The 56 reference contexts in paper Christopher F Baum, Mustafa Caglayan, Bing Xu (2017) “The Impact of Uncertainty on Financial Institutions” / RePEc:boc:bocoec:939

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    of the 2007–08 global financial crisis, most studies have focused on a particular aspect of financial institutions such as credit, profitability, liquidity or loan quality.1 The literature has not provided a comprehensive view on the overall health of major financial institutions under uncertainty, despite the calls from both academicians and policymakers (see for example,ˇCih ́ak et al., 2012;
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    Law and Singh, 2014; Arcand et al., 2015).
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    2 In contrast to the prior literature, we explore the impact of uncertainty on the overall functioning of the financial sector by taking into account the multidimensional nature of the question under investigation.
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    A broad exploration of uncertainty effects is relevant given the ongoing debate whether monetary policy makers could aim to identify and remove balance sheet impairments which can easily block the flow of funds that the productive sectors seek while they strive to maintain the stability of financial institutions (e.g., see
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    Smets, 2014 and Sannikov and Brunnermeier, 2013;
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    also see Caglayan et al., 2017 on the role of financial depth in the transmission of monetary policy shocks). Our paper contributes to the literature in several ways. First, we investigate the effect of 1For example, Louzis et al. (2012) examined the determinants of banks’ non-performing loans; Delis et al. (2014) investigated US banks’ lending decisions during periods of anxiety; and K
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    A broad exploration of uncertainty effects is relevant given the ongoing debate whether monetary policy makers could aim to identify and remove balance sheet impairments which can easily block the flow of funds that the productive sectors seek while they strive to maintain the stability of financial institutions (e.g., see Smets, 2014 and Sannikov and Brunnermeier, 2013; also see
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    Caglayan et al., 2017
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    on the role of financial depth in the transmission of monetary policy shocks). Our paper contributes to the literature in several ways. First, we investigate the effect of 1For example, Louzis et al. (2012) examined the determinants of banks’ non-performing loans; Delis et al. (2014) investigated US banks’ lending decisions during periods of anxiety; and Khan et al. (2017) studied the role of
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    that the productive sectors seek while they strive to maintain the stability of financial institutions (e.g., see Smets, 2014 and Sannikov and Brunnermeier, 2013; also see Caglayan et al., 2017 on the role of financial depth in the transmission of monetary policy shocks). Our paper contributes to the literature in several ways. First, we investigate the effect of 1For example,
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    Louzis et al. (2012)
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    examined the determinants of banks’ non-performing loans; Delis et al. (2014) investigated US banks’ lending decisions during periods of anxiety; and Khan et al. (2017) studied the role of funding liquidity on bank managers’ risk-taking behavior. 2See, among others, Boyd et al. (2001) who studied the effect of inflation on the development of banking sector and equity market activities; Beck et al.
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    of financial institutions (e.g., see Smets, 2014 and Sannikov and Brunnermeier, 2013; also see Caglayan et al., 2017 on the role of financial depth in the transmission of monetary policy shocks). Our paper contributes to the literature in several ways. First, we investigate the effect of 1For example, Louzis et al. (2012) examined the determinants of banks’ non-performing loans;
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    Delis et al. (2014)
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    investigated US banks’ lending decisions during periods of anxiety; and Khan et al. (2017) studied the role of funding liquidity on bank managers’ risk-taking behavior. 2See, among others, Boyd et al. (2001) who studied the effect of inflation on the development of banking sector and equity market activities; Beck et al. (2013) who compared the relative performance of (Islamic and conventional)
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    First, we investigate the effect of 1For example, Louzis et al. (2012) examined the determinants of banks’ non-performing loans; Delis et al. (2014) investigated US banks’ lending decisions during periods of anxiety; and
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    Khan et al. (2017)
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    studied the role of funding liquidity on bank managers’ risk-taking behavior. 2See, among others, Boyd et al. (2001) who studied the effect of inflation on the development of banking sector and equity market activities; Beck et al. (2013) who compared the relative performance of (Islamic and conventional) banks on different aspects including their business model, efficiency, asset qual
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    First, we investigate the effect of 1For example, Louzis et al. (2012) examined the determinants of banks’ non-performing loans; Delis et al. (2014) investigated US banks’ lending decisions during periods of anxiety; and Khan et al. (2017) studied the role of funding liquidity on bank managers’ risk-taking behavior. 2See, among others,
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    Boyd et al. (2001)
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    who studied the effect of inflation on the development of banking sector and equity market activities; Beck et al. (2013) who compared the relative performance of (Islamic and conventional) banks on different aspects including their business model, efficiency, asset quality and stability. 2 uncertainty on different aspects of financial institutions using a large panel of international
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    . (2012) examined the determinants of banks’ non-performing loans; Delis et al. (2014) investigated US banks’ lending decisions during periods of anxiety; and Khan et al. (2017) studied the role of funding liquidity on bank managers’ risk-taking behavior. 2See, among others, Boyd et al. (2001) who studied the effect of inflation on the development of banking sector and equity market activities;
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    Beck et al. (2013)
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    who compared the relative performance of (Islamic and conventional) banks on different aspects including their business model, efficiency, asset quality and stability. 2 uncertainty on different aspects of financial institutions using a large panel of international data from 89 countries for the period between 1996 to 2015, eoncompassing the period of the global financial crisis.
