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Is there a financial accelerator in European banking?
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Finance Research Letters 0 0 0 (2016) 1–4
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Is there a financial accelerator in European banking? Yener Altunbas¸ a, Caterina Di Tommaso b, John Thornton a,∗ a b
Bangor University, College Road, Bangor LL57 2DG, United Kingdom University of Calabria, Via Pietro Bucci, 87036 Arcavacata, Rende CS, Italy
a r t i c l e
i n f o
Article history: Received 31 January 2016 Accepted 10 March 2016 Available online xxx JEL classification numbers: E32 E44
a b s t r a c t We show that price-cost margins for European banks are countercyclical after controlling for monetary policy, interest rate risk, and several banking industry and bank-specific factors. Our results support the existence of a “financial accelerator” at work in European economies. © 2016 Published by Elsevier Inc.
Keywords: Banks’ margins Business cycles Financial accelerator
1. Introduction Bernanke and Gertler (1989) and Bernanke, Gertler and Gilchrist (1996) showed how the effects of a real shock (such as a shock to productivity) on financial conditions could lead to persistent fluctuations in the economy, even if the initiating shock had little intrinsic persistence. A key concept in their analysis was a positive external finance premium, defined as the difference between the cost to a borrower of raising funds externally and the opportunity cost of internal funds. The theory predicts that the external finance premium that a borrower must pay should depend inversely on the strength of the borrower’s financial position, measured in terms of factors such as net worth, liquidity, and current and future expected cash flows. The inverse relationship of the external finance premium and the financial condition of borrowers creates a channel through which otherwise short-lived economic shocks may have long-lasting effects. In the hypothetical case that Bernanke and Gertler (1989) analyzed, an increase in productivity that improves the cash flows and balance sheet positions of firms leads in turn to lower external finance premiums in subsequent periods, which extends the expansion as firms are induced to continue investing even after the initial productivity shock has dissipated. This “financial accelerator” effect applies in principle to any shock that affects borrower balance sheets or cash flows. The concept can help to explain the persistence and amplitude of cyclical fluctuations in a modern economy. Some recent empirical work testing the financial accelerator hypotheses has focused on developments in banks’ pricecost margins on the assumption that they are a proxy for the external finance premium that banks charge to firms.1 The accelerator hypothesis implies that these margins are counter-cyclical thereby making bank credit more expensive during economic downturns than would be the case in an economy with constant bank margins. For example, Aliaga-Díaz and ∗
1
Corresponding author. Tel.: +4401244 388545; fax: +4401244579576. E-mail address: [email protected] (J. Thornton). The basis for this assumption is that banks’ marginal cost of funds are a good proxy for firms’ marginal cost of internal funds.
http://dx.doi.org/10.1016/j.frl.2016.03.020 1544-6123/© 2016 Published by Elsevier Inc.
Please cite this article as: Y. Altunbas¸ et al., Is there a financial accelerator in European banking? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.03.020
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Olivero (2010) apply a VAR forecast error-based methodology to measure the co-movement between bank margins and output to a panel of US banks over 1984–2005 and report that margins are countercyclical with respect to GDP and bank loans after controlling for monetary policy and credit risk. Aliaga-Díaz and Olivero (2011) apply more traditional regression techniques to a panel of US banks over 1979–2005 and find that margins are counter-cyclical with respect to per capita GDP and bank loans. Outside of the US, Turgutlu (2010) reports weak evidence of the counter-cyclicality of bank margins with respect to bank loans and no evidence of counter-cyclicality with respect to GDP and GDP per capita from a panel of 36 Turkish commercial banks over 20 01–20 08, though the study covers barely one full business cycle. In this paper, we add to a sparse empirical literature by reporting on the cyclicality of banks’ price–cost margins in a panel dataset of European banks. Our results provide strong support the existence of a “financial accelerator” at work in European economies. 2. Methodology and data We model the cyclicality of bank margins as follows:
yi,t = β1 ∗ yi−1,t + β2 ∗ log (Xt ) +
k1 j=1
γ j ∗ Z j,t +
k1
δi ∗ Wi,t + εi,t
(1)
i=1
In Eq. (1), i and j are banks and country, respectively, for which we use annual bank-level data from balance sheets and income statements from Bankscope for 1133 banks across 15 European countries for 1989 to 2012.2 yi, t is banks’ price-cost margins measured as the difference between either: (i) the ratio of interest income on loans to the volume of loans and the ratio of interest rate expense on deposits to the volume of deposits (Margin 1); (ii) the ratio of net interest revenue to the volume of loans and the ratio of net interest expense on deposits to the volume of deposits (Margin 2); and (iii) as the ratio to total assets of interest income on loans less interest expenses on deposits (Margin 3). Xt is the business cycle indicator, measured either by developments in the output gap for each country as reported in the IMF’s World Economic Outlook database, or by total bank loans, which we cyclically adjust using the Hodrick-Prescott filter and include as an alternative because many spending aggregates depend substantially on bank financing (Aliaga-Díaz and Olivero, 2011). Zj, t is a vector of economic variables that includes the central bank policy interest rate, included because monetary policy shocks impact positively on margins reflecting the inertia of deposit rates (Hannan and Berger, 1991; Neumark and Sharpe, 1992), and interest rate risk, which we represent by the standard deviation of the 3-month Treasury bill rate in each country, and include because banks may charge a premium to compensate for risk (Ho and Saunders, 1981). Finally, Wi, t is a vector of bank industry and bank-specific variables that includes: market concentration, measured by the Herfindahl-Hirschman index, because it may impact on bank risk (Beck, Demirgüç-Kunt and Levine, 2006); credit quality, measured as the ratio of loan loss provisions to total loans and is included because an increase in credit default rates may lead banks to increase their margins, (Demirgüç-Kunt and Huizinga, 1999), bank size, measured by total bank assets and included because larger banks have more possibility to diversify, which could reduce their cost of credit and lead to a narrowing of margins (Maudos and de Guevara, 2004); bank liquidity, measured as the ratio of cash plus securities to total assets and included because banks that choose to hold more liquid portfolios pay for the cost of that liquidity by raising their margins (Ho and Saunders, 1981); bank specialization, measured by the ratio of bank deposits to total assets and included because deposits are relatively interest-inelastic so that the margins of banks that specialize in deposit taking may be more isolated against economic shocks (Berlin and Mester, 1999); and capital, measured by the ratio of capital to total assets, and included as more capitalized banks may charge higher margins if holding equity is more costly than holding debt, for example, because of the latter’s more favourable tax treatment (Adrian and Shin, 2010). Finally, we include two 0–1 dummy variables that seek to capture, respectively, the impact on margins of the adoption of the Basel 11 Accord by European bank regulators from 2008 that forced a recalculation of credit risk to determine minimum capital, and the launch of the euro in 1999 that may have impacted the margins of banks in euro-adopting countries, for example by reducing risk. Descriptive statistics for the variables are presented in Table 1. The relationship between bank margins and developments in the business cycle might be driven by reverse causation. For example, Aliaga-Diaz and Olivero (2011) note that economic activity might be a function of the cost of bank credit, as proxied by bank margins. To control for potential reverse causation we estimate Eq. (1) using the generalized-methodsof moments (GMM) panel estimator developed for dynamic models by Arellano and Bond (1991) and Arellano and Bover (1995). 3. Results GMM results for the three measures of bank margins are reported in Table 2. In all but one case the coefficients on the business cycle indicators are highly statistically significant and negative—i.e., the price-cost margins of European banks are counter-cyclical after controlling for monetary policy, interest rate risk, and bank industry and bank specific factors. In each
2 The countries are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom.
Please cite this article as: Y. Altunbas¸ et al., Is there a financial accelerator in European banking? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.03.020
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Table 1 Descriptive statistics. Variables Dependent variables: Margin 1 Margin 2 Margin 3 Business cycle indicators Output gap (% deviation from trend) Bank loans (detrended, millions) Other independent variables: Policy interest rate (%) Interest rate risk Herfindahl-Hirschman index Credit quality ratio Total assets (in logs) Liquidity ratio Deposit ratio Capital ratio
Mean
Median
Standard deviation
Minimum
Maximum
2.67 1.64 2.46
2.49 1.17 2.36
1.99 1.76 1.57
0.00 0.00 0.01
45.90 24.08 50.42
–0.27 12.80
-0.28 11.00
1.95 6.72
-8.76 3.80
10.88 23.00
2.78 0.38 0.13 0.50 14.36 21.55 73.66 7.88
2.71 0.33 0.01 0.30 14.10 16.17 80.92 6.97
1.82 0.34 0.76 0.84 2.12 19.57 18.89 4.41
0.00 0.01 0.00 0.00 7.18 0.01 0.00 0.01
21.50 3.45 19.24 26.27 22.06 95.00 98.00 29.74
Table 2 GMM estimates of the determinants of banks’ price-cost margins. Margin 1 Constant Price-cost margin-1 Output gap
1.0382∗∗∗ (0.0451) 0.4736∗∗∗ (0.0113) –0.0912∗∗∗ (0.0249)
Margin 2 2.2982∗∗∗ (0.0784) 0.3191∗∗∗ (0.0069)
0.1123∗∗∗ (0.0078) –0.0378 (0.0462)
–0.0539∗∗∗ (0.0060) 0.0994∗∗∗ (0.0049) 0.0369 (0.0253)
0.1141∗∗∗ (0.0335) 0.7237∗∗∗ (0.1033) –0.1389∗∗∗ (0.0345)
3.1862∗∗∗ (0.0383) 0.0414 (0.0583) –0.1278∗∗∗ (0.0112)
Margin 3 3.7154∗∗∗ (0.2148) 0.0266∗∗∗ (0.0058)
1.3162∗∗∗ (0.0231) 0.4074∗∗∗ (0.0088) –0.0276∗∗∗ (0.0052)
0.8785∗∗∗ (0.0852) 0.5244∗∗∗ (0.0075)
0.1923∗∗∗ (0.0102) –0.2873∗∗∗ (0.0629)
–0.0354 (0.1694) 0.1748∗∗∗ (0.0101) –0.1599∗∗ (0.0622)
0.0549∗∗∗ (0.0042) –0.0 0 08 (0.0199)
–0.0196∗∗∗ (0.0068) 0.0198∗∗∗ (0.0041) –0.0497 (0.2173)
0.0384∗∗∗ (0.0137) 0.1126∗∗ (0.0499) –0.1792∗∗∗ (0.0048)
0.0080 (0.0237) 0.7702∗∗∗ (0.0319) –0.3537∗∗∗ (0.0120)
0.0125 (0.0236) 0.8026∗∗∗ (0.0318) –0.3583∗∗∗ (0.0121)
0.0657∗∗∗ (0.0222) 0.3275∗∗∗ (0.0121) –0.0166∗∗ (0.0148)
0.0134∗∗∗ (0.0278) 0.3147∗∗∗ (0.0341) –0.3049∗∗∗ (0.0133)
Sargan testa Hansen testb
–0.0199∗∗∗ (0.0046) –0.0486∗∗∗ (0.0041) 0.1610∗∗∗ (0.0211) 0.0800 (0.1378) 0.4454∗∗∗ (0.0286) 0.260 0.539
–0.0105∗∗∗ (0.0 0 04) –0.0030∗∗∗ (0.0 0 04) 0.0285∗∗∗ (0.0015) –0.0334 (0.0343) 0.1055∗∗∗ (0.0344) 0.406 0.446
–0.0078∗∗∗ (0.0011) –0.0876∗∗∗ (0.0013) 0.0066 (0.0053) -0.2034 (0.1270) 1.1299∗∗∗ (0.1340) 0.919 0.211
–0.0075∗∗∗ (0.0011) –0.0892∗∗∗ (0.0013) 0.0105 (0.0053) –0.0259 (0.1310) 0.0627 (0.1297) 0.896 0.148
–0.0094∗∗∗ (0.0 0 04) –0.0073∗∗∗ (0.0012) 0.0156∗∗∗ (0.0023) -0.1349∗∗∗ (0.0379) 0.0108 (0.0348) 0.354 0.342
–0.0193∗∗∗ (0.0 0 09) –0.0157∗∗∗ (0.0011) 0.0668∗∗∗ (0.0047) –0.0723∗∗ (0.0306) 0.0058 (0.0324) 0.422 0.998
No. of observations
16,699
16,699
16,871
16,871
16,401
16,401
Bank loans Policy interest rate Interest rate risk
Herfindahl-Hirschman index Credit quality Total assets
Liquidity ratio Deposit ratio Capital ratio Euro membership dummy Basel II dummy
Robust standard errors (clustered at the bank level) are in parenthesis below the estimated coefficients. ∗∗∗ and ∗∗ indicate statistical Significance at the 1 and 5% levels, respectively. a The Sargan test reports p-values for the null hypothesis that the errors in the first difference regression exhibit no second order serial correlation. b The Hansen test reports p-values for the null hypothesis that the instruments used are not correlated with the residuals.
case, lagged bank margins have a positive and significant impact on the current level of margins, implying that the countercyclical effect of margins on output increases over time. The result is suggestive of a financial accelerator mechanism at work in European economies. The control variables in most cases are also statistically significant and the signs on their coefficients generally accord with our expectations from the literature. For example European bank margins widen in response to a tightening of monetary policy (increase in the policy interest rate), an increase in market concentration, a decline in credit quality (increase in bank provisioning), and an increase in bank capital, and narrow as banks get larger, are more liquid, Please cite this article as: Y. Altunbas¸ et al., Is there a financial accelerator in European banking? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.03.020
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and are more specialized in deposit taking. The impact of interest rate risk, euro adoption and the Basel II Accords is clearly sensitive to margin specification but European banks appear to narrow margins in response to an increase in interest rate risk, and they narrowed margins in countries that adopted the euro and raised them in response to regulators implementing the Basel II Accord recommendations. 4. Conclusion We find that margins in European banking are counter-cyclical with respect to the output gap and total bank loans, and that this relationship is robust to controlling for monetary policy, bank risk, banking industry and bank-specific variables, and to the impact of euro adoption and regulatory changes stemming from Basel II. The results are evidence of a financial accelerator—i.e., bank credit becomes more expensive in economic downturns compared to an economy in which bank spreads are constant or procyclical. Our results complement recent results for US banks, and the macroeconomic literature that uses countercyclical margins and the accelerator as a mechanism for the propagation of aggregate shocks. References Adrian, T., Shin, H.S., 2010. Liquidity and leverage. J. Financ. Intermed. 19, 418–437. Aliaga-Díaz, R., Olivero, M.P., 2010. Is there a financial accelerator in US banking? Evidence from the cyclicality of banks’ price-cost margins. Econ. Lett. 108, 167–171. Aliaga-Diaz, R., Olivero, M.P., 2011. The cyclicality of the price-cost margins in banking: an empirical analysis of its determinants. Econ. Inq. 49, 26–46. Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297. Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. J. Econom. 68, 29–51. Beck, T., Demirgüç-Kunt, A., Levine, R., 2006. Bank concentration, competition, and crises: first results. J. Bank. Financ. 30, 1581–1603. Berlin, M., Mester, L.J., 1999. Deposits and relationship lending. Rev. Financ. Stud. 12, 579–607. Bernanke, B., Gertler, M., 1989. Agency costs, net worth and business fluctuations. Am. Econ. Rev. 79, 14–31. Bernanke, B., Gertler, M., Gilchrist, S., 1996. The financial accelerator and the flight to quality. Rev. Econ. Stat. 78, 1–15. Demirgüç-Kunt, A., Huizinga, H., 1999. Determinants of commercial bank interest margins and profitability: some international evidence. World Bank Econ. Rev. 13, 379–408. Hannan, T.H., Berger, A.N., 1991. The rigidity of prices: evidence from the banking industry. Am. Econ. Rev. 81, 938–945. Ho, T., Saunders, A., 1981. The determinants of bank interest margins: theory and empirical evidence. J. Financ. Quant. Anal. 16, 581–600. Maudos, J., Fernández de Guevara, J., 2004. Factors explaining the interest margin in the banking sectors of the European Union. J Banking Finance 28, 2259–2281. Neumark, D., Sharpe, S.A., 1992. Market structure and the nature of price rigidity: evidence from the market for consumer deposits. Q. J. Econ. 107, 657–680. Turgutlu, E., 2010. Cyclical behaviour of price-cost margins in the Turkish banking industry. Econ. Model. 27, 368–374.
Please cite this article as: Y. Altunbas¸ et al., Is there a financial accelerator in European banking? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.03.020
JID: FRL
[m3Gsc;March 28, 2016;14:12]
Finance Research Letters 0 0 0 (2016) 1–4
Contents lists available at ScienceDirect
Finance Research Letters journal homepage: www.elsevier.com/locate/frl
Is there a financial accelerator in European banking? Yener Altunbas¸ a, Caterina Di Tommaso b, John Thornton a,∗ a b
Bangor University, College Road, Bangor LL57 2DG, United Kingdom University of Calabria, Via Pietro Bucci, 87036 Arcavacata, Rende CS, Italy
a r t i c l e
i n f o
Article history: Received 31 January 2016 Accepted 10 March 2016 Available online xxx JEL classification numbers: E32 E44
a b s t r a c t We show that price-cost margins for European banks are countercyclical after controlling for monetary policy, interest rate risk, and several banking industry and bank-specific factors. Our results support the existence of a “financial accelerator” at work in European economies. © 2016 Published by Elsevier Inc.
Keywords: Banks’ margins Business cycles Financial accelerator
1. Introduction Bernanke and Gertler (1989) and Bernanke, Gertler and Gilchrist (1996) showed how the effects of a real shock (such as a shock to productivity) on financial conditions could lead to persistent fluctuations in the economy, even if the initiating shock had little intrinsic persistence. A key concept in their analysis was a positive external finance premium, defined as the difference between the cost to a borrower of raising funds externally and the opportunity cost of internal funds. The theory predicts that the external finance premium that a borrower must pay should depend inversely on the strength of the borrower’s financial position, measured in terms of factors such as net worth, liquidity, and current and future expected cash flows. The inverse relationship of the external finance premium and the financial condition of borrowers creates a channel through which otherwise short-lived economic shocks may have long-lasting effects. In the hypothetical case that Bernanke and Gertler (1989) analyzed, an increase in productivity that improves the cash flows and balance sheet positions of firms leads in turn to lower external finance premiums in subsequent periods, which extends the expansion as firms are induced to continue investing even after the initial productivity shock has dissipated. This “financial accelerator” effect applies in principle to any shock that affects borrower balance sheets or cash flows. The concept can help to explain the persistence and amplitude of cyclical fluctuations in a modern economy. Some recent empirical work testing the financial accelerator hypotheses has focused on developments in banks’ pricecost margins on the assumption that they are a proxy for the external finance premium that banks charge to firms.1 The accelerator hypothesis implies that these margins are counter-cyclical thereby making bank credit more expensive during economic downturns than would be the case in an economy with constant bank margins. For example, Aliaga-Díaz and ∗
1
Corresponding author. Tel.: +4401244 388545; fax: +4401244579576. E-mail address: [email protected] (J. Thornton). The basis for this assumption is that banks’ marginal cost of funds are a good proxy for firms’ marginal cost of internal funds.
http://dx.doi.org/10.1016/j.frl.2016.03.020 1544-6123/© 2016 Published by Elsevier Inc.
Please cite this article as: Y. Altunbas¸ et al., Is there a financial accelerator in European banking? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.03.020
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Olivero (2010) apply a VAR forecast error-based methodology to measure the co-movement between bank margins and output to a panel of US banks over 1984–2005 and report that margins are countercyclical with respect to GDP and bank loans after controlling for monetary policy and credit risk. Aliaga-Díaz and Olivero (2011) apply more traditional regression techniques to a panel of US banks over 1979–2005 and find that margins are counter-cyclical with respect to per capita GDP and bank loans. Outside of the US, Turgutlu (2010) reports weak evidence of the counter-cyclicality of bank margins with respect to bank loans and no evidence of counter-cyclicality with respect to GDP and GDP per capita from a panel of 36 Turkish commercial banks over 20 01–20 08, though the study covers barely one full business cycle. In this paper, we add to a sparse empirical literature by reporting on the cyclicality of banks’ price–cost margins in a panel dataset of European banks. Our results provide strong support the existence of a “financial accelerator” at work in European economies. 2. Methodology and data We model the cyclicality of bank margins as follows:
yi,t = β1 ∗ yi−1,t + β2 ∗ log (Xt ) +
k1 j=1
γ j ∗ Z j,t +
k1
δi ∗ Wi,t + εi,t
(1)
i=1
In Eq. (1), i and j are banks and country, respectively, for which we use annual bank-level data from balance sheets and income statements from Bankscope for 1133 banks across 15 European countries for 1989 to 2012.2 yi, t is banks’ price-cost margins measured as the difference between either: (i) the ratio of interest income on loans to the volume of loans and the ratio of interest rate expense on deposits to the volume of deposits (Margin 1); (ii) the ratio of net interest revenue to the volume of loans and the ratio of net interest expense on deposits to the volume of deposits (Margin 2); and (iii) as the ratio to total assets of interest income on loans less interest expenses on deposits (Margin 3). Xt is the business cycle indicator, measured either by developments in the output gap for each country as reported in the IMF’s World Economic Outlook database, or by total bank loans, which we cyclically adjust using the Hodrick-Prescott filter and include as an alternative because many spending aggregates depend substantially on bank financing (Aliaga-Díaz and Olivero, 2011). Zj, t is a vector of economic variables that includes the central bank policy interest rate, included because monetary policy shocks impact positively on margins reflecting the inertia of deposit rates (Hannan and Berger, 1991; Neumark and Sharpe, 1992), and interest rate risk, which we represent by the standard deviation of the 3-month Treasury bill rate in each country, and include because banks may charge a premium to compensate for risk (Ho and Saunders, 1981). Finally, Wi, t is a vector of bank industry and bank-specific variables that includes: market concentration, measured by the Herfindahl-Hirschman index, because it may impact on bank risk (Beck, Demirgüç-Kunt and Levine, 2006); credit quality, measured as the ratio of loan loss provisions to total loans and is included because an increase in credit default rates may lead banks to increase their margins, (Demirgüç-Kunt and Huizinga, 1999), bank size, measured by total bank assets and included because larger banks have more possibility to diversify, which could reduce their cost of credit and lead to a narrowing of margins (Maudos and de Guevara, 2004); bank liquidity, measured as the ratio of cash plus securities to total assets and included because banks that choose to hold more liquid portfolios pay for the cost of that liquidity by raising their margins (Ho and Saunders, 1981); bank specialization, measured by the ratio of bank deposits to total assets and included because deposits are relatively interest-inelastic so that the margins of banks that specialize in deposit taking may be more isolated against economic shocks (Berlin and Mester, 1999); and capital, measured by the ratio of capital to total assets, and included as more capitalized banks may charge higher margins if holding equity is more costly than holding debt, for example, because of the latter’s more favourable tax treatment (Adrian and Shin, 2010). Finally, we include two 0–1 dummy variables that seek to capture, respectively, the impact on margins of the adoption of the Basel 11 Accord by European bank regulators from 2008 that forced a recalculation of credit risk to determine minimum capital, and the launch of the euro in 1999 that may have impacted the margins of banks in euro-adopting countries, for example by reducing risk. Descriptive statistics for the variables are presented in Table 1. The relationship between bank margins and developments in the business cycle might be driven by reverse causation. For example, Aliaga-Diaz and Olivero (2011) note that economic activity might be a function of the cost of bank credit, as proxied by bank margins. To control for potential reverse causation we estimate Eq. (1) using the generalized-methodsof moments (GMM) panel estimator developed for dynamic models by Arellano and Bond (1991) and Arellano and Bover (1995). 3. Results GMM results for the three measures of bank margins are reported in Table 2. In all but one case the coefficients on the business cycle indicators are highly statistically significant and negative—i.e., the price-cost margins of European banks are counter-cyclical after controlling for monetary policy, interest rate risk, and bank industry and bank specific factors. In each
2 The countries are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom.
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Table 1 Descriptive statistics. Variables Dependent variables: Margin 1 Margin 2 Margin 3 Business cycle indicators Output gap (% deviation from trend) Bank loans (detrended, millions) Other independent variables: Policy interest rate (%) Interest rate risk Herfindahl-Hirschman index Credit quality ratio Total assets (in logs) Liquidity ratio Deposit ratio Capital ratio
Mean
Median
Standard deviation
Minimum
Maximum
2.67 1.64 2.46
2.49 1.17 2.36
1.99 1.76 1.57
0.00 0.00 0.01
45.90 24.08 50.42
–0.27 12.80
-0.28 11.00
1.95 6.72
-8.76 3.80
10.88 23.00
2.78 0.38 0.13 0.50 14.36 21.55 73.66 7.88
2.71 0.33 0.01 0.30 14.10 16.17 80.92 6.97
1.82 0.34 0.76 0.84 2.12 19.57 18.89 4.41
0.00 0.01 0.00 0.00 7.18 0.01 0.00 0.01
21.50 3.45 19.24 26.27 22.06 95.00 98.00 29.74
Table 2 GMM estimates of the determinants of banks’ price-cost margins. Margin 1 Constant Price-cost margin-1 Output gap
1.0382∗∗∗ (0.0451) 0.4736∗∗∗ (0.0113) –0.0912∗∗∗ (0.0249)
Margin 2 2.2982∗∗∗ (0.0784) 0.3191∗∗∗ (0.0069)
0.1123∗∗∗ (0.0078) –0.0378 (0.0462)
–0.0539∗∗∗ (0.0060) 0.0994∗∗∗ (0.0049) 0.0369 (0.0253)
0.1141∗∗∗ (0.0335) 0.7237∗∗∗ (0.1033) –0.1389∗∗∗ (0.0345)
3.1862∗∗∗ (0.0383) 0.0414 (0.0583) –0.1278∗∗∗ (0.0112)
Margin 3 3.7154∗∗∗ (0.2148) 0.0266∗∗∗ (0.0058)
1.3162∗∗∗ (0.0231) 0.4074∗∗∗ (0.0088) –0.0276∗∗∗ (0.0052)
0.8785∗∗∗ (0.0852) 0.5244∗∗∗ (0.0075)
0.1923∗∗∗ (0.0102) –0.2873∗∗∗ (0.0629)
–0.0354 (0.1694) 0.1748∗∗∗ (0.0101) –0.1599∗∗ (0.0622)
0.0549∗∗∗ (0.0042) –0.0 0 08 (0.0199)
–0.0196∗∗∗ (0.0068) 0.0198∗∗∗ (0.0041) –0.0497 (0.2173)
0.0384∗∗∗ (0.0137) 0.1126∗∗ (0.0499) –0.1792∗∗∗ (0.0048)
0.0080 (0.0237) 0.7702∗∗∗ (0.0319) –0.3537∗∗∗ (0.0120)
0.0125 (0.0236) 0.8026∗∗∗ (0.0318) –0.3583∗∗∗ (0.0121)
0.0657∗∗∗ (0.0222) 0.3275∗∗∗ (0.0121) –0.0166∗∗ (0.0148)
0.0134∗∗∗ (0.0278) 0.3147∗∗∗ (0.0341) –0.3049∗∗∗ (0.0133)
Sargan testa Hansen testb
–0.0199∗∗∗ (0.0046) –0.0486∗∗∗ (0.0041) 0.1610∗∗∗ (0.0211) 0.0800 (0.1378) 0.4454∗∗∗ (0.0286) 0.260 0.539
–0.0105∗∗∗ (0.0 0 04) –0.0030∗∗∗ (0.0 0 04) 0.0285∗∗∗ (0.0015) –0.0334 (0.0343) 0.1055∗∗∗ (0.0344) 0.406 0.446
–0.0078∗∗∗ (0.0011) –0.0876∗∗∗ (0.0013) 0.0066 (0.0053) -0.2034 (0.1270) 1.1299∗∗∗ (0.1340) 0.919 0.211
–0.0075∗∗∗ (0.0011) –0.0892∗∗∗ (0.0013) 0.0105 (0.0053) –0.0259 (0.1310) 0.0627 (0.1297) 0.896 0.148
–0.0094∗∗∗ (0.0 0 04) –0.0073∗∗∗ (0.0012) 0.0156∗∗∗ (0.0023) -0.1349∗∗∗ (0.0379) 0.0108 (0.0348) 0.354 0.342
–0.0193∗∗∗ (0.0 0 09) –0.0157∗∗∗ (0.0011) 0.0668∗∗∗ (0.0047) –0.0723∗∗ (0.0306) 0.0058 (0.0324) 0.422 0.998
No. of observations
16,699
16,699
16,871
16,871
16,401
16,401
Bank loans Policy interest rate Interest rate risk
Herfindahl-Hirschman index Credit quality Total assets
Liquidity ratio Deposit ratio Capital ratio Euro membership dummy Basel II dummy
Robust standard errors (clustered at the bank level) are in parenthesis below the estimated coefficients. ∗∗∗ and ∗∗ indicate statistical Significance at the 1 and 5% levels, respectively. a The Sargan test reports p-values for the null hypothesis that the errors in the first difference regression exhibit no second order serial correlation. b The Hansen test reports p-values for the null hypothesis that the instruments used are not correlated with the residuals.
case, lagged bank margins have a positive and significant impact on the current level of margins, implying that the countercyclical effect of margins on output increases over time. The result is suggestive of a financial accelerator mechanism at work in European economies. The control variables in most cases are also statistically significant and the signs on their coefficients generally accord with our expectations from the literature. For example European bank margins widen in response to a tightening of monetary policy (increase in the policy interest rate), an increase in market concentration, a decline in credit quality (increase in bank provisioning), and an increase in bank capital, and narrow as banks get larger, are more liquid, Please cite this article as: Y. Altunbas¸ et al., Is there a financial accelerator in European banking? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.03.020
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and are more specialized in deposit taking. The impact of interest rate risk, euro adoption and the Basel II Accords is clearly sensitive to margin specification but European banks appear to narrow margins in response to an increase in interest rate risk, and they narrowed margins in countries that adopted the euro and raised them in response to regulators implementing the Basel II Accord recommendations. 4. Conclusion We find that margins in European banking are counter-cyclical with respect to the output gap and total bank loans, and that this relationship is robust to controlling for monetary policy, bank risk, banking industry and bank-specific variables, and to the impact of euro adoption and regulatory changes stemming from Basel II. The results are evidence of a financial accelerator—i.e., bank credit becomes more expensive in economic downturns compared to an economy in which bank spreads are constant or procyclical. Our results complement recent results for US banks, and the macroeconomic literature that uses countercyclical margins and the accelerator as a mechanism for the propagation of aggregate shocks. References Adrian, T., Shin, H.S., 2010. Liquidity and leverage. J. Financ. Intermed. 19, 418–437. Aliaga-Díaz, R., Olivero, M.P., 2010. Is there a financial accelerator in US banking? Evidence from the cyclicality of banks’ price-cost margins. Econ. Lett. 108, 167–171. Aliaga-Diaz, R., Olivero, M.P., 2011. The cyclicality of the price-cost margins in banking: an empirical analysis of its determinants. Econ. Inq. 49, 26–46. Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297. Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. J. Econom. 68, 29–51. Beck, T., Demirgüç-Kunt, A., Levine, R., 2006. Bank concentration, competition, and crises: first results. J. Bank. Financ. 30, 1581–1603. Berlin, M., Mester, L.J., 1999. Deposits and relationship lending. Rev. Financ. Stud. 12, 579–607. Bernanke, B., Gertler, M., 1989. Agency costs, net worth and business fluctuations. Am. Econ. Rev. 79, 14–31. Bernanke, B., Gertler, M., Gilchrist, S., 1996. The financial accelerator and the flight to quality. Rev. Econ. Stat. 78, 1–15. Demirgüç-Kunt, A., Huizinga, H., 1999. Determinants of commercial bank interest margins and profitability: some international evidence. World Bank Econ. Rev. 13, 379–408. Hannan, T.H., Berger, A.N., 1991. The rigidity of prices: evidence from the banking industry. Am. Econ. Rev. 81, 938–945. Ho, T., Saunders, A., 1981. The determinants of bank interest margins: theory and empirical evidence. J. Financ. Quant. Anal. 16, 581–600. Maudos, J., Fernández de Guevara, J., 2004. Factors explaining the interest margin in the banking sectors of the European Union. J Banking Finance 28, 2259–2281. Neumark, D., Sharpe, S.A., 1992. Market structure and the nature of price rigidity: evidence from the market for consumer deposits. Q. J. Econ. 107, 657–680. Turgutlu, E., 2010. Cyclical behaviour of price-cost margins in the Turkish banking industry. Econ. Model. 27, 368–374.
Please cite this article as: Y. Altunbas¸ et al., Is there a financial accelerator in European banking? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.03.020