on the lower costs of external financing at large banks needs to be further attested
to Vietnamese banks, so we leave this respect for more thorough works in the future.
Our findings are also economically significant. For instance, using column 2 in Table 4, we
infer that a decrease of one percentage point in refinance rates leads larger banks (75
percentile) to build leverage by 0.004 (0.005 3 [14.245 – 13.411]) units more than smaller
banks (25 percentile). In contrast, taking column 6 in Table 4, we expect that a decrease of one
percentage point in rediscount rates causes smaller banks (25 percentile) to reduce liquidity
positions by 0.023 (0.028 3 [14.245 – 13.411]) percentage points more than larger banks (75
percentile).
5.4 The analysis with the Basel III NSFR
Our analysis of the link between monetary policy and bank balance structure for Vietnamese
banks has two main patterns during monetary expansion: (1) banks increase their financial
leverage, and (2) banks reduce their liquidity positions. Going a step further, we now integrate
both sides of balance sheets into the NSFR rule under the Basel III Accord and ask how
monetary policy induces an impact on bank funding liquidity. Studying this linkage is the
key contribution of this paper.
The NSFR is introduced to encourage banks to hold a stable funding profile to avoid the
likelihood of eroding their liquidity positions. In other words, the NSFR compares the overall
illiquidity of bank assets to the overall stability of bank funding sources, with a prudential
balance between liquid and illiquid exposures at the benchmark of NSFR equaling to 100%.
According to the Basel III guidelines, we determine the NSFR by the ratio of “available stable
Monetary
policy, bank
leverage and
liquidity
629
IJMF
17,4
Lagged dependent
variable
630
i_lend
i_lend 3 Size
i_refin
i_refin 3 Size
(1)
Leverage
(2)
Leverage
(3)
Leverage
(4)
Liquidity
(5)
Liquidity
(6)
Liquidity
0.837***
0.779***
0.787***
0.483***
0.484***
0.474***
(0.036)
(0.036)
(0.027)
(0.028)
(0.041)
0.102***
(0.034)
0.002
(0.002)
0.069**
(0.032)
0.005***
(0.002)
(0.031)
0.520***
(0.083)
0.035***
(0.008)
0.640***
(0.091)
0.045***
(0.006)
0.088***
0.525***
(0.029)
(0.077)
i_redis 3 Size
0.003***
0.028***
(0.001)
(0.005)
Size
0.390
0.897***
0.824***
1.207***
1.171***
1.346***
(0.257)
(0.227)
(0.223)
(0.440)
(0.337)
(0.369)
Return
0.002
0.277**
0.299**
2.635***
2.598***
2.457***
(0.136)
(0.125)
(0.118)
(0.524)
(0.487)
(0.501)
Risk
0.115
0.164
0.182
2.084***
2.046***
2.230***
(0.121)
(0.114)
(0.115)
(0.311)
(0.323)
(0.316)
GDP
0.329***
0.299**
0.223*
1.203***
1.484***
1.273***
(0.121)
(0.128)
(0.128)
(0.269)
(0.262)
(0.251)
Observations
319
319
319
319
319
319
Banks
30
30
30
30
30
30
Instruments
27
27
27
27
27
27
AR(1) test
0.006
0.007
0.007
0.000
0.000
0.000
AR(2) test
0.455
0.459
0.467
0.359
0.892
0.753
Hansen test
0.152
0.170
0.183
0.171
0.156
0.142
Note(s): The regression results are obtained using the two-step system GMM estimator in the dynamic panel.
The dependent variables are bank leverage (total assets/equity, columns 1–3) and bank liquidity (liquid assets/
total assets, columns 4–6). The explanatory variables of interest are monetary policy indicators, including
lending rates (i_lend), refinance rates (i_refin) and rediscount rates (i_redis). Other controls are return/average
assets (Return), the natural logarithm of total assets (Size), loan loss provisions/gross loans (Risk) and the
growth rate of GDP (GDP). Diagnostic tests are displayed with p-values. Standard errors are reported in
parentheses and *, ** and *** indicate significance levels at 10%, 5% and 1%, respectively
i_redis
Table 4.
The heterogeneity
across banks of
different sizes
funding” (a sum of components on the liability and equity side, each component is weighted
based on its stability) to “required stable funding” (a sum of components on the asset side,
each component is weighted based on its illiquidity). Within the scope of this study, we
consider a bank with a higher value of the NSFR to improve funding liquidity, based on the
construction of the NSFR [4]. In this regard, banks could grow the NSFR in several ways, such
as by holding more liquid assets with stable funding, increasing stable funding levels or
substituting illiquid assets with liquid assets.
This study predates the NFSR regulation’s implementation in Vietnam. Moreover, our
available bank-level data from our sample do not afford all items required to compute the
NSFR precisely as suggested by Basel III. So, we employ the approximation approach
commonly used in the literature to generate the NSFR for Vietnamese banks (Dietrich et al.,
2014; King, 2013). This approach is close in spirit to Basel III guidelines [5].
Table 5 shows the estimation results for the function of the NSFR. These results
consistently suggest a negative and significant impact of monetary policy on the NSFR
across three different types of interest rates, which means that banks improve their funding
liquidity when interest rates decrease. In particular, a decrease of one percentage point in
interest rates (columns 4–6) leads to a 0.413–0.584% points increase in the NSFR.
Furthermore, the significantly positive coefficients of interaction terms reveal that the
transmission is elevated at smaller banks.
Lower interest rates reduce the liquidity of asset portfolios; in other words, it enhances
bank illiquidity, the denominator of the NSFR. So, an increase in the NSFR can be attributed
to the numerator’s overwhelming effect, which must ensure a superior increase in bank
funding stability. In should be noted that our previous interpretations exhibit the minor
impact of monetary policy on bank leverage as a whole, compared to the sizable effect on
liquidity. Consequently, the starling result from regressions of the NSFR suggests that banks
seem to rearrange their funding sources on the liability side toward a more stable structure
when costs of financing decrease. This result concretizes the idea of Angeloni et al. (2015) on
the reconstructed composition of bank liabilities during monetary shocks. In more detail,
(1) NSFR
Lagged dependent
variable
i_lend
i_refin
i_redis
i_lend 3 Size
i_refin 3 Size
i_redis 3 Size
0.503***
(0.039)
0.081
(0.125)
(2) NSFR
(3) NSFR
0.438***
0.451***
(0.039)
(0.039)
0.543***
(0.166)
(4) NSFR
0.534***
(0.040)
0.413***
(0.121)
0.500***
(0.149)
(5) NSFR
631
(6) NSFR
0.511***
0.502***
(0.039)
(0.040)
0.584***
(0.152)
Monetary
policy, bank
leverage and
liquidity
0.429***
(0.139)
0.053***
(0.011)
0.043***
(0.010)
0.027***
(0.009)
Size
2.831*
3.544*** 3.660*** 2.215*** 2.896*** 2.856***
(1.465)
(1.371)
(1.394)
(0.693)
(0.843)
(0.883)
Return
0.197
0.517
0.384
1.060
0.591
0.373
(0.737)
(0.710)
(0.724)
(0.778)
(0.876)
(0.812)
Risk
3.313***
3.539***
3.615***
2.342***
2.974***
3.256***
(0.936)
(0.943)
(0.931)
(0.818)
(0.818)
(0.841)
GDP
2.250***
2.735***
2.915***
3.950***
3.712***
3.398***
(0.433)
(0.428)
(0.459)
(0.624)
(0.549)
(0.552)
Observations
319
319
319
319
319
319
Banks
30
30
30
30
30
30
Instruments
26
26
26
27
27
27
AR(1) test
0.001
0.002
0.002
0.000
0.002
0.001
AR(2) test
0.653
0.431
0.515
0.836
0.639
0.755
Hansen test
0.103
0.138
0.135
0.104
0.123
0.118
Note(s): The regression results are obtained using the two-step system GMM estimator in the dynamic panel.