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    Using the parameter estimates for the full sample, we find that a one standard deviation change in uncertainty could induce a 1.4% change in financial depth from its mean value.3Even though this seems small, as
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    Bernanke (1983) and Bernanke and Gertler (1989)
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    3The average ratio of private credit to GDP in our sample is around 70%. Hence, the change in availability of credit to private sector in response to a one standard change in uncertainty can easily amount to a figure around 1% of GDP. 3 discuss, even minor changes in the availability of credit can induce large fluctuations in an economy.
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    . 4 2 Literature Survey Our investigation relates to many earlier studies which have separately focused on financial depth and bank lending behavior, as well as the efficiency and stability of financial institutions. An examination of the literature yields a vast body of work which has examined the relationship between financial development and stability.4For instance, using aggregate data,
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    Bernanke and Gertler (1989)
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    suggested that countries with developed and deeper financial markets enable firms to have easier access to external funds. This in turn dampens the impact of negative shocks on the economy. For instance, da Silva (2002) showed that countries with deeper financial markets experience smoother business cycles.
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    Similarly, Raddatz (2006) found that financial depth helps to reduce output volatility in industries which strive for high levels of liquidity. Another strand of papers have shown that bank lending varies over the business cycle, declining during periods of extreme uncertainty or financial crisis (e.g.,
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    Ivashina and Scharfstein, 2010; Puri et al., 2011; Delis et al., 2014; and Kosak et al., 2015).
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    Taken together, researchers have argued that financial deepening is important for the smooth functioning of the economy as well as mitigating the adverse effects of shocks. However, to our knowledge, earlier research has not specifically focused on the direct impact of uncertainty on financial depth.
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    Turning to studies that have focused on bank efficiency, we see that researchers have associated significant reductions in bank profitability as a signal of an impending financial crisis (Demirg ̈u ̧c-Kunt and
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    Huizinga, 1999, Bourke, 1989, Cornett et al., 2010b). Bikker and Vervliet (2018)
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    found that low interest rate environments lower US banks’ profitability. In a similar line, Albertazzi and Gambacorta (2009) provided evidence that high risk periods weaken banks’ returns. Bolt et al. (2012) further showed the effects of a 1% contraction in real GDP during periods of deep recessions would lead to a 25 basis point decline in banks’ ROA (return on assets).
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    Turning to studies that have focused on bank efficiency, we see that researchers have associated significant reductions in bank profitability as a signal of an impending financial crisis (Demirg ̈u ̧c-Kunt and Huizinga, 1999, Bourke, 1989, Cornett et al., 2010b). Bikker and Vervliet (2018) found that low interest rate environments lower US banks’ profitability. In a similar line,
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    Albertazzi and Gambacorta (2009)
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    provided evidence that high risk periods weaken banks’ returns. Bolt et al. (2012) further showed the effects of a 1% contraction in real GDP during periods of deep recessions would lead to a 25 basis point decline in banks’ ROA (return on assets).
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    Bikker and Vervliet (2018) found that low interest rate environments lower US banks’ profitability. In a similar line, Albertazzi and Gambacorta (2009) provided evidence that high risk periods weaken banks’ returns.
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    Bolt et al. (2012)
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    further showed the effects of a 1% contraction in real GDP during periods of deep recessions would lead to a 25 basis point decline in banks’ ROA (return on assets). Separately, researchers who have examined banks’ non-interest income have suggested that an increased level of income from this category leads to higher systemic 4See Levine (2005) as well as Demirg ̈u ̧c-Kunt and Levine (2008) for d
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    Bolt et al. (2012) further showed the effects of a 1% contraction in real GDP during periods of deep recessions would lead to a 25 basis point decline in banks’ ROA (return on assets). Separately, researchers who have examined banks’ non-interest income have suggested that an increased level of income from this category leads to higher systemic 4See
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    Levine (2005)
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    as well as Demirg ̈u ̧c-Kunt and Levine (2008) for detailed reviews. 5 risk and lower efficiency (e.g., see Brunnermeier et al., 2012). In this context, DeYoung and Roland (2001), and Lepetit et al. (2008) argued that changes in banks’ non-interest income in periods of uncertainty can be taken to signal banks’ risk appetite and deterioration of the efficient functioning of financial intermediarie
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    Separately, researchers who have examined banks’ non-interest income have suggested that an increased level of income from this category leads to higher systemic 4See Levine (2005) as well as Demirg ̈u ̧c-Kunt and
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    Levine (2008)
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    for detailed reviews. 5 risk and lower efficiency (e.g., see Brunnermeier et al., 2012). In this context, DeYoung and Roland (2001), and Lepetit et al. (2008) argued that changes in banks’ non-interest income in periods of uncertainty can be taken to signal banks’ risk appetite and deterioration of the efficient functioning of financial intermediaries.
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    Separately, researchers who have examined banks’ non-interest income have suggested that an increased level of income from this category leads to higher systemic 4See Levine (2005) as well as Demirg ̈u ̧c-Kunt and Levine (2008) for detailed reviews. 5 risk and lower efficiency (e.g., see
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    Brunnermeier et al., 2012).
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    In this context, DeYoung and Roland (2001), and Lepetit et al. (2008) argued that changes in banks’ non-interest income in periods of uncertainty can be taken to signal banks’ risk appetite and deterioration of the efficient functioning of financial intermediaries.
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    Separately, researchers who have examined banks’ non-interest income have suggested that an increased level of income from this category leads to higher systemic 4See Levine (2005) as well as Demirg ̈u ̧c-Kunt and Levine (2008) for detailed reviews. 5 risk and lower efficiency (e.g., see Brunnermeier et al., 2012). In this context,
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    DeYoung and Roland (2001), and Lepetit et al. (2008)
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    argued that changes in banks’ non-interest income in periods of uncertainty can be taken to signal banks’ risk appetite and deterioration of the efficient functioning of financial intermediaries. Several other researchers have examined factors that promote stability of the financial system and confirmed that liquidity plays an important role: an issue which was under the spotlight during the 2008
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    researchers have examined factors that promote stability of the financial system and confirmed that liquidity plays an important role: an issue which was under the spotlight during the 2008 financial crisis. In order to manage liquidity risk, bank managers generate liquidity on their balance sheet by converting illiquid assets (e.g., bank loans) into liquid assets (e.g., cash and securities)
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    (Berger and Bouwman, 2009).
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    Hence, maintaining the right amount of liquidity is essential to achieve stability so that loans need not be liquidated to overcome cash shortages. Gatev and Strahan (2006) and Gatev et al. (2009), among others, have shown that deposit withdrawals and commitment drawdowns are negatively related to market stress.
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    In order to manage liquidity risk, bank managers generate liquidity on their balance sheet by converting illiquid assets (e.g., bank loans) into liquid assets (e.g., cash and securities) (Berger and Bouwman, 2009). Hence, maintaining the right amount of liquidity is essential to achieve stability so that loans need not be liquidated to overcome cash shortages.
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    Gatev and Strahan (2006) and Gatev et al. (2009),
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    among others, have shown that deposit withdrawals and commitment drawdowns are negatively related to market stress. Acharya and Naqvi (2012) developed a theoretical model to show the positive linkages between abundant liquidity and bank managers’ risk-taking behaviours.
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    Hence, maintaining the right amount of liquidity is essential to achieve stability so that loans need not be liquidated to overcome cash shortages. Gatev and Strahan (2006) and Gatev et al. (2009), among others, have shown that deposit withdrawals and commitment drawdowns are negatively related to market stress.
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    Acharya and Naqvi (2012)
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    developed a theoretical model to show the positive linkages between abundant liquidity and bank managers’ risk-taking behaviours. In addition to liquidity, researchers have used non-performing loans (NPLs) as a separate indicator to monitor stability of banks.
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    NPLs capture the asset quality of banks: higher NPLs indicate that banks are holding riskier assets. To that end, most studies that examined the relation between the macroeconomic environment and credit risk have generally found economic conditions negatively affect NPLs (e.g.,
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    Louzis et al., 2012; Klein, 2013).
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    It is interesting to note that higher management quality can reduce problem loans, as shown in Berger and DeYoung (1997) and Louzis et al. (2012). In what follows, we provide empirical evidence that uncertainty adversely affects several facets of the financial system.
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    To that end, most studies that examined the relation between the macroeconomic environment and credit risk have generally found economic conditions negatively affect NPLs (e.g., Louzis et al., 2012; Klein, 2013). It is interesting to note that higher management quality can reduce problem loans, as shown in
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    Berger and DeYoung (1997) and Louzis et al. (2012).
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    In what follows, we provide empirical evidence that uncertainty adversely affects several facets of the financial system. In doing so, we examine three aspects of the financial system rather than just one to develop an understanding of uncertainty effects on the whole system, and show that these effects are economically significant on all dimensions. 6 3 Data To pursue our study, we acqui
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    Furthermore, credit issued by the central bank is excluded. 7 the annual standard deviation of monthly logarithmic differences inCPI. This uncertainty measure has been implemented by several researchers in the literature including
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    Barro (1996), Judson and Orphanides (1999) and Caglayan and Xu (2016).
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    In this respect, we use this naive uncertainty measure to serve as a benchmark. We then generate two additional modelbased measures. The second measure is obtained from a static model. We initially estimated an AR(p) model which took the following form: πt=α+ ∑p i=1 βiπt−i+-t(1) whereπtis the log difference ofCPIand-tis a random term.
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    have usedNIIto capture the extent to which banks resort to riskier strategies.7We expect to find a negative impact of uncertainty on bank efficiency, for increases in uncertainty would trigger a fall in bank profitability. In 6Details on uncertainty proxies are given in section 4.1. 7NIIhas been used as a forward-looking measure of risk by several researchers, including
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    Buch et al. (2014), Brunnermeier et al. (2012), DeYoung and Roland (2001).
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    10 contrast, an increase in uncertainty would encourage bank managers to increase their noninterest activities, as they search for high yield in a period when returns from traditional operations fall and monitoring borrowers and recovery of funds become a difficult task.
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    rate of inflation (Inflation) as well as several additional control variables including the GDP growth rate (∆GDP) and a measure of trade openness (Openness) to control for changes in domestic and foreign demand, respectively. We also use a dummy variable (oBC) to capture the effects of ongoing banking crises. This variable is constructed based on the following rules which are similar to that in
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    Laeven and Valencia (2013).
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    We defined a banking crisis as systemic if there are 1) significant signs of financial distress, captured by significant bank runs, losses in the banking system, and/or bank liquidations; and 2) significant banking policy intervention measures due to significant losses incurred in the banking system.
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    crisis is set to the year when both criteria are met, while the end is defined as the year before both real GDP growth and real credit growth are positive for at least two consecutive years.9For EU countries, we used banking crisis periods identified by the ECB (Lo Duca et al., 2017). For non-EU countries, we have identified crisis episodes when the banking system of a country exhibits 8See
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    Huizinga (1993)
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    along these lines. 9Our definition Laeven and Valencia (2013) data only cover up to 2011 starting as of 1970. 11 significant losses pushing the share of non-performing loans above 20%. In addition to the aforementioned variables, three more control variables are used in the model.
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    met, while the end is defined as the year before both real GDP growth and real credit growth are positive for at least two consecutive years.9For EU countries, we used banking crisis periods identified by the ECB (Lo Duca et al., 2017). For non-EU countries, we have identified crisis episodes when the banking system of a country exhibits 8See Huizinga (1993) along these lines. 9Our definition
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    Laeven and Valencia (2013)
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    data only cover up to 2011 starting as of 1970. 11 significant losses pushing the share of non-performing loans above 20%. In addition to the aforementioned variables, three more control variables are used in the model.
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    The last three columns lay out the results for the alternative definition of financial depth (FD2) which captures the ratio of private credit to the real sector from deposit money banks 10We have carried out the HayashiCtest
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    (Hayashi (2000),
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    pp. 218–222) to examine whether the independent variables in these estimated models can be treated as exogenous. Based on this test, we can not reject the null hypothesis, and thus employed the fixed effects model. 12 and other financial institutions to GDP, excluding credit issued to governments, government agencies, and public enterprises.11For each group, we initially present results for the
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    In addition, our empirical results show that a country’s ratio of international debt issues to GDP (Debt/GDP) has a positive impact on financial depth. This is sensible asDebt/GDP measures the stock of outstanding international bonds relative to a country’s economic activity and increases with countries’ income level
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    (Beck et al., 2010).
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    This finding suggests that funds raised from external creditors are injected into the economy through the financial system. Overall, the results in Table 2 confirm that uncertainty has a negative effect on financial depth.
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    Researchers have shown that following monetary and financial shocks bank loans decline sharply, making it difficult for bank-dependent borrowers to raise funds from financial institutions (e.g., See
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    Ferri et al., 2014).
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    Research has revealed that the cyclicality of loans leads to inefficiencies in credit allocation, as during the expansionary state of the economy banks can easily grant credit to firms with marginally positive or even negative net present value projects as bank lending standards decline and competition increases while risks are underestimated.
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    On the other hand, during recessionary periods, banks can even reject loan applications of firms with positive net present value projects due to increased risk premiums. No doubt, this behavior reflects banks’ increased risk aversion during recessionary periods (see for example,
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    Ruckes, 2004 and Bassett et al., 2014).
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    Furthermore, researchers also argued that the extent of asymmetric information over the business cycle can affect banks’ risk preferences. Hence, during the expansionary phase] of the business cycle, banks tend to grant more loans than in recessionary periods, as lenders suffer less from asymmetric information problems during the upward phase of the economy.
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    Hence, during the expansionary phase] of the business cycle, banks tend to grant more loans than in recessionary periods, as lenders suffer less from asymmetric information problems during the upward phase of the economy. Moreover, the business cycle can affect the cost of monitoring borrowers which in turn can lead to fluctuations in bank credit (e.g.,
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    Athanasoglou et al., 2014).
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    If these banklevel observations are valid, then there will be similar implications for overall private sector credit. Given that financial crises trigger deep recessions, our findings complement the findings reported in the literature. 5.2 Uncertainty effects on bank efficiency In this section, we discuss uncertainty effects on financial institutions’ efficiency as we examine bank
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    The results show that bank returns during ongoing banking crisis episodes are negatively and significantly affected at the 1% level. These observations complement findings reported in the literature. For example,
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    Albertazzi and Gambacorta (2009)
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    have argued that bad economic conditions worsen the quality of banks’ loan portfolio, and, therefore, generate credit losses and reduction in bank profits. In a similar line, Cornett et al. (2010a) have reported that banks of all size groups suffered as bank performance decreased before and during the recent financial crisis.
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    For example, Albertazzi and Gambacorta (2009) have argued that bad economic conditions worsen the quality of banks’ loan portfolio, and, therefore, generate credit losses and reduction in bank profits. In a similar line,
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    Cornett et al. (2010a)
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    have reported that banks of all size groups suffered as bank performance decreased before and during the recent financial crisis. In contrast to Table 2, in which we examined the impact of uncertainty on financial depth, foreign bank concentration has a negative and significant impact on banks’ return on equity.
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    This is consistent with the literature which has shown that the entry of foreign banks render national banking markets more competitive, reduce profitability and costs of financial intermediation in the industry (e.g.,
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    Claessens et al., 2001; and Claessens and Horen, 2014).
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    Next, we examine the impact of uncertainty on banks’ non-interest income activities. This set of activities, including income from trading and securitization, investment banking and advisory fees, and service charges, are considered to be separate from the traditional deposit taking and lending functions of banks (see Brunnermeier et al., 2012) and deemed to act as a forward-looking m
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    This set of activities, including income from trading and securitization, investment banking and advisory fees, and service charges, are considered to be separate from the traditional deposit taking and lending functions of banks (see
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    Brunnermeier et al., 2012) and
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    deemed to act as a forward-looking measure of bank risk. As a consequence, changes in non-interest income activities may adversely affect banks’ earnings volatility because of a higher degree of financial leverage.
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    These results suggest that when income from traditional lines of business declines (which in consequence leads to a decline in earnings to equity, as shown in the first columns), banks engage in other activities to boost their profitability. This finding is consistent with the risk-taking channel through search for yield
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    (Rajan, 2006).
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    15 In fact, a number of researchers have found a significant positive relationship between non-interest income activities and earnings volatility (see DeYoung and Roland, 2001, Stiroh, 2004, Lepetit et al., 2008 and Abuzayed et al., 2018).
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    This finding is consistent with the risk-taking channel through search for yield (Rajan, 2006). 15 In fact, a number of researchers have found a significant positive relationship between non-interest income activities and earnings volatility (see
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    DeYoung and Roland, 2001, Stiroh, 2004, Lepetit et al., 2008 and Abuzayed et al., 2018).
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    For instance, Lepetit et al. (2008) argued that non-interest income activities12are considered to be riskier than traditional credit creation, as it might be easier for customers to switch banks for these types of services rather than traditional banking activities such as relationship lending.
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    This finding is consistent with the risk-taking channel through search for yield (Rajan, 2006). 15 In fact, a number of researchers have found a significant positive relationship between non-interest income activities and earnings volatility (see DeYoung and Roland, 2001, Stiroh, 2004, Lepetit et al., 2008 and Abuzayed et al., 2018). For instance,
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    Lepetit et al. (2008)
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    argued that non-interest income activities12are considered to be riskier than traditional credit creation, as it might be easier for customers to switch banks for these types of services rather than traditional banking activities such as relationship lending.
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    For instance, Lepetit et al. (2008) argued that non-interest income activities12are considered to be riskier than traditional credit creation, as it might be easier for customers to switch banks for these types of services rather than traditional banking activities such as relationship lending.
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    DeYoung and Torna (2013)
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    found that banks with increased exposure to activities such as investment banking, insurance underwriting and venture capital also tended to take more risk in their traditional banking activities. They argued that such banks were more aggressive in their lending behavior, had less diversified and riskier loan portfolios, and funded their loan books with less stable deposit bases.
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    Taking these findings together, we conclude that uncertainty affects the efficiency of financial intermediaries negatively by reducing banks’ operational performance and increas12Activities such as cash withdrawal fees, bank account management, or data processing. 16 ing risk-taking activities. Our findings are consistent with
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    Kok et al. (2015),
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    who have also shown that greater reliance on non-interest income is related to weaker bank profitability. The significance of ongoing banking crises in the model constitutes further evidence that efficiency worsens during periods of banking crises when asymmetric information problems heighten. 5.3 Uncertainty effects on bank stability Table 4 presents uncertainty effects on banks’ l
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    The first three columns show that inflation uncertainty has a positive and significant effect on liquidity at the 1% level. Our finding complements the literature which demonstrated that during periods of disturbance, banks increase their liquid asset holdings (e.g.,
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    Cornett et al., 2011).
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    Several authors such as Gatev and Strahan (2006) and Gatev et al. (2009), have found that deposit withdrawals and commitment drawdowns are negatively associated to market stress. To be more specific, when a crisis occurs outside the banking system (e.g., in the commercial paper market), the funds that investors remove from these instruments would flow primarily into the banking system, because
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    The first three columns show that inflation uncertainty has a positive and significant effect on liquidity at the 1% level. Our finding complements the literature which demonstrated that during periods of disturbance, banks increase their liquid asset holdings (e.g., Cornett et al., 2011). Several authors such as
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    Gatev and Strahan (2006) and Gatev et al. (2009),
    Suffix
    have found that deposit withdrawals and commitment drawdowns are negatively associated to market stress. To be more specific, when a crisis occurs outside the banking system (e.g., in the commercial paper market), the funds that investors remove from these instruments would flow primarily into the banking system, because banks would be seen as a safe haven given government guarantees on deposit
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    38045
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    Results are available upon request. 17 Nevertheless, availability of excess liquidity can induce bank managers to seek higher returns and encourage excessive credit volumes as bank managers may misprice the downside risks (e.g.,
    Exact
    Acharya and Naqvi, 2012).
    Suffix
    This is further acerbated as loan officers are compensated based on the volume of loans they book, while they are only penalised if the bank suffers from a liquidity shortfall. However, during periods of high macroeconomic risk, it is natural that investors would prefer to save with banks rather than making direct investments elsewhere.
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  46. Start
    39240
    Prefix
    Furthermore, as expected, we find that an ongoing banking crisis displays a positive and significant coefficient. These findings are consistent with the literature on loan quality and the macroeconomic environment (e.g.,
    Exact
    Loutskina, 2011; and Klein, 2013).
    Suffix
    In particular, during periods of tranquility, banks have fewer problem loans as both businesses and households have sufficient streams of revