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Excess liquidity and net interest margins: Evidence from Vietnamese banks
Journal Pre-proof Excess Liquidity and Net Interest Margins: Evidence from Vietnamese Banks Thai Vu Hong Nguyen, Tra Thi Thu Pham, Canh Phuc Nguyen, Thanh Cong Nguyen, Binh Thanh Nguyen
PII:
S0148-6195(19)30130-4
DOI:
https://doi.org/10.1016/j.jeconbus.2020.105893
Reference:
JEB 105893
To appear in:
Journal of Economics and Business
Received Date:
2 May 2019
Revised Date:
19 January 2020
Accepted Date:
21 January 2020
Please cite this article as: Hong Nguyen TV, Thu Pham TT, Nguyen CP, Nguyen TC, Nguyen BT, Excess Liquidity and Net Interest Margins: Evidence from Vietnamese Banks, Journal of Economics and Business (2020), doi: https://doi.org/10.1016/j.jeconbus.2020.105893
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Excess Liquidity and Net Interest Margins: Evidence from Vietnamese Banks By Thai Vu Hong Nguyen, Tra Thi Thu Pham, Canh Phuc Nguyen, Thanh Cong Nguyen, and Binh Thanh Nguyen
Corresponding author: Thai Vu Hong Nguyen
702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam Email: [email protected]; [email protected]
Tra Thi Thu Pham
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RMIT University, Vietnam Campus.
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Work tel: (+8423) 3776 1300 (ext 1309)
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School of Business and Management, RMIT University, Vietnam Campus.
702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam
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Canh Phuc Nguyen
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Email: [email protected]
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School of Banking, University of Economics Ho Chi Minh City. 59C Nguyen Dinh Chieu, District 3, Ho Chi Minh City, Vietnam Email: [email protected]
Thanh Cong Nguyen School of Accounting – Finance – Banking, Ho Chi Minh City University of Technology 475A Dien Bien Phu Street, Binh Thanh District, Ho Chi Minh City, Vietnam 1
Email: [email protected]
Binh Thanh Nguyen School of Business and Management, RMIT University, Vietnam Campus. 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam Email: [email protected]
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Highlights
Excess liquidity compresses net interest margins (NIM) of Vietnamese banks.
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Excess liquidity attenuates the positive impacts of policy interest rates on NIM.
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Excess liquidity is argued to make tightening monetary policy less effective.
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JEL: E40, G21
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Abstract: This study analyzes the impacts of excess liquidity in association with monetary policy rates on commercial banks’ performance – as indicated by their net interest margins (NIMs) – in Vietnam. The study finds that excess liquidity compresses NIMs and attenuates the positive impacts of policy interest rates on NIMs. The study argues that excess liquidity induces banks to reduce lending interest rates so as to facilitate credit expansion, making tightening monetary policy less effective. The study extends the monetary policy transmission literature to the context of an emerging economy with excess liquidity.
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Keywords: excess liquidity, net interest margin, monetary policy rate, emerging market, Vietnamese banks
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1. Introduction Excess liquidity has recently become a prominent phenomenon in emerging economies (Menon, 2009; Nguyen and Boateng, 2015a) – a consequence of trade and financial openness policies encouraging large capital inflows (Zhang, 2009). These capital inflows build pressures on domestic currency appreciation, and government intervention is then required to maintain a fixed exchange regime. However, currency sterilization mostly remains incomplete, resulting in large excess liquidity in banking systems (Ganley; 2004, Miura,
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2008; Zhang 2009, Nguyen and Boateng, 2015a). The literature on commercial banks’ responses to excess liquidity, which includes their lending and risk-taking behaviors, is scarce but growing (Nguyen and Boateng, 2013, 2015b; and Nguyen et al., 2018b). For example, Nguyen and Boateng (2013) employ a sample of 95
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commercial banks in China from 2000 to 2011 and find that large banks and liquid banks are
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more vulnerable to monetary policy shocks when a large excess reserve is present. Nguyen and Boateng (2015b) further find that the existence of an excess reserve incentivizes risk-
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taking behaviors among Chinese commercial banks. In addition, Nguyen et al. (2018b) argue that the accumulation of banking excess reserves encourages banks to take greater risk and
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exposes those banks to higher financing cost in the subsequent periods. Therefore, banks tend to pass tightening monetary policy rates through more rapidly (Nguyen et al., 2018b).
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This study contributes to the literature on commercial banks’ responses to excess liquidity by
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examining the outcomes of their response – especially, the impact of excess liquidity on bank performance as indicated by their net interest margins (NIMs). NIM represents the difference between interest income and interest expense, and reflects intermediation efficiency or the cost of intermediation (Almarzoqi and Naceur, 2015). NIM is measured as the ratio of net interest income to earning assets. We investigate the impact of excess liquidity on NIMs in association with monetary policy changes in Vietnam. We argue that excess liquidity induces bank managers to reduce lending interest rates so as to facilitate aggressive lending and 3
improve their remuneration. We find evidence that excess liquidity compresses NIMs. Interestingly, we also find that policy interest rates contribute positively to NIMs, yet excess liquidity attenuates the positive impact of the policy rates on NIMs. This study thus contributes to the literature on two fronts. First, it sheds lights on the impact of excess liquidity on commercial banks’ NIMs. Second, it extends the theories on monetary policy transmission to the context of an emerging economy with excess liquidity. We examine the context of Vietnam, a fast-growing economy in which banks play a key role
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in capital intermediation. Excess liquidity is a critical issue in Vietnam and considered one of the roots of high inflation (Miura, 2008; Menon, 2009; Vo and Nguyen, 2014). Capital inflows into Vietnam through both direct and indirect investments have increased sharply since Vietnam joined the World Trade Organization in 2006 (Nguyen and Nguyen, 2010).
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The surge of these strong capital inflows, coupled with large remittance flows, has surpassed
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the current account deficit (due to the trade deficit) and led to an overall capital surplus in the economy (Nguyen and Nguyen, 2010). The State Bank of Vietnam (SBV), the country’s
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central bank, has had to inject large amounts of the domestic currency to stabilize the exchange rate; however, the sterilization was neither timely nor complete, leading to excess
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liquidity in the economy and high inflation rates during the 2010s (Nguyen and Nguyen, 2010).
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As Vietnam is a transitioning economy, some argue that open market operations are
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ineffective as a monetary policy tool (Nguyen and Do, 2017). In addition, the use of interest on required reserves is limited and the SBV does not pay interest on excess reserves (Camen, 2006). According to the SBV (2010), the refinancing rate (i.e., the rate it applies to credit institutions as the lender of last resort) is its main monetary policy instrument. In other words, it is the main channel used by the SBV to manage liquidity in the economy (Camen, 2006). Therefore, the refinancing rate serves as a guide for the lending and deposit interest rates of commercial banks in Vietnam (Le and Pfau, 2009). 4
The SBV has significantly varied its refinancing rate over the past decade or so. For example, in response to inflationary pressure from the abovementioned large capital inflows, it doubled the refinancing rate from 7.5% to 15% during 2007–2008; consequently, inflation fell sharply from 28% to 5% in 2009 (Pham, 2016). In the same year, the SBV decided to cut the refinancing rate to 7% to support economic growth at the expense of increasing inflation (Pham, 2016). However, the high inflation rate (around 23% during 2010–2011) led the SBV to sharply increase the refinancing rate to 15% in 2011. Thereafter, the rate was cut gradually over 2012–2015 to the 2006 level of 6.5%. It is interesting to examine how Vietnamese
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monetary policy affected the NIMs of commercial banks during this period of excess liquidity because their lending and deposit interest rates are driven by the SBV’s refinancing rate—as opposed to other countries, in which the slope of the yield curve has a more
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important impact on NIMs (Borio et al., 2017; Alessandri and Nelson, 2015; English et al.,
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2018).
The remainder of the paper is structured as follows. Section 2 develops hypotheses. The
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methodology and data collection are discussed in Section 3. Section 4 presents the data analysis, and Section 5 concludes the study with policy implications.
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2. Hypothesis Development
The literature on the impact of policy interest rates on NIMs offers mixed conclusions, and it
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has recently regained academic interest following the global financial crisis in 2008. Borio et
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al. (2017) point out that policy rates affect NIMs via the “retail deposit endowment effect” and “quantity effect”. First, the former implies that banking markets with limited competition tend to price deposits as a markdown, and hence, the endowment effect allows banks to improve profitability during periods of high inflation. This endowment effect, which suggests the rigidity of deposit rates, is in line with Claessens et al.’s (2017) finding that low rates negatively affect NIMs as banks are reluctant to lower deposit rates quickly. Ampudia and Van den Heuvel (2019) also posit that the policy rate cuts squeeze the NIMs of Euro-area 5
banks, as they are reluctant to reduce deposit interest rates when short-term rates are already low. Second, the quantity effect argues that higher policy rates decrease bank loans, and banks then tend to expand NIMs to maintain their profitability with a smaller credit volume (Alessandri and Nelson, 2015). Borio et al. (2017) find a positive impact of the interest rate structure on the net interest income for 14 major advanced economies. Alessandri and Nelson (2015) provide evidence that policy rates contribute positively to banks’ NIMs and suggest
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that banks in the United Kingdom raise their lending interest rates and lower lending quantities in response to higher funding costs.