The dependent variable is the net stable funding ratio under Basel III. The explanatory variables of interest are
monetary policy indicators, including lending rates (i_lend), refinance rates (i_refin) and rediscount rates
(i_redis). Other controls are return/average assets (Return), the natural logarithm of total assets (Size), loan loss
provisions/gross loans (Risk) and the growth rate of GDP (GDP). Diagnostic tests are displayed with p-values.
Standard errors are reported in parentheses and * and *** indicate significance levels at 10% and 1%,
respectively
Table 5.
Monetary policy and
Basel III funding
liquidity regulation
IJMF
17,4
632
such funding restructure is more pronounced for smaller banks, which less prefer to
rebalance the ratio between liabilities and equity as described in Subsection 5.3.
While there is a handful of studies that examine the determinants of liquidity rules
introduced by Basel III guidelines, this study makes the first attempt to shed light on the
impact of monetary policy on the NSFR. In our context, it allows us to achieve a better
understanding of how banks organize their stable funding sources and illiquid asset
portfolios during monetary changes.
5.5 Robustness checks
Though the usage of the system GMM estimator in the dynamic panel with multiple
monetary policy indicators has yielded consistent results so far, we still desire to demonstrate
further the robustness of our findings by some additional directions. To this end, we first
employ alternative measures for bank leverage and liquidity. We continue to use liquid assets
as a share of total assets to proxy bank liquidity, in which liquid assets expand to a
combination of cash, interbank deposits and securities (Gambacorta and Mistrulli, 2004). In
Vietnam, banks could simply liquidate their securities via the stock exchange or open market
operations. Regarding bank leverage, we follow Beltrame et al. (2018) to define an adjusted
measure by the ratio of total assets plus loan loss provisions to equity plus loan loss
provisions. From the perspective of a financial intermediary, this measure could be
considered as the sterilization of the loan loss provisions’ influences. After designing
alternative dependent variables, we re-estimate the regressions with the two-step system
GMM estimator and report the results in Table 6. Interestingly, our results remain consistent
with the patterns obtained previously.
Second, we change the model specification and thus apply alternative estimators. One
could claim that bank balance sheets do not necessarily reflect a persistent nature over time.
So, we perform our analysis in this part by the static model with fixed effects. The setting is
supported by the Hausman test that suggests the preference of fixed effects over randomeffects models. We first regress our model with Driscoll-Kraay standard errors. In this
respect, we conduct the correction procedure introduced by Hoechle (2007) to address the
autocorrelation, heteroskedasticity and cross-sectional dependence issues. We also employ
the IV method (which requires exact identification) given that our model specification may
suffer from the endogeneity bias (due to potential simultaneity or omitted variables). To this
end, we use the inflation variable as an instrument for monetary policy indicators as theories
posit that the central bank establishes interest rates in response to inflation (Clarida et al.,
1998). We then re-estimate all regressions of various alternative measures for both dependent
and independent variables using models with fixed effects. Tables 7 and 8 exhibit the
outcomes [6]. Though the significance levels of several regressions have diminished slightly,
our main findings are still confirmed [7].
6. Conclusions
The study empirically examines the effects of monetary policy on bank leverage and
liquidity. Based on a sample of Vietnamese commercial banks during 2007–2018, we
document that banks become more leveraged and less liquid during monetary expansion.
Further analysis reveals that the liquidity of smaller banks is more sensitive to the monetary
policy changes, while the leverage of larger banks is more responsive to the monetary policy
shocks. The former is in line with the vast majority of literature highlighting the strengthened
potency of monetary policy transmission at smaller banks. However, the latter interestingly
challenges the widely employed claim that larger banks are less affected by monetary shocks
due to their copious substitute funding (Kashyap and Stein, 1995).
(1) Leverage
(alter)
Lagged
dependent
variable
i_lend
i_lend 3 Size
i_refin
i_refin 3 Size
0.799***
(0.044)
0.118***
(0.028)
0.001
(0.001)
(2) Leverage
(alter)
(3) Leverage
(alter)
0.739***
0.748***
(0.044)
(0.043)
0.083***
(0.030)
0.006***
(0.001)
(4) Liquidity
(alter)
0.629***
(0.039)
0.478***
(0.093)
0.044***
(0.006)
(5) Liquidity
(alter)
(6) Liquidity
(alter)
0.619***
0.613***
(0.057)
(0.060)
Monetary
policy, bank
leverage and
liquidity
633
0.529***
(0.111)
0.044***
(0.007)
0.098***
0.434***
(0.026)
(0.090)
i_redis 3 Size
0.004***
0.030***
(0.001)
(0.007)
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Observations
319
319
319
319
319
319
Instruments
27
27
27
27
27
27
AR(1) test
0.014
0.014
0.015
0.000
0.000
0.000
AR(2) test
0.447
0.411
0.423
0.258
0.403
0.386
Hansen test
0.166
0.139
0.162
0.317
0.154
0.163
Note(s): The regression results are obtained using the two-step system GMM estimator in the dynamic panel.
The dependent variables are bank leverage ([total assets þ loan loss provisions]/[equity þ loan loss provisions],
columns 1–3) and bank liquidity ([cash þ interbank deposits þ securities]/total assets, columns 4–6). The
explanatory variables of interest are monetary policy indicators, including lending rates (i_lend), refinance
rates (i_refin) and rediscount rates (i_redis). All control variables are included. Diagnostic tests are displayed
with p-values. Standard errors are reported in parentheses and *** indicates significance levels at 1%
i_redis
Additionally, as an innovation in this study, we examine how monetary policy drives the
NSFR rule under Basel III, which is introduced to maintain the stability of funding and the
liquidity of assets. The result suggests that lower interest rates encourage Vietnamese banks
to improve the NSFR, and this impact is more pronounced at smaller banks. Amid monetary
policy easing, banks appear to modify the composition of liabilities to improve the stability of
funding sources. Our findings are strongly confirmed by a battery of robustness checks,
using two econometric models of static and dynamic panels, multiple monetary policy
indicators and alternative measures of bank liquidity and leverage.
Our findings provide insightful implications for monetary authorities, particularly in
emerging countries. First, the significant response of banks to monetary policy stance may
give monetary authorities the confidence to rely on their current tools to regulate the
market. Second, the indications of bank risk-taking behavior should be carefully considered
when central banks initiate monetary policy. More concretely, when monetary policy is
softer, the bright side is that banks tend to shift their funding sources into more stable ones,
whereas the dark side is that it provides banks with incentives to utilize more financial
leverage and maintain lower liquidity positions. Third, the adverse effects of monetary
policy easing on bank risk-taking can be alleviated or elevated according to different
groups of bank sizes. Additionally, from the standpoint of research implication, our work
opens an avenue for future investigation on the impact of monetary policy on Basel
regulations.
Table 6.
Alternative measures
for bank leverage and
liquidity
Table 7.
Robustness checks
with static models
Yes
319
0.344
Yes
319
0.345
0.158**
(0.079)
0.006
(0.005)
0.145**
(0.067)
0.006
(0.004)
Yes
319
0.344
(3)
Leverage
Yes
319
0.204
239.773***
1383.299***
2312.407***
0.275***
(0.071)
0.004
(0.004)
(5) Leverage
(alter)
Yes
319
0.244
257.389***
0.163***
(0.060)
0.007*
(0.004)
(4) Leverage
(alter)
2282.061***
0.258***
(0.064)
0.006
(0.004)
Yes
319
0.209
257.015***
(6) Leverage
(alter)
Yes
319
0.475
0.782***
(0.155)
0.007
(0.010)
(7)
Liquidity
Yes
319
0.483
0.975***
(0.168)
0.015
(0.010)
(8)
Liquidity
0.883***
(0.159)
0.001
(0.009)
Yes
319
0.478
(9)
Liquidity
1969.085***
Yes
319
0.264
252.684***
0.790***
(0.186)
0.028**
(0.012)
(10) Liquidity
(alter)
1368.554***
Yes
319
0.281
239.479***
0.837***
(0.206)
0.029**
(0.012)
(11) Liquidity
(alter)
2462.750***
0.739***
(0.188)
0.014
(0.010)
Yes
319
0.275
259.213***
(12) Liquidity
(alter)
Note(s): The regression results are obtained using the fixed effects static panel, regressed with Driscoll-Kraay standard errors (columns 1–3 and columns 7–9) and the IV
approach (columns 4–6 and columns 10–12). The dependent variables are bank leverage (two alternative measures, columns 1–6) and bank liquidity (two alternative
measures, columns 7–12). The explanatory variables of interest are monetary policy indicators, including lending rates (i_lend), refinance rates (i_refin) and rediscount
rates (i_redis). All control variables are included. Standard errors are reported in parentheses and *, ** and *** indicate significance levels at 10%, 5% and 1%,
respectively. The results from the tests for the IV approach indicate that our instruments are neither under-identified nor weak
Controls
Observations
R-squared
Underidentification
test
Weak
identification
test
i_redis 3 Size
i_redis
i_refin 3 Size
i_refin
i_lend 3 Size
0.191**
(0.090)
0.003
(0.004)
(2)
Leverage
634
i_lend
(1)
Leverage
IJMF
17,4
Yes
319
0.117
0.005
(0.015)
Yes
319
0.109
0.986***
(0.260)
(6) NSFR
0.839***
(0.293)
(8) NSFR
0.778***
(0.272)
(9) NSFR
0.033**
(0.017)
0.854***
(0.267)
(10) NSFR
0.017
(0.017)
0.864***
(0.299)
(11) NSFR
0.779***
(0.272)
(12) NSFR
0.005
(0.015)
Yes
Yes
Yes
Yes
Yes
Yes
319
319
319
319
319
319
0.095
0.110
0.107
0.110
0.114
0.107
252.768*** 231.377*** 259.038*** 252.684*** 239.479*** 259.213***
1981.318*** 1140.371*** 2455.329*** 1969.085*** 1368.554*** 2462.750***
0.681***
(0.240)
(7) NSFR
Note(s): The regression results are obtained using the fixed effects static panel, regressed with Driscoll-Kraay standard errors (columns 1–6) and the IV approach
(columns 7–12). The dependent variable is the net stable funding ratio under Basel III. The explanatory variables of interest are monetary policy indicators, including
lending rates (i_lend), refinance rates (i_refin) and rediscount rates (i_redis). All control variables are included. Standard errors are reported in parentheses and ** and ***
indicate significance levels at 5% and 1%, respectively. The results from the tests for the IV approach indicate that our instruments are neither under-identified nor weak
Yes
319
0.109
Yes
319
0.110
i_redis 3 Size
Controls
Observations
R-squared
Under-identification test
Weak identification test
1.128***
(0.274)
(5) NSFR
0.022
(0.017)
0.982***
(0.259)
0.960***
(0.252)
(4) NSFR
i_refin 3 Size
Yes
319
0.112
1.033***
(0.264)
(3) NSFR
0.036**
(0.017)
Yes
319
0.096
0.711***
(0.226)
(2) NSFR
i_lend 3 Size
i_redis
i_refin
i_lend
(1) NSFR
Monetary
policy, bank
leverage and
liquidity
635
Table 8.
Robustness checks for
the NSFR with static
models
IJMF
17,4
Notes
1. See Chen et al. (2017) for more explanation of the different monetary policy background in advanced
and emerging countries.
2. We use monetary policy easing for illustration hereinafter.
3. We further check the variance inflation factor (VIF) to ensure this treatment.
636
4. Drehmann and Nikolaou (2013) define funding liquidity as banks’ ability to immediately settle their
obligations. The authors admit that this definition is partially equivalent to the view of Basel
Accords.
5. For detailed information on the weights of items on- and off-balance sheets to calculate the NSFR, see
Dietrich et al. (2014, p. 16).
6. To save space, we do not report all regressions we have. The remaining results indicate the identical
patterns and are available upon request.
7. The statistical insignificance of the interaction terms (columns 1–6 in Table 7) yields evidence that
bank size cannot modify how monetary policy drives bank leverage as substantially as the way
monetary policy alters bank liquidity.
References
Acharya, V. and Naqvi, H. (2012), “The seeds of a crisis: a theory of bank liquidity and risk taking
over the business cycle”, Journal of Financial Economics, Vol. 106 No. 2, pp. 349-366.
Adesina, K.S. (2019), “Basel III liquidity rules: the implications for bank lending growth in Africa”,
Economic Systems, Vol. 43 No. 2, doi: 10.1016/j.ecosys.2018.10.002.
Adrian, T. and Shin, H.S. (2010), “The changing nature of financial intermediation and the financial
crisis of 2007–2009”, Annual Review of Economics, Vol. 2 No. 1, pp. 603-618.
Altunbas, Y., Gambacorta, L. and Marques-Ibanez, D. (2014), “Does monetary policy affect bank risk?”,
International Journal of Central Banking, Vol. 10 No. 1, pp. 95-135.
Amidu, M. and Wolfe, S. (2013), “Does bank competition and diversification lead to greater stability?
Evidence from emerging markets”, Review of Development Finance, Vol. 3 No. 3, pp. 152-166.
Angeloni, I., Faia, E. and Lo Duca, M. (2015), “Monetary policy and risk taking”, Journal of Economic
Dynamics and Control, Vol. 52, pp. 285-307.
Arellano, M. and Bover, O. (1995), “Another look at the instrumental variable estimation of errorcomponents models”, Journal of Econometrics, Vol. 68 No. 1, pp. 29-51.
Batten, J.A. and Vo, X.V. (2016), “Bank risk shifting and diversification in an emerging market”, Risk
Management, Vol. 18 No. 4, pp. 217-235.
Beltrame, F., Previtali, D. and Sclip, A. (2018), “Systematic risk and banks leverage: the role of asset
quality”, Finance Research Letters, Vol. 27, pp. 113-117.
Blundell, R. and Bond, S. (1998), “Initial conditions and moment restrictions in dynamic panel data
models”, Journal of Econometrics, Vol. 87 No. 1, pp. 115-143.
Borio, C. and Zhu, H. (2012), “Capital regulation, risk-taking and monetary policy: a missing link in the
transmission mechanism?”, Journal of Financial Stability, Vol. 8 No. 4, pp. 236-251.
Bradley, M., Jarrell, G.A. and Kim, E.H. (1984), “On the existence of an optimal capital structure: theory
and evidence”, The Journal of Finance, Vol. 39 No. 3, pp. 1421-1460.
Chen, M., Wu, J., Jeon, B.N. and Wang, R. (2017), “Monetary policy and bank risk-taking: evidence
from emerging economies”, Emerging Markets Review, Vol. 31, pp. 116-140.
Clarida, R., Galı, J. and Gertler, M. (1998), “Monetary policy rules in practice some international
evidence”, European Economic Review, Vol. 42 No. 6, pp. 1033-1067.
Dang, V.D. (2019a), “The risk-return trade-off of liquidity positions: evidence from Vietnamese
banking system”, International Journal of Monetary Economics and Finance, Vol. 12 No. 5,
pp. 390-406.
Dang, V.D. (2019b), “The effects of loan growth on bank performance: evidence from Vietnam”,
Management Science Letters, Vol. 9 No. 6, pp. 899-910.
Monetary
policy, bank
leverage and
liquidity
Dang, V.D. and Dang, V.C. (2020), “The conditioning role of performance on the bank risk-taking
channel of monetary policy: evidence from a multiple-tool regime”, Research in International
Business and Finance, Vol. 54, doi: 10.1016/j.ribaf.2020.101301.
637
de Moraes, C.O. and de Mendonça, H.F. (2019), “Bank’s risk measures and monetary policy: evidence
from a large emerging economy”, North American Journal of Economics and Finance, Vol. 49,
pp. 121-132.
de Moraes, C.O., Montes, G.C. and Antunes, J.A.P. (2016), “How does capital regulation react to
monetary policy? New evidence on the risk-taking channel”, Economic Modelling, Vol. 56,
pp. 177-186.
Delis, M.D. and Kouretas, G.P. (2011), “Interest rates and bank risk-taking”, Journal of Banking and
Finance, Vol. 35 No. 4, pp. 840-855.
Dell’Ariccia, G., Laeven, L. and Marquez, R. (2014), “Real interest rates, leverage, and bank risktaking”, Journal of Economic Theory, Vol. 149 No. 1, pp. 65-99.
Diamond, D.W. and Rajan, R.G. (2001), “Liquidity risk, liquidity creation, and financial fragility: a
theory of banking”, Journal of Political Economy, Vol. 109 No. 2, pp. 287-327.
Diamond, D.W. and Rajan, R.G. (2005), “Liquidity shortages and banking crises”, Journal of Finance,
Vol. 60 No. 2, pp. 615-647.
Diamond, D.W. and Rajan, R.G. (2006), “Money in a theory of banking”, American Economic Review,
Vol. 96 No. 1, pp. 30-53.
Diamond, D.W. and Rajan, R.G. (2012), “Illiquid banks, financial stability, and interest rate policy”,
Journal of Political Economy, Vol. 120 No. 3, pp. 552-591.
Dietrich, A., Hess, K. and Wanzenried, G. (2014), “The good and bad news about the new liquidity
rules of Basel III in Western European countries”, Journal of Banking and Finance, Vol. 44 No. 1,
pp. 13-25.
Drehmann, M. and Nikolaou, K. (2013), “Funding liquidity risk: definition and measurement”, Journal
of Banking and Finance, Vol. 37 No. 7, pp. 2173-2182.
Gambacorta, L. and Marques-Ibanez, D. (2011), “The bank lending channel: lessons from the crisis”,
Economic Policy, Vol. 26 No. 66, pp. 135-182.
Gambacorta, L. and Mistrulli, P.E. (2004), “Does bank capital affect lending behavior?”, Journal of
Financial Intermediation, Vol. 13 No. 4, pp. 436-457.
Gan, J. (2004), “Banking market structure and financial stability: evidence from the Texas real estate
crisis in the 1980s”, Journal of Financial Economics, Vol. 73 No. 3, pp. 567-601.
Giordana, G.A. and Schumacher, I. (2013), “Bank liquidity risk and monetary policy. Empirical
evidence on the impact of Basel III liquidity standards”, International Review of Applied
Economics, Vol. 27 No. 5, pp. 633-655.
Gropp, R. and Heider, F. (2010), “The determinants of bank capital structure”, Review of Finance,
Vol. 14 No. 4, pp. 587-622.
Hoechle, D. (2007), “Robust standard errors for panel regressions with cross-sectional dependence”,
Stata Journal, Vol. 7 No. 3, pp. 281-312.
Jimenez, G., Ongena, S., Peydro, J. and Saurina, J. (2014), “Hazardous times for monetary policy: what
do twenty-three million bank loans say about the effects of monetary policy on credit risktaking?”, Econometrica, Vol. 82 No. 2, pp. 463-505. The Econometric Society.
IJMF
17,4
Kane, E.J. (1989), “The S&L insurance mess: how did it happen?”, The Journal of Finance, Vol. 44
No. 5, pp. 1444-1448.
Kashyap, A.K. and Stein, J.C. (1995), “The impact of monetary policy on bank balance sheets”, in
Carnegie-Rochester Confer. Series on Public Policy, Vol. 42 No. C, pp. 151-195.
Kashyap, A.K. and Stein, J.C. (2000), “What do a million observations on banks say about the
transmission of monetary policy?”, American Economic Review, Vol. 90 No. 3, pp. 407-428.
638
Khan, H.H., Ahmed, R.B. and Gee, C.S. (2016), “Bank competition and monetary policy transmission
through the bank lending channel: evidence from ASEAN”, International Review of Economics
and Finance, Vol. 44, pp. 19-39.
King, M.R. (2013), “The Basel III net stable funding ratio and bank net interest margins”, Journal of
Banking and Finance, Vol. 37 No. 11, pp. 4144-4156.
Kishan, R.P. and Opiela, T.P. (2006), “Bank capital and loan asymmetry in the transmission of
monetary policy”, Journal of Banking and Finance, Vol. 30 No. 1, pp. 259-285.
Koetter, M., Kolari, J.W. and Spierdijk, L. (2012), “Enjoying the quiet life under deregulation? Evidence
from adjusted lerner indices for U.S. banks”, Review of Economics and Statistics, Vol. 94 No. 2,
pp. 462-480.
Le, T.D.Q. (2017), “The interrelationship between net interest margin and non-interest income:
evidence from Vietnam”, International Journal of Managerial Finance, Vol. 13 No. 5, pp. 521-540.
Lucchetta, M. (2007), “What do data say about monetary policy, bank liquidity and bank risk taking?”,
Economic Notes, Vol. 36 No. 2, pp. 189-203.
Maddaloni, A. and Peydro, J.L. (2011), “Bank risk-taking, securitization, supervision, and low interest
rates: evidence from the Euro-area and the U.S. lending standards”, Review of Financial Studies,
Vol. 24 No. 6, pp. 2121-2165.
Mankiw, N.G. (1986), “The allocation of credit and financial collapse”, The Quarterly Journal of
Economics, Vol. 101 No. 3, pp. 455-470, Oxford University Press (OUP).
Myers, S.C. and Majluf, N.S. (1984), “Corporate financing and investment decisions when firms have
information that investors do not have”, Journal of Financial Economics, Vol. 13 No. 2,
pp. 187-221.
Olivero, M.P., Li, Y. and Jeon, B.N. (2011), “Consolidation in banking and the lending channel of
monetary transmission: evidence from Asia and Latin America”, Journal of International Money
and Finance, Vol. 30 No. 6, pp. 1034-1054.
Peydro, J.-L., Polo, A. and Sette, E. (2017), “Monetary policy at work: security and credit application
registers evidence”, SSRN Electronic Journal. doi: 10.2139/ssrn.2958917.
Rajan, R.G. (2006), “Has finance made the world riskier?”, European Financial Management, Vol. 12
No. 4, pp. 499-533.
Roodman, D. (2009), “How to do xtabond2: an introduction to difference and system GMM in Stata”,
Stata Journal, Vol. 9 No. 1, pp. 86-136.
Shim, J. (2013), “Bank capital buffer and portfolio risk: the influence of business cycle and revenue
diversification”, Journal of Banking and Finance, Vol. 37 No. 3, pp. 761-772.
Smith, B.D. (2002), “Monetary policy, banking crises, and the Friedman rule”, American Economic
Review, Vol. 92 No. 2, pp. 128-134.
Stewart, C., Matousek, R. and Nguyen, T.N. (2016), “Efficiency in the Vietnamese banking system: a
DEA double bootstrap approach”, Research in International Business and Finance, Vol. 36,
pp. 96-111.
Taylor, J.B. (2011), “The financial crisis and the policy responses: an empirical analysis of what went
wrong”, SSRN Electronic Journal. doi: 10.2139/ssrn.1341170.
Valencia, F. (2014), “Monetary policy, bank leverage, and financial stability”, Journal of Economic
Dynamics and Control, Vol. 47, pp. 20-38.
Yang, J. and Shao, H. (2016), “Impact of bank competition on the bank lending channel of monetary
transmission: evidence from China”, International Review of Economics and Finance, Vol. 43,
pp. 468-481.
Corresponding author
Van Dan Dang can be contacted at: dandv@buh.edu.vn
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
Monetary
policy, bank
leverage and
liquidity
639