However, other studies finds a negative impact of policy rates on NIMs. For instance, English
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et al. (2018) provide evidence that unanticipated increases in the level or the slope of the yield curve raise the NIMs of US banks in the short term, supporting the argument of sticky
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deposit interest rates. Nevertheless, NIMs decline in the medium term when banks substitute
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core deposits with non-core liabilities, which are repriced much more quickly at higher market interest rates. The change in the composition of bank liabilities makes the overall effect on NIMs negative (English et al., 2018). In this line, Berry et al. (2019) note that
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tightening monetary policy episodes in the United States do not always lead to higher NIMs,
and loans.
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as interest rate pass-through sensitivities depend on the competitive environment of deposits
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With regard to excess liquidity, Acharya and Naqvi (2012) develop a conceptual framework under which excess liquidity creates the perception of low liquidity risk among bank managers. The perception of low liquidity risk induces bank managers to lend aggressively to improve their remuneration. Bank managers can also conceal their risk easily under the excess liquidity condition (Acharya and Naqvi, 2012). Empirical studies provide evidence that excess liquidity leads to credit expansion (Nguyen and Boateng, 2013), higher risk to 6
commercial banks (Nguyen and Boateng, 2015b), higher remuneration to bank managers (Nguyen et al., 2018a), and commercial banks’ rapid responses to tightening monetary policy (Nguyen et al., 2018b). However, the literature largely ignores the mechanism facilitating the aggressive lending process induced by excess liquidity. In this study, we argue that excess liquidity incentivizes bank managers to reduce lending interest rates to expand credit volume. Given the rigidity of deposit rates under the retail deposit endowment effect and quantity effect discussed above (Borio et al., 2017), we hypothesize that excess liquidity induces
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lower lending interest rates, and hence has an adverse effect on NIMs. In addition, we argue that excess liquidity attenuates the impact of the policy rates on NIMs. Under the retail deposit endowment effect and the quantity effect, deposit rates tend to be rigid; therefore, higher policy rates lead to higher NIMs thanks to the increase in lending
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interest rates (Borio et al., 2017). We argue that in the presence of excess liquidity, banks
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tend to reduce lending interest rates to expand credit volume, and this makes lending interest rates less responsive to policy rates. Therefore, both the retail deposit endowment effect and
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the quantity effect will be less effective. However, under the composition effect, in response to tightening monetary policy, banks substitute deposits with non-core liabilities, which
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would adjust quickly to higher interest rates and thus have a negative effect on NIMs (English et al., 2018). We argue that in an environment of excess liquidity, banks may not
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feel the need to switch to other types of funding and would not reprice short-term liabilities quickly, making the composition effect less likely. Hence, the presence of excess liquidity
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would attenuate the negative impact of monetary policy rate on NIMs. Therefore, we hypothesize that excess liquidity lessens the impacts of monetary policy rates on NIMs. 3. Methodology and Data 3.1 Data We collect banking data from Fitch’s International Bank Database (Bankscope) for the period from 2006 – when Vietnam joined the World Trade Organization and began to experience 7
huge capital inflows (Nguyen and Nguyen, 2010) – to 2015. We consider only commercial banks and exclude other bank types as they are less profit-oriented. After removing samples with missing data from the population of 31 banks, our final unbalanced sample consists of 21 banks with 210 annual observations; Gambacorta (2005) finds evidence that adopting an annual frequency is sufficient to capture the heterogeneity in Italian banks’ lending adjustment to monetary policy. Appendix 1 lists the banks in our sample. Macro data are collected from the World Bank Database.
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3.2 Econometric Model Following Ho and Saunders (1981) and Zhou and Wong (2008), we use the following model to capture the impact of excess liquidity on NIMs:
NIM it 0 1 NIM it 1 2 AOCit 3 KAit 4 IIPit 5Qualityit 6 LRit 7 Sizeit
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NIM it 0 8CRt 9 MPt 10 Excesst 11MPt Excesst it
(1)
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Where NIM is net interest margin. Following Zhou and Wong (2008), we control for bank specific characteristics by including average operating cost (AOC), equity ratio (KA), implicit
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interest payments (IIP), quality of management (Quality), credit risk measured as the ratio of net loans to total assets (LR), and size of operations (Size). We also control for concentration
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ratio (CR). MP is the change in monetary policy rates. Prior studies argue that the refinancing rate is an effective policy rate and reflects the monetary policy stance in Vietnam (Vo and
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Nguyen, 2017). We provide the variable measurement details in Appendix 2.
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We measure excess liquidity (Excess) using three alternative methods based on the ratio of broad money M2 to nominal gross domestic product (M2/NGDP), which is common in the literature on excess liquidity (see Zhang, 2009). For the first method, we estimate the theoretical equilibrium in the money market under Taylor rules (Litterman and Weiss, 1983; Taylor, 1993; Woodford, 2001; Orphanides, 2003). The theoretical equilibrium in the money market is predicted by three main explanatory variables, including the real GDP per capita growth rate (GDPpcgrowth) to capture national income changes, inflation (Inflation), and the 8
real interest rate (RIR). The World Bank defines real interest rate as the lending interest rate adjusted for inflation based on the GDP deflator. We collect the residuals (µ𝑡 ) from the regression model (2) to reflect the excess liquidity condition. M 2t 0 1 RIRt 2 Inflationt 3GDPpcGrowtht t NGDPt
(2)
For the second and third methods, we identify the trend of the M2/NGDP ratio by the Hodrick–Prescott filter (HP) (Hodrick and Prescott, 1997) and the Baxter-King filter (BK) (Baxter and King, 1999). The deviation from the HP and BK trends reflect excess liquidity
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conditions. Therefore, we form the series of excess liquidity by taking the deviations from those two alternative filters.
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To deal with the endogeneity arising from the correlation between the lagged dependent variable and the error term, we employ the system generalised method of moments (SGMM)
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estimator developed by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998) for the main regression (1). Blundell and Bond (1998) designed the SGMM
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estimator to augment GMM (also called the difference GMM) by estimating simultaneously in differences and levels. The equation in differences is instrumented by the lagged values,
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while the equation in levels is instrumented by the first difference of the lagged values (Roodman, 2009). We apply the SGMM rather than the GMM as the former is more efficient
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for unbalanced panels (Roodman, 2009). The impact of excess liquidity associated with the monetary policy rates on NIMs is captured by the interaction term. The model residuals are
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free of serial correlation and unit roots by applying Arellano-Bond tests (Arellano and Bond, 1991) and Agumented Dickey-Fuller test (Greene, 2002). To ensure robustness of the results, we also employ the fixed effects model (FEM) to capture individual bank effects. Table 1 summarizes data descriptions. The NIMs fluctuate between 0.7% to 11%. The maximum values of excess liquidity span between 14.8 to 19.8 of NGDP across alternative
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measurement methods. The monetary policy rate varies between 6.5% and 15% over the sample period. Appendix 3 presents the charts of the key variables. [Insert Table 1 here] 4. Estimation Results [Insert Tables 2 and 3 here] Table 2 reports the regression results for SGMM. The F-test results reject the hypothesis that the independent variables are jointly equal to zero. The Arellano-Bond tests, AR(1) and
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AR(2), indicate first-order serial correlation, but no second-order serial correlation in the residuals (Arellano and Bond, 1991). The Hansen values (not equal to 1) ensure the
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appropriateness of the models (Roodman, 2009). Table 3 reports the results for the FEM.
With regard to the control variables, banks with high operating costs, high equity ratios, and
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large size tend to have higher NIMs. Meanwhile, banks with high non-interest expenses, high cost-to-income ratios, and high loan-to-total-assets ratios tend to maintain lower NIMs. In
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addition, we find that higher concentration decreases NIMs.
With regard to monetary policy and excess liquidity, we find that the coefficients of monetary
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policy rate change (MP) are positive and significant across the regressions. This result indicates that the change in monetary policy rates contributes positively to NIMs. This
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finding is in line with studies stating that banks shrink credit supply and increase lending
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interest rates more rapidly compared with deposit rates to maintain profitability when monetary policy is tightened. It further aligns with the findings of Alessandri and Nelson (2015) and Borio et al. (2017), who show a positive relationship between policy rates and NIMs in advanced economies. It is also consistent with the argument that deposit interest rates tend to be rigid in the short term (English et al., 2018; Berry, et al., 2019). On the contrary, this finding does not support the view that banks substitute deposits with non-core liabilities, which may adjust quickly to the market rates in the medium term (English et al., 10
2018), as deposits remain the main source of funding for Vietnamese banks (Tran et al., 2015). This result implies that banks can pass tightening monetary policy rates through to borrowers; therefore, the SBV should consider the interest rate burden when firms borrow during tightening policy regimes. We also find that the coefficients of excess liquidity (Excess) are consistently negative, and the results are statistically significant. These results suggest that the excess liquidity condition compresses NIMs. This finding supports our hypothesis that excess liquidity induces bank
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managers to reduce lending interest rates to facilitate aggressive lending, and hence negatively affects NIMs. In addition, the significantly negative coefficients of the interaction term indicate that the presence of excess liquidity attenuates the positive impact of the monetary policy rates on NIMs. This result supports our argument that excess liquidity makes
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monetary policy transmission less effective, as lending interest rates tend to be less
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responsive to higher policy interest rates. This finding aligns with the evidence presented by Nguyen et al. (2018b) that liquid banks tend to adjust lending interest rates sluggishly in
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response to tightening monetary policy in China. Therefore, the liquidity condition in the economy should be considered to ensure the effectiveness of tightening monetary policy.
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We can derive the total effects of monetary policy (MP Total) and excess liquidity (Excess Total) on NIMs using the means of Excess and MP, respectively, as follows:
∂MP
= β9 +
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∂NIM
∂NIM
β11 × 𝐸𝑥𝑐𝑒𝑠𝑠 , and
∂Excess
= β10 + β11 × 𝑀𝑃. The results show that the interaction term
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accentuates the negative impact of excess liquidity on NIMs. The total effect of monetary policy rate turns negative due to the strong negative effect of excess liquidity, except for the SGMM estimation with excess liquidity measured by the regression method (regression 1). To conduct the robustness check, we measure bank size (𝑆𝑖𝑧𝑒) using the log of total assets as an alternative to the log of gross loans. We include the real GDP growth rate to capture the demand for loans and examine the policy interest rate level as an alternative to the policy rate 11
change. We conduct these robustness regressions using both the SGMM and the FEM estimators. Appendixes 4 and 5 provide the robustness results, which are in line with those of the main regressions in Tables 2 and 3. 5. Conclusion This study examines the impacts of excess liquidity in association with monetary policy rates on NIMs. We find that higher monetary policy interest rates expand NIMs, while excess liquidity compresses NIMs. In addition, excess liquidity attenuates the positive impacts of
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monetary policy interest rates on NIMs. These results indicate that excess liquidity tends to induce banks to reduce lending interest rates so as to expand credit supply. This behavior negatively affects NIMs and makes monetary policy transmission less effective when policy
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rates increase.
We suggest that central banks reduce excess liquidity in the economies to maintain healthy
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profitability in the banking systems and financial stability. In addition, they should conduct
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prudent monetary policies, as a tightening regime may be transferred to the economy sluggishly. Future studies should consider the effect of excess liquidity lags to capture risk taking in previous periods in response to concurrent monetary policy changes, as Nguyen et
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funding sources.
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al. (2018b) suggest. Future studies should also consider the impacts of excess liquidity on
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Claessens, S., Coleman, N. and Donnelly, M. (2017). “Low-for-long” interest rates and banks’ interest margins and profitability: cross-country evidence. Journal of Financial Intermediation, forthcoming, https://doi.org/10.1016/j.jfi.2017.05.004
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English, W. B., Van den Heuvel, S. J., and Zakrajšek, E. (2018). Interest Rate Risk and Bank Equity Valuations. Journal of Monetary Economics, 98, 80‐97.
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Tables
Mean
Std. Dev.
Min
Max
NIM it
0.05253
0.01782
-0.00703
0.11133
AOCit
0.01552
0.00594
-0.00396
0.05990
KAit
0.11988
0.07007
-0.03704
0.46260
IIPit
0.02414
0.01137
-0.00487
0.08953
Qualityit
0.47600
0.16319
-0.13476
1.40815
LRit
0.52116
0.13342
-0.19429
0.85168
Sizeit
17.5895
1.43345
-13.5720
20.5615
CRt
0.46573
0.08582
MPt
0.00150
0.03144
MPt - level
0.08425
2.54094
Excesst by Regression
0.05392
0.08774
Excesst by BK filter
0.09211
0.05602
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Table 1: Summary Statistics for NIM Regression Variables
Excesst by HP filter
0.03317
0.14097
-0.37200
0.65960
-0.06000
0.06000
0.06500
0.15000
-0.07218
0.19839
-0.00963
0.14816
-0.25446
0.18460
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na
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Variable
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Table 2: NIM Regression Results (SGMM)
𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
F-Statistics
(2)
(3)
0.00757 (0.0663) 4.953*** (0.569) 0.0498** (0.0202) -1.255*** (0.268) -0.0708*** (0.0115) -0.0873*** (0.0218) 0.00463*** (0.000438) -0.00561** (0.00269) 0.0604** (0.0234) -0.00669 (0.0151) -0.742** (0.339) 0.02 -0.0078 189 19 21
0.0978 (0.121) 4.499*** (0.632) 0.0921** (0.0371) -1.161*** (0.270) -0.0699*** (0.0135) -0.0757*** (0.0218) 0.00435*** (0.000567) -0.0129*** (0.00363) 0.0744** (0.0317) -0.0260* (0.0131) -2.289* (1.262) -0.136 -0.029 147 20 21
0.0932* (0.0472) 1.360 (1.052) 0.0816 (0.0730) -0.199 (0.421) -0.0512*** (0.0102) -0.0259 (0.0269) 0.00443*** (0.000801) -0.00894*** (0.00241) 0.0710** (0.0298) -0.0952*** (0.0182) -4.260*** (0.889) -0.07 -0.102 147 19 21
1937.68 -0.000 -2.720 -0.006 -0.150 -0.878 -8.330 -0.501
-425.5 -0.000 -2.230 -0.025 -0.970 -0.333 -4.030 -0.854
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𝑀𝑃𝑡 (𝑇𝑜𝑡𝑎𝑙) 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 (Total) Number of Observations Number of Instruments Number of Groups
(1)
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𝐴𝑂𝐶𝑖𝑡
Excess measured by Hodrick–Prescott
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𝑁𝐼𝑀𝑖𝑡−1
Excess measured by Baxter–King
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𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
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SGMM Estimations
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Prob. Arellano-Bond test for AR(1) Prob. Arellano-Bond test for AR(2) Prob. Hansen test of overid. Prob. Standard errors in parentheses
1911.51 -0.000 -2.970 -0.003 -0.590 -0.558 -10.93 -0.206
*** p<0.01, ** p<0.05, * p<0.1
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Table 3: NIM Regression Results (FEM)
𝐴𝑂𝐶𝑖𝑡 𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
Excess measured by Hodrick–Prescott
(4)
(5)
(6)
1.993*** (0.240) 0.0667*** (0.0166) -0.287** (0.126) -0.0587*** (0.00603) -0.0710*** (0.00851) 0.00511*** (0.00120) -0.00427 (0.00517) 0.0568* (0.0295) -0.0279*** (0.0103) -1.598*** (0.449) -0.00152 (0.0239) -0.03 -0.03 210 0.661 0.661 0.475 0.599 0.000
3.206*** (0.328) 0.0707** (0.0282) -0.412*** (0.126) -0.0558*** (0.00578) -0.0839*** (0.0112) 0.00438 (0.00319) -0.00929** (0.00404) 0.0399* (0.0208) -0.0489*** (0.0172) -1.697 (1.071) 0.00657 (0.0588) -0.12 -0.05 147 0.793 0.793 0.758 0.776 0.000
2.026*** (0.247) 0.0685*** (0.0189) -0.275** (0.126) -0.0638*** (0.00608) -0.0719*** (0.00860) 0.00766*** (0.00258) -0.00821** (0.00405) 0.00890 (0.0247) -0.0146 (0.0132) -0.906*** (0.309) -0.0428 (0.0475) -0.02 -0.01 210 0.647 0.647 0.268 0.49 0.000
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𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
Excess measured by Baxter–King
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𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
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FEM Estimations
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Constant
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𝑀𝑃𝑡 (𝑇𝑜𝑡𝑎𝑙) 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 (Total) Observations R-squared within R-squared between R-squared overall F-Statistics
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Prob.
*** p<0.01, ** p<0.05, * p<0.1
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Standard errors in parentheses
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Appendices Appendix 1: List of Banks
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
CTG EXIM HDB KLB MB MSB NAB NVB OCB PGB SAIGON SEABANK TCB VAB VCB VIB VPBANK
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BIDV
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4
Full names An Binh Commercial Joint Stock Bank Asia Commercial Bank Viet Capital Bank The Joint Stock Commercial Bank for Investment and Development of Vietnam Vietnam Joint Stock Commercial Bank for Industry and Trade Vietnam Export Import Commercial Joint-Stock Bank Ho Chi Minh City Housing Development Joint Stock Bank Kienlong Commercial Joint Stock Bank Military Commercial Joint Stock Bank Vietnam Maritime Commercial Joint Stock Bank National Australia Bank National Citizen Commercial Joint Stock Bank Orient Commercial Joint Stock Bank Petrolimex Group Commercial Joint Stock Bank Saigon Commercial Bank Southeast Asia Commercial Joint Stock Bank Vietnam Technological and Commercial Joint Stock Bank Vietnam Asia Commercial Joint Stock Bank Joint Stock Commercial Bank for Foreign Trade of Vietnam Vietnam International Commercial Joint Stock Bank Vietnam Prosperity Joint Stock Commercial Bank
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Banks ABB ACB BANVIET
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No. 1 2 3
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AOC KA IIP
Quality LoanRatio (LR) Size CR MP 𝐸𝑥𝑐𝑒𝑠𝑠
Description Net Interest Margin
Measurement Ratio of net interest income to total interest earning assets Average Operating Cost Ratio of operational expense to total assets Equity ratio Equity to total asset Implicit interest Ratio of net non-interest expense to total payment assets Quality of Management Ratio of cost to income Loan ratio Ratio of gross loans to total assets Size of operations Logarithm of gross loans Concentration Ratio Market shares of five largest banks Monetary policy The change in average of refinance rates from the central banks Excess liquidity The deviation of M2/NGDP ratio measured alternatively by residuals of Taylor-rules regression, Hodrick–Prescott filter and Baxter-King filter.
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Variable NIM
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Appendix 2: Variable Measurement
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.3
.14
.2
.12
.1
.10
.0
.08
-.1
.06
-.2
.04
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.16
-.3
2006
2007
2008
2009
2010
2011
2013
2014
MP_level EXCESS_REG EXCESS_BK
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NIM MP EXCESS_HP
2012
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Excess
Other variables
Appendix 3: Charts – Key Variables
2015
Appendix 4: NIM Regression Results (SGMM) – Robustness Tests
𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 𝐺𝐷𝑃𝑔𝑟𝑜𝑤𝑡ℎ𝑡
F-Statistics
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Prob. Arellano-Bond test for AR(1) Prob. Arellano-Bond test for AR(2) Prob. Hansen test of overid. Prob.
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(8)
(9)
0.330*** (0.0785) 3.131** (1.338) 0.0278 (0.0302) -0.745** (0.367) -0.0602** (0.0236) -0.0374 (0.0328) -0.000183 (0.00112) 0.000853 (0.00773) 0.00124** (0.000559) -0.0269* (0.0146) -1.779** (0.808) 0.00729** (0.00299) 189 21 21
0.181* (0.0967) 4.611*** (0.722) 0.101** (0.0461) -1.200*** (0.399) -0.0472*** (0.0118) -0.115*** (0.0248) 0.00215*** (0.000811) -0.00671** (0.00288) 0.00102** (0.000495) -0.0334* (0.0197) -0.0162** (0.00770) 0.00527*** (0.00203) 147 21 21
0.124* (0.0712) 1.042 (0.899) 0.0338 (0.0602) 0.00568 (0.466) -0.0435*** (0.0135) -0.0306 (0.0205) 0.00350*** (0.00134) -0.00801*** (0.00193) 0.00120** (0.000493) 0.000988 (0.00278) -0.0648*** (0.0218) -3.188*** (0.558) 126 20 21
53879.6 0 -2.353 0.0186 -0.65 0.516 6.107 0.729
22728.9 0 -2.188 0.0287 -1.441 0.15 7.67 0.466
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Number of Observations Number of Instruments Number of Groups
(7)
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𝐴𝑂𝐶𝑖𝑡
Excess measured by Hodrick–Prescott
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𝑁𝐼𝑀𝑖𝑡−1
Excess measured by Baxter–King
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𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
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SGMM Estimation
Standard errors in parentheses
26463.7 0 -3.222 0.00127 0.812 0.417 6.336 0.706
*** p<0.01, ** p<0.05, * p<0.1
𝑆𝑖𝑧𝑒 is measured as log of total assets. MP is the level of policy interest rate. GDPgrowth refers to real GDP growth rate.
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Appendix 5: NIM Regression Results (FEM) – Robustness Tests
𝐴𝑂𝐶𝑖𝑡 𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
𝐺𝐷𝑃𝑔𝑟𝑜𝑤𝑡ℎ𝑡
(10)
(11)
(12)
1.731*** (0.399) 0.0477 (0.0368) -0.141 (0.225) -0.0515*** (0.00638) -0.0677*** (0.00923) 0.00263 (0.00234) -0.00177 (0.00437) 0.00135*** (0.000366) -0.0191** (0.00699) -1.024** (0.397) 0.000183 (0.00169) 0.0273 (0.0480) 210 0.675 0.621 0.652 44.50 0.000
2.998*** (0.402) 0.0412 (0.0309) -0.305 (0.208) -0.0509*** (0.00740) -0.0820*** (0.0116) -0.000743 (0.00292) -0.00264 (0.00332) 0.00134** (0.000543) -0.0259 (0.0176) -1.740** (0.683) 0.00598** (0.00278) 0.0447 (0.0514) 147 0.800 0.765 0.786 83.37 0.000
1.730*** (0.361) 0.0485 (0.0373) -0.199 (0.231) -0.0548*** (0.00643) -0.0654*** (0.00933) 0.00318 (0.00229) -0.00753** (0.00323) 0.000703** (0.000324) -0.0432*** (0.00999) -0.00613 (0.00492) -0.000840 (0.00143) 0.0339 (0.0433) 210 0.685 0.573 0.645 47.22 0.000
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Constant Observations R-squared within R-squared between R-squared overall F-Statistics
Excess measured by Hodrick–Prescott
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𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
Excess measured by Baxter–King
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𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
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FEM Estimation
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Prob.
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
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𝑆𝑖𝑧𝑒 is measured as log of total assets. MP is the level of policy interest rate. GDPgrowth refers to real GDP growth rate.
22
PII:
S0148-6195(19)30130-4
DOI:
https://doi.org/10.1016/j.jeconbus.2020.105893
Reference:
JEB 105893
To appear in:
Journal of Economics and Business
Received Date:
2 May 2019
Revised Date:
19 January 2020
Accepted Date:
21 January 2020
Please cite this article as: Hong Nguyen TV, Thu Pham TT, Nguyen CP, Nguyen TC, Nguyen BT, Excess Liquidity and Net Interest Margins: Evidence from Vietnamese Banks, Journal of Economics and Business (2020), doi: https://doi.org/10.1016/j.jeconbus.2020.105893
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.
Excess Liquidity and Net Interest Margins: Evidence from Vietnamese Banks By Thai Vu Hong Nguyen, Tra Thi Thu Pham, Canh Phuc Nguyen, Thanh Cong Nguyen, and Binh Thanh Nguyen
Corresponding author: Thai Vu Hong Nguyen
702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam Email: [email protected]; [email protected]
Tra Thi Thu Pham
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RMIT University, Vietnam Campus.
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Work tel: (+8423) 3776 1300 (ext 1309)
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School of Business and Management, RMIT University, Vietnam Campus.
702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam
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Canh Phuc Nguyen
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Email: [email protected]
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School of Banking, University of Economics Ho Chi Minh City. 59C Nguyen Dinh Chieu, District 3, Ho Chi Minh City, Vietnam Email: [email protected]
Thanh Cong Nguyen School of Accounting – Finance – Banking, Ho Chi Minh City University of Technology 475A Dien Bien Phu Street, Binh Thanh District, Ho Chi Minh City, Vietnam 1
Email: [email protected]
Binh Thanh Nguyen School of Business and Management, RMIT University, Vietnam Campus. 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam Email: [email protected]
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Highlights
Excess liquidity compresses net interest margins (NIM) of Vietnamese banks.
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Excess liquidity attenuates the positive impacts of policy interest rates on NIM.
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Excess liquidity is argued to make tightening monetary policy less effective.
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JEL: E40, G21
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Abstract: This study analyzes the impacts of excess liquidity in association with monetary policy rates on commercial banks’ performance – as indicated by their net interest margins (NIMs) – in Vietnam. The study finds that excess liquidity compresses NIMs and attenuates the positive impacts of policy interest rates on NIMs. The study argues that excess liquidity induces banks to reduce lending interest rates so as to facilitate credit expansion, making tightening monetary policy less effective. The study extends the monetary policy transmission literature to the context of an emerging economy with excess liquidity.
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Keywords: excess liquidity, net interest margin, monetary policy rate, emerging market, Vietnamese banks
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1. Introduction Excess liquidity has recently become a prominent phenomenon in emerging economies (Menon, 2009; Nguyen and Boateng, 2015a) – a consequence of trade and financial openness policies encouraging large capital inflows (Zhang, 2009). These capital inflows build pressures on domestic currency appreciation, and government intervention is then required to maintain a fixed exchange regime. However, currency sterilization mostly remains incomplete, resulting in large excess liquidity in banking systems (Ganley; 2004, Miura,
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2008; Zhang 2009, Nguyen and Boateng, 2015a). The literature on commercial banks’ responses to excess liquidity, which includes their lending and risk-taking behaviors, is scarce but growing (Nguyen and Boateng, 2013, 2015b; and Nguyen et al., 2018b). For example, Nguyen and Boateng (2013) employ a sample of 95
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commercial banks in China from 2000 to 2011 and find that large banks and liquid banks are
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more vulnerable to monetary policy shocks when a large excess reserve is present. Nguyen and Boateng (2015b) further find that the existence of an excess reserve incentivizes risk-
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taking behaviors among Chinese commercial banks. In addition, Nguyen et al. (2018b) argue that the accumulation of banking excess reserves encourages banks to take greater risk and
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exposes those banks to higher financing cost in the subsequent periods. Therefore, banks tend to pass tightening monetary policy rates through more rapidly (Nguyen et al., 2018b).
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This study contributes to the literature on commercial banks’ responses to excess liquidity by
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examining the outcomes of their response – especially, the impact of excess liquidity on bank performance as indicated by their net interest margins (NIMs). NIM represents the difference between interest income and interest expense, and reflects intermediation efficiency or the cost of intermediation (Almarzoqi and Naceur, 2015). NIM is measured as the ratio of net interest income to earning assets. We investigate the impact of excess liquidity on NIMs in association with monetary policy changes in Vietnam. We argue that excess liquidity induces bank managers to reduce lending interest rates so as to facilitate aggressive lending and 3
improve their remuneration. We find evidence that excess liquidity compresses NIMs. Interestingly, we also find that policy interest rates contribute positively to NIMs, yet excess liquidity attenuates the positive impact of the policy rates on NIMs. This study thus contributes to the literature on two fronts. First, it sheds lights on the impact of excess liquidity on commercial banks’ NIMs. Second, it extends the theories on monetary policy transmission to the context of an emerging economy with excess liquidity. We examine the context of Vietnam, a fast-growing economy in which banks play a key role
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in capital intermediation. Excess liquidity is a critical issue in Vietnam and considered one of the roots of high inflation (Miura, 2008; Menon, 2009; Vo and Nguyen, 2014). Capital inflows into Vietnam through both direct and indirect investments have increased sharply since Vietnam joined the World Trade Organization in 2006 (Nguyen and Nguyen, 2010).
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The surge of these strong capital inflows, coupled with large remittance flows, has surpassed
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the current account deficit (due to the trade deficit) and led to an overall capital surplus in the economy (Nguyen and Nguyen, 2010). The State Bank of Vietnam (SBV), the country’s
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central bank, has had to inject large amounts of the domestic currency to stabilize the exchange rate; however, the sterilization was neither timely nor complete, leading to excess
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liquidity in the economy and high inflation rates during the 2010s (Nguyen and Nguyen, 2010).
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As Vietnam is a transitioning economy, some argue that open market operations are
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ineffective as a monetary policy tool (Nguyen and Do, 2017). In addition, the use of interest on required reserves is limited and the SBV does not pay interest on excess reserves (Camen, 2006). According to the SBV (2010), the refinancing rate (i.e., the rate it applies to credit institutions as the lender of last resort) is its main monetary policy instrument. In other words, it is the main channel used by the SBV to manage liquidity in the economy (Camen, 2006). Therefore, the refinancing rate serves as a guide for the lending and deposit interest rates of commercial banks in Vietnam (Le and Pfau, 2009). 4
The SBV has significantly varied its refinancing rate over the past decade or so. For example, in response to inflationary pressure from the abovementioned large capital inflows, it doubled the refinancing rate from 7.5% to 15% during 2007–2008; consequently, inflation fell sharply from 28% to 5% in 2009 (Pham, 2016). In the same year, the SBV decided to cut the refinancing rate to 7% to support economic growth at the expense of increasing inflation (Pham, 2016). However, the high inflation rate (around 23% during 2010–2011) led the SBV to sharply increase the refinancing rate to 15% in 2011. Thereafter, the rate was cut gradually over 2012–2015 to the 2006 level of 6.5%. It is interesting to examine how Vietnamese
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monetary policy affected the NIMs of commercial banks during this period of excess liquidity because their lending and deposit interest rates are driven by the SBV’s refinancing rate—as opposed to other countries, in which the slope of the yield curve has a more
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important impact on NIMs (Borio et al., 2017; Alessandri and Nelson, 2015; English et al.,
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2018).
The remainder of the paper is structured as follows. Section 2 develops hypotheses. The
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methodology and data collection are discussed in Section 3. Section 4 presents the data analysis, and Section 5 concludes the study with policy implications.
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2. Hypothesis Development
The literature on the impact of policy interest rates on NIMs offers mixed conclusions, and it
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has recently regained academic interest following the global financial crisis in 2008. Borio et
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al. (2017) point out that policy rates affect NIMs via the “retail deposit endowment effect” and “quantity effect”. First, the former implies that banking markets with limited competition tend to price deposits as a markdown, and hence, the endowment effect allows banks to improve profitability during periods of high inflation. This endowment effect, which suggests the rigidity of deposit rates, is in line with Claessens et al.’s (2017) finding that low rates negatively affect NIMs as banks are reluctant to lower deposit rates quickly. Ampudia and Van den Heuvel (2019) also posit that the policy rate cuts squeeze the NIMs of Euro-area 5
banks, as they are reluctant to reduce deposit interest rates when short-term rates are already low. Second, the quantity effect argues that higher policy rates decrease bank loans, and banks then tend to expand NIMs to maintain their profitability with a smaller credit volume (Alessandri and Nelson, 2015). Borio et al. (2017) find a positive impact of the interest rate structure on the net interest income for 14 major advanced economies. Alessandri and Nelson (2015) provide evidence that policy rates contribute positively to banks’ NIMs and suggest
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that banks in the United Kingdom raise their lending interest rates and lower lending quantities in response to higher funding costs.
However, other studies finds a negative impact of policy rates on NIMs. For instance, English
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et al. (2018) provide evidence that unanticipated increases in the level or the slope of the yield curve raise the NIMs of US banks in the short term, supporting the argument of sticky
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deposit interest rates. Nevertheless, NIMs decline in the medium term when banks substitute
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core deposits with non-core liabilities, which are repriced much more quickly at higher market interest rates. The change in the composition of bank liabilities makes the overall effect on NIMs negative (English et al., 2018). In this line, Berry et al. (2019) note that
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tightening monetary policy episodes in the United States do not always lead to higher NIMs,
and loans.
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as interest rate pass-through sensitivities depend on the competitive environment of deposits
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With regard to excess liquidity, Acharya and Naqvi (2012) develop a conceptual framework under which excess liquidity creates the perception of low liquidity risk among bank managers. The perception of low liquidity risk induces bank managers to lend aggressively to improve their remuneration. Bank managers can also conceal their risk easily under the excess liquidity condition (Acharya and Naqvi, 2012). Empirical studies provide evidence that excess liquidity leads to credit expansion (Nguyen and Boateng, 2013), higher risk to 6
commercial banks (Nguyen and Boateng, 2015b), higher remuneration to bank managers (Nguyen et al., 2018a), and commercial banks’ rapid responses to tightening monetary policy (Nguyen et al., 2018b). However, the literature largely ignores the mechanism facilitating the aggressive lending process induced by excess liquidity. In this study, we argue that excess liquidity incentivizes bank managers to reduce lending interest rates to expand credit volume. Given the rigidity of deposit rates under the retail deposit endowment effect and quantity effect discussed above (Borio et al., 2017), we hypothesize that excess liquidity induces
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lower lending interest rates, and hence has an adverse effect on NIMs. In addition, we argue that excess liquidity attenuates the impact of the policy rates on NIMs. Under the retail deposit endowment effect and the quantity effect, deposit rates tend to be rigid; therefore, higher policy rates lead to higher NIMs thanks to the increase in lending
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interest rates (Borio et al., 2017). We argue that in the presence of excess liquidity, banks
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tend to reduce lending interest rates to expand credit volume, and this makes lending interest rates less responsive to policy rates. Therefore, both the retail deposit endowment effect and
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the quantity effect will be less effective. However, under the composition effect, in response to tightening monetary policy, banks substitute deposits with non-core liabilities, which
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would adjust quickly to higher interest rates and thus have a negative effect on NIMs (English et al., 2018). We argue that in an environment of excess liquidity, banks may not
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feel the need to switch to other types of funding and would not reprice short-term liabilities quickly, making the composition effect less likely. Hence, the presence of excess liquidity
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would attenuate the negative impact of monetary policy rate on NIMs. Therefore, we hypothesize that excess liquidity lessens the impacts of monetary policy rates on NIMs. 3. Methodology and Data 3.1 Data We collect banking data from Fitch’s International Bank Database (Bankscope) for the period from 2006 – when Vietnam joined the World Trade Organization and began to experience 7
huge capital inflows (Nguyen and Nguyen, 2010) – to 2015. We consider only commercial banks and exclude other bank types as they are less profit-oriented. After removing samples with missing data from the population of 31 banks, our final unbalanced sample consists of 21 banks with 210 annual observations; Gambacorta (2005) finds evidence that adopting an annual frequency is sufficient to capture the heterogeneity in Italian banks’ lending adjustment to monetary policy. Appendix 1 lists the banks in our sample. Macro data are collected from the World Bank Database.
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3.2 Econometric Model Following Ho and Saunders (1981) and Zhou and Wong (2008), we use the following model to capture the impact of excess liquidity on NIMs:
NIM it 0 1 NIM it 1 2 AOCit 3 KAit 4 IIPit 5Qualityit 6 LRit 7 Sizeit
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NIM it 0 8CRt 9 MPt 10 Excesst 11MPt Excesst it
(1)
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Where NIM is net interest margin. Following Zhou and Wong (2008), we control for bank specific characteristics by including average operating cost (AOC), equity ratio (KA), implicit
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interest payments (IIP), quality of management (Quality), credit risk measured as the ratio of net loans to total assets (LR), and size of operations (Size). We also control for concentration
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ratio (CR). MP is the change in monetary policy rates. Prior studies argue that the refinancing rate is an effective policy rate and reflects the monetary policy stance in Vietnam (Vo and
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Nguyen, 2017). We provide the variable measurement details in Appendix 2.
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We measure excess liquidity (Excess) using three alternative methods based on the ratio of broad money M2 to nominal gross domestic product (M2/NGDP), which is common in the literature on excess liquidity (see Zhang, 2009). For the first method, we estimate the theoretical equilibrium in the money market under Taylor rules (Litterman and Weiss, 1983; Taylor, 1993; Woodford, 2001; Orphanides, 2003). The theoretical equilibrium in the money market is predicted by three main explanatory variables, including the real GDP per capita growth rate (GDPpcgrowth) to capture national income changes, inflation (Inflation), and the 8
real interest rate (RIR). The World Bank defines real interest rate as the lending interest rate adjusted for inflation based on the GDP deflator. We collect the residuals (µ𝑡 ) from the regression model (2) to reflect the excess liquidity condition. M 2t 0 1 RIRt 2 Inflationt 3GDPpcGrowtht t NGDPt
(2)
For the second and third methods, we identify the trend of the M2/NGDP ratio by the Hodrick–Prescott filter (HP) (Hodrick and Prescott, 1997) and the Baxter-King filter (BK) (Baxter and King, 1999). The deviation from the HP and BK trends reflect excess liquidity
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conditions. Therefore, we form the series of excess liquidity by taking the deviations from those two alternative filters.
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To deal with the endogeneity arising from the correlation between the lagged dependent variable and the error term, we employ the system generalised method of moments (SGMM)
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estimator developed by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998) for the main regression (1). Blundell and Bond (1998) designed the SGMM
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estimator to augment GMM (also called the difference GMM) by estimating simultaneously in differences and levels. The equation in differences is instrumented by the lagged values,
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while the equation in levels is instrumented by the first difference of the lagged values (Roodman, 2009). We apply the SGMM rather than the GMM as the former is more efficient
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for unbalanced panels (Roodman, 2009). The impact of excess liquidity associated with the monetary policy rates on NIMs is captured by the interaction term. The model residuals are
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free of serial correlation and unit roots by applying Arellano-Bond tests (Arellano and Bond, 1991) and Agumented Dickey-Fuller test (Greene, 2002). To ensure robustness of the results, we also employ the fixed effects model (FEM) to capture individual bank effects. Table 1 summarizes data descriptions. The NIMs fluctuate between 0.7% to 11%. The maximum values of excess liquidity span between 14.8 to 19.8 of NGDP across alternative
9
measurement methods. The monetary policy rate varies between 6.5% and 15% over the sample period. Appendix 3 presents the charts of the key variables. [Insert Table 1 here] 4. Estimation Results [Insert Tables 2 and 3 here] Table 2 reports the regression results for SGMM. The F-test results reject the hypothesis that the independent variables are jointly equal to zero. The Arellano-Bond tests, AR(1) and
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AR(2), indicate first-order serial correlation, but no second-order serial correlation in the residuals (Arellano and Bond, 1991). The Hansen values (not equal to 1) ensure the
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appropriateness of the models (Roodman, 2009). Table 3 reports the results for the FEM.
With regard to the control variables, banks with high operating costs, high equity ratios, and
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large size tend to have higher NIMs. Meanwhile, banks with high non-interest expenses, high cost-to-income ratios, and high loan-to-total-assets ratios tend to maintain lower NIMs. In
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addition, we find that higher concentration decreases NIMs.
With regard to monetary policy and excess liquidity, we find that the coefficients of monetary
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policy rate change (MP) are positive and significant across the regressions. This result indicates that the change in monetary policy rates contributes positively to NIMs. This
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finding is in line with studies stating that banks shrink credit supply and increase lending
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interest rates more rapidly compared with deposit rates to maintain profitability when monetary policy is tightened. It further aligns with the findings of Alessandri and Nelson (2015) and Borio et al. (2017), who show a positive relationship between policy rates and NIMs in advanced economies. It is also consistent with the argument that deposit interest rates tend to be rigid in the short term (English et al., 2018; Berry, et al., 2019). On the contrary, this finding does not support the view that banks substitute deposits with non-core liabilities, which may adjust quickly to the market rates in the medium term (English et al., 10
2018), as deposits remain the main source of funding for Vietnamese banks (Tran et al., 2015). This result implies that banks can pass tightening monetary policy rates through to borrowers; therefore, the SBV should consider the interest rate burden when firms borrow during tightening policy regimes. We also find that the coefficients of excess liquidity (Excess) are consistently negative, and the results are statistically significant. These results suggest that the excess liquidity condition compresses NIMs. This finding supports our hypothesis that excess liquidity induces bank
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managers to reduce lending interest rates to facilitate aggressive lending, and hence negatively affects NIMs. In addition, the significantly negative coefficients of the interaction term indicate that the presence of excess liquidity attenuates the positive impact of the monetary policy rates on NIMs. This result supports our argument that excess liquidity makes
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monetary policy transmission less effective, as lending interest rates tend to be less
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responsive to higher policy interest rates. This finding aligns with the evidence presented by Nguyen et al. (2018b) that liquid banks tend to adjust lending interest rates sluggishly in
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response to tightening monetary policy in China. Therefore, the liquidity condition in the economy should be considered to ensure the effectiveness of tightening monetary policy.
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We can derive the total effects of monetary policy (MP Total) and excess liquidity (Excess Total) on NIMs using the means of Excess and MP, respectively, as follows:
∂MP
= β9 +
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∂NIM
∂NIM
β11 × 𝐸𝑥𝑐𝑒𝑠𝑠 , and
∂Excess
= β10 + β11 × 𝑀𝑃. The results show that the interaction term
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accentuates the negative impact of excess liquidity on NIMs. The total effect of monetary policy rate turns negative due to the strong negative effect of excess liquidity, except for the SGMM estimation with excess liquidity measured by the regression method (regression 1). To conduct the robustness check, we measure bank size (𝑆𝑖𝑧𝑒) using the log of total assets as an alternative to the log of gross loans. We include the real GDP growth rate to capture the demand for loans and examine the policy interest rate level as an alternative to the policy rate 11
change. We conduct these robustness regressions using both the SGMM and the FEM estimators. Appendixes 4 and 5 provide the robustness results, which are in line with those of the main regressions in Tables 2 and 3. 5. Conclusion This study examines the impacts of excess liquidity in association with monetary policy rates on NIMs. We find that higher monetary policy interest rates expand NIMs, while excess liquidity compresses NIMs. In addition, excess liquidity attenuates the positive impacts of
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monetary policy interest rates on NIMs. These results indicate that excess liquidity tends to induce banks to reduce lending interest rates so as to expand credit supply. This behavior negatively affects NIMs and makes monetary policy transmission less effective when policy
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rates increase.
We suggest that central banks reduce excess liquidity in the economies to maintain healthy
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profitability in the banking systems and financial stability. In addition, they should conduct
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prudent monetary policies, as a tightening regime may be transferred to the economy sluggishly. Future studies should consider the effect of excess liquidity lags to capture risk taking in previous periods in response to concurrent monetary policy changes, as Nguyen et
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funding sources.
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al. (2018b) suggest. Future studies should also consider the impacts of excess liquidity on
12
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, 106(2), 349-366. Alessandri, P. and Nelson, B.D. (2015). Simple banking: profitability and the yield curve. Journal of Money, Credit and Banking, 47(1), 144-175. Almarzoqi, R.A. and Naceur, S.B. (2015), “Determinants of bank interest margins in the Caucasus and Central Asia”, IMF Working Paper No. WP/15/87, International Monetary Fund, Washington, DC. Ampudia, M., and Van den Heuvel, S. J. (2019). Monetary policy and bank equity values in a time of low and negative interest rates. Finance and Economics Discussion Series. Board of Governors of the Federal Reserve System (U.S.).
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Arellano, M., and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297. Arellano, M., and Bover, O. (1995). Another look at the instrumental variables estimation of error components models, Journal of Econometrics, 68(1), 29-51. Baxter, M. and King, R.G. (1999). Measuring business cycles: approximate bank-pass filters for economic time series. Review of Economics and Statistics, 81, 575-593.
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Berry, J., Ionescu, F., Kurtzman, R., and Zarutskie, R. (2019). Changes in Monetary Policy and Banks' Net Interest Margins: A Comparison across Four Tightening Episodes. FEDS Notes. Board of Governors of the Federal Reserve System (U.S.).
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Blundell, R., and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115-143.
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Borio, C., Gambacorta, L. and Hofmann, B. (2017). The influence of monetary policy on bank profitability. International Finance, 20, 48-63. Camen, U. (2006). Monetary Policy in Vietnam: The Case of a Transition Country. Bank for International Settlements (BIS) Papers, No. 31.
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Claessens, S., Coleman, N. and Donnelly, M. (2017). “Low-for-long” interest rates and banks’ interest margins and profitability: cross-country evidence. Journal of Financial Intermediation, forthcoming, https://doi.org/10.1016/j.jfi.2017.05.004
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English, W. B., Van den Heuvel, S. J., and Zakrajšek, E. (2018). Interest Rate Risk and Bank Equity Valuations. Journal of Monetary Economics, 98, 80‐97.
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Gambacorta, L. (2005). Inside the bank lending channel. European Economic Review, 49(7), 1737-1759. Ganley, J. (2004). Surplus liquidity: Implications for central banks. London: Centre for Central Banking Studies, Bank of England. Greene, W. H. (2002). Econometric Analysis (5th ed.). Prentice Hall: New Jersey. Ho, T., and Saunders, A. (1981). The determinants of banks interest margins: theory and empirical evidence. Journal of Financial and Quantitative Analysis, 16(4), 581-600. Hodrick, R. and Prescott, E.C. (1997). Postwar U.S. business cycles: an empirical investigation. Journal of Money, Credit and Banking, 29(1), 1-16.
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Le, V. H., and Pfau, W. D. (2009). VAR Analysis of the Monetary Transmission Mechanism in Vietnam. Applied Econometrics and International Development, 9(1), 165-179. Litterman, R. B., and Weiss, L. (1983). Money, real interest rates, and output: A reinterpretation of postwar US data. NBER Working Paper No. 1077 Menon, J. (2009). Managing success in Vietnam: macroeconomic consequences of large capital inflows with limited instruments. ASEAN Economic Bulletin, 26(1), 77-95. Miura, J. (2008). Is Vietnam on the brink of a currency crisis? Deciphering signs of increased vulnerability resulting from globalization. Pacific Business and Industries, VIII(29),1-28. Nguyen, D. T. and Do, T. B. H. (2017). The interest rate channel of SBV’s monetary policy: An empirical analysis. Vietnam Economist Annual Meeting Conference Paper. Nguyen, V. H. T. and Boateng, A. (2013). The Impact of Excess Reserves beyond Precautionary Levels on Bank Lending Channels in China, Journal of International Financial Markets, Institutions and Money, 26 (C), 358-377.
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Nguyen, V. H. T. and Boateng, A. (2015a). Bank excess reserves in emerging economies: A critical review and research agenda, International Review of Financial Analysis, 39, 158166. Nguyen, V. H. T. and Boateng, A. (2015b). An Analysis of Involuntary Excess Reserves, Monetary Policy and Risk-taking Behaviour of Chinese Banks, International Review of Financial Analysis, 37, 63-72.
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Nguyen, V. H. T., Boateng, A. and Nguyen, C.T. (2018a). Involuntary excess reserve and bankers’ remuneration: evidence from Chinese banks, Applied Economics Letters, 25(8), 518-522.
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Nguyen, V. H. T., Boateng, A. and Pham, T.T.T. (2018b). Involuntary excess reserve and heterogeneous transmission of policy rates to bank lending rates in China, Empirical Economics, in press. https://doi.org/10.1007/s00181-018-1468-x
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Nguyen, T. T. H. and Nguyen, D. T. (2010) Macroeconomic Determinants of Vietnam’s inflation 2000–2010: Evidence and Analysis’, December, Vietnam Centre for Economic and Policy Research, University of Economics and Business, Vietnam National University, Hanoi, Vietnam.
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Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of monetary Economics, 50(5), 983-1022.
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Pham, A. T. (2016). Monetary Policies and the Macroeconomic Performance of Vietnam. PhD Thesis, Queensland University of Technology.
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Roodman, David. (2009). Practitioners’ Corner - A Note on the Theme of Too Many Instruments. Oxford Bulletin of Economics and Statistics, 71(1), 135-158. State Bank of Vietnam. (2010). Law on the State Bank of Vietnam. https://www.sbv.gov.vn/webcenter/portal/en/home/sbv/mpolicy/mpt?_afrLoop=291773149024695 77#%40%3F_afrLoop%3D29177314902469577%26centerWidth%3D80%2525%26leftWidth%3 D20%2525%26rightWidth%3D0%2525%26showFooter%3Dfalse%26showHeader%3Dfalse%26 _adf.ctrl-state%3Dxaa72glfs_100
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39(1), 195–214 Tran, B. T., Ong, B. and Weldon, S. (2015). Vietnam Banking Industry Report, Duxton Asset Management. 14
Vo, X. V., & Nguyen, P. C. (2014). Monetary Policy and Bank Credit Risk in Vietnam Pre and Post Global Financial Crisis. Contemporary Studies in Economic and Financial Analysis, 96, 277 - 290. Vo, X. V., & Nguyen, P. C. (2017). Monetary policy transmission in Vietnam: evidence from a VAR approach. Australian Economic Papers, 56(1), 27-38. Woodford, M. (2001). The Taylor rule and optimal monetary policy. American Economic Review, 91(2), 232-237. Zhang, C. (2009). Excess liquidity, inflation and the yuan appreciation: What can China learn from recent history? The World Economy, 32(7), 998–1018.
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Zhou, K. and Wong, M.C.S. (2008). The determinants of net interest margins of commercial banks in Mainland China. Emerging Markets Finance & Trade, 44(5), 41-53.
15
Tables
Mean
Std. Dev.
Min
Max
NIM it
0.05253
0.01782
-0.00703
0.11133
AOCit
0.01552
0.00594
-0.00396
0.05990
KAit
0.11988
0.07007
-0.03704
0.46260
IIPit
0.02414
0.01137
-0.00487
0.08953
Qualityit
0.47600
0.16319
-0.13476
1.40815
LRit
0.52116
0.13342
-0.19429
0.85168
Sizeit
17.5895
1.43345
-13.5720
20.5615
CRt
0.46573
0.08582
MPt
0.00150
0.03144
MPt - level
0.08425
2.54094
Excesst by Regression
0.05392
0.08774
Excesst by BK filter
0.09211
0.05602
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Table 1: Summary Statistics for NIM Regression Variables
Excesst by HP filter
0.03317
0.14097
-0.37200
0.65960
-0.06000
0.06000
0.06500
0.15000
-0.07218
0.19839
-0.00963
0.14816
-0.25446
0.18460
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Variable
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Table 2: NIM Regression Results (SGMM)
𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
F-Statistics
(2)
(3)
0.00757 (0.0663) 4.953*** (0.569) 0.0498** (0.0202) -1.255*** (0.268) -0.0708*** (0.0115) -0.0873*** (0.0218) 0.00463*** (0.000438) -0.00561** (0.00269) 0.0604** (0.0234) -0.00669 (0.0151) -0.742** (0.339) 0.02 -0.0078 189 19 21
0.0978 (0.121) 4.499*** (0.632) 0.0921** (0.0371) -1.161*** (0.270) -0.0699*** (0.0135) -0.0757*** (0.0218) 0.00435*** (0.000567) -0.0129*** (0.00363) 0.0744** (0.0317) -0.0260* (0.0131) -2.289* (1.262) -0.136 -0.029 147 20 21
0.0932* (0.0472) 1.360 (1.052) 0.0816 (0.0730) -0.199 (0.421) -0.0512*** (0.0102) -0.0259 (0.0269) 0.00443*** (0.000801) -0.00894*** (0.00241) 0.0710** (0.0298) -0.0952*** (0.0182) -4.260*** (0.889) -0.07 -0.102 147 19 21
1937.68 -0.000 -2.720 -0.006 -0.150 -0.878 -8.330 -0.501
-425.5 -0.000 -2.230 -0.025 -0.970 -0.333 -4.030 -0.854
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𝑀𝑃𝑡 (𝑇𝑜𝑡𝑎𝑙) 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 (Total) Number of Observations Number of Instruments Number of Groups
(1)
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𝐴𝑂𝐶𝑖𝑡
Excess measured by Hodrick–Prescott
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𝑁𝐼𝑀𝑖𝑡−1
Excess measured by Baxter–King
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𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
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SGMM Estimations
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Prob. Arellano-Bond test for AR(1) Prob. Arellano-Bond test for AR(2) Prob. Hansen test of overid. Prob. Standard errors in parentheses
1911.51 -0.000 -2.970 -0.003 -0.590 -0.558 -10.93 -0.206
*** p<0.01, ** p<0.05, * p<0.1
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Table 3: NIM Regression Results (FEM)
𝐴𝑂𝐶𝑖𝑡 𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
Excess measured by Hodrick–Prescott
(4)
(5)
(6)
1.993*** (0.240) 0.0667*** (0.0166) -0.287** (0.126) -0.0587*** (0.00603) -0.0710*** (0.00851) 0.00511*** (0.00120) -0.00427 (0.00517) 0.0568* (0.0295) -0.0279*** (0.0103) -1.598*** (0.449) -0.00152 (0.0239) -0.03 -0.03 210 0.661 0.661 0.475 0.599 0.000
3.206*** (0.328) 0.0707** (0.0282) -0.412*** (0.126) -0.0558*** (0.00578) -0.0839*** (0.0112) 0.00438 (0.00319) -0.00929** (0.00404) 0.0399* (0.0208) -0.0489*** (0.0172) -1.697 (1.071) 0.00657 (0.0588) -0.12 -0.05 147 0.793 0.793 0.758 0.776 0.000
2.026*** (0.247) 0.0685*** (0.0189) -0.275** (0.126) -0.0638*** (0.00608) -0.0719*** (0.00860) 0.00766*** (0.00258) -0.00821** (0.00405) 0.00890 (0.0247) -0.0146 (0.0132) -0.906*** (0.309) -0.0428 (0.0475) -0.02 -0.01 210 0.647 0.647 0.268 0.49 0.000
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𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
Excess measured by Baxter–King
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𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
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FEM Estimations
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Constant
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𝑀𝑃𝑡 (𝑇𝑜𝑡𝑎𝑙) 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 (Total) Observations R-squared within R-squared between R-squared overall F-Statistics
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Prob.
*** p<0.01, ** p<0.05, * p<0.1
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Standard errors in parentheses
18
Appendices Appendix 1: List of Banks
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
CTG EXIM HDB KLB MB MSB NAB NVB OCB PGB SAIGON SEABANK TCB VAB VCB VIB VPBANK
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BIDV
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4
Full names An Binh Commercial Joint Stock Bank Asia Commercial Bank Viet Capital Bank The Joint Stock Commercial Bank for Investment and Development of Vietnam Vietnam Joint Stock Commercial Bank for Industry and Trade Vietnam Export Import Commercial Joint-Stock Bank Ho Chi Minh City Housing Development Joint Stock Bank Kienlong Commercial Joint Stock Bank Military Commercial Joint Stock Bank Vietnam Maritime Commercial Joint Stock Bank National Australia Bank National Citizen Commercial Joint Stock Bank Orient Commercial Joint Stock Bank Petrolimex Group Commercial Joint Stock Bank Saigon Commercial Bank Southeast Asia Commercial Joint Stock Bank Vietnam Technological and Commercial Joint Stock Bank Vietnam Asia Commercial Joint Stock Bank Joint Stock Commercial Bank for Foreign Trade of Vietnam Vietnam International Commercial Joint Stock Bank Vietnam Prosperity Joint Stock Commercial Bank
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Banks ABB ACB BANVIET
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No. 1 2 3
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AOC KA IIP
Quality LoanRatio (LR) Size CR MP 𝐸𝑥𝑐𝑒𝑠𝑠
Description Net Interest Margin
Measurement Ratio of net interest income to total interest earning assets Average Operating Cost Ratio of operational expense to total assets Equity ratio Equity to total asset Implicit interest Ratio of net non-interest expense to total payment assets Quality of Management Ratio of cost to income Loan ratio Ratio of gross loans to total assets Size of operations Logarithm of gross loans Concentration Ratio Market shares of five largest banks Monetary policy The change in average of refinance rates from the central banks Excess liquidity The deviation of M2/NGDP ratio measured alternatively by residuals of Taylor-rules regression, Hodrick–Prescott filter and Baxter-King filter.
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Variable NIM
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Appendix 2: Variable Measurement
19
.3
.14
.2
.12
.1
.10
.0
.08
-.1
.06
-.2
.04
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.16
-.3
2006
2007
2008
2009
2010
2011
2013
2014
MP_level EXCESS_REG EXCESS_BK
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NIM MP EXCESS_HP
2012
20
Excess
Other variables
Appendix 3: Charts – Key Variables
2015
Appendix 4: NIM Regression Results (SGMM) – Robustness Tests
𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡 𝐺𝐷𝑃𝑔𝑟𝑜𝑤𝑡ℎ𝑡
F-Statistics
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Prob. Arellano-Bond test for AR(1) Prob. Arellano-Bond test for AR(2) Prob. Hansen test of overid. Prob.
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(8)
(9)
0.330*** (0.0785) 3.131** (1.338) 0.0278 (0.0302) -0.745** (0.367) -0.0602** (0.0236) -0.0374 (0.0328) -0.000183 (0.00112) 0.000853 (0.00773) 0.00124** (0.000559) -0.0269* (0.0146) -1.779** (0.808) 0.00729** (0.00299) 189 21 21
0.181* (0.0967) 4.611*** (0.722) 0.101** (0.0461) -1.200*** (0.399) -0.0472*** (0.0118) -0.115*** (0.0248) 0.00215*** (0.000811) -0.00671** (0.00288) 0.00102** (0.000495) -0.0334* (0.0197) -0.0162** (0.00770) 0.00527*** (0.00203) 147 21 21
0.124* (0.0712) 1.042 (0.899) 0.0338 (0.0602) 0.00568 (0.466) -0.0435*** (0.0135) -0.0306 (0.0205) 0.00350*** (0.00134) -0.00801*** (0.00193) 0.00120** (0.000493) 0.000988 (0.00278) -0.0648*** (0.0218) -3.188*** (0.558) 126 20 21
53879.6 0 -2.353 0.0186 -0.65 0.516 6.107 0.729
22728.9 0 -2.188 0.0287 -1.441 0.15 7.67 0.466
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Number of Observations Number of Instruments Number of Groups
(7)
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𝐴𝑂𝐶𝑖𝑡
Excess measured by Hodrick–Prescott
-p
𝑁𝐼𝑀𝑖𝑡−1
Excess measured by Baxter–King
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𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
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SGMM Estimation
Standard errors in parentheses
26463.7 0 -3.222 0.00127 0.812 0.417 6.336 0.706
*** p<0.01, ** p<0.05, * p<0.1
𝑆𝑖𝑧𝑒 is measured as log of total assets. MP is the level of policy interest rate. GDPgrowth refers to real GDP growth rate.
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Appendix 5: NIM Regression Results (FEM) – Robustness Tests
𝐴𝑂𝐶𝑖𝑡 𝐾𝐴𝑖𝑡 𝐼𝐼𝑃𝑖𝑡 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑖𝑡 𝐿𝑅𝑖𝑡 𝑆𝑖𝑧𝑒𝑖𝑡 𝐶𝑅𝑡 𝑀𝑃𝑡 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
𝐺𝐷𝑃𝑔𝑟𝑜𝑤𝑡ℎ𝑡
(10)
(11)
(12)
1.731*** (0.399) 0.0477 (0.0368) -0.141 (0.225) -0.0515*** (0.00638) -0.0677*** (0.00923) 0.00263 (0.00234) -0.00177 (0.00437) 0.00135*** (0.000366) -0.0191** (0.00699) -1.024** (0.397) 0.000183 (0.00169) 0.0273 (0.0480) 210 0.675 0.621 0.652 44.50 0.000
2.998*** (0.402) 0.0412 (0.0309) -0.305 (0.208) -0.0509*** (0.00740) -0.0820*** (0.0116) -0.000743 (0.00292) -0.00264 (0.00332) 0.00134** (0.000543) -0.0259 (0.0176) -1.740** (0.683) 0.00598** (0.00278) 0.0447 (0.0514) 147 0.800 0.765 0.786 83.37 0.000
1.730*** (0.361) 0.0485 (0.0373) -0.199 (0.231) -0.0548*** (0.00643) -0.0654*** (0.00933) 0.00318 (0.00229) -0.00753** (0.00323) 0.000703** (0.000324) -0.0432*** (0.00999) -0.00613 (0.00492) -0.000840 (0.00143) 0.0339 (0.0433) 210 0.685 0.573 0.645 47.22 0.000
na
lP
Constant Observations R-squared within R-squared between R-squared overall F-Statistics
Excess measured by Hodrick–Prescott
re
𝑀𝑃𝑡 × 𝐸𝑥𝑐𝑒𝑠𝑠𝑡
Excess measured by Baxter–King
ro of
𝑁𝐼𝑀𝑖𝑡
Excess measured by Regression
-p
FEM Estimation
ur
Prob.
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Jo
𝑆𝑖𝑧𝑒 is measured as log of total assets. MP is the level of policy interest rate. GDPgrowth refers to real GDP growth rate.
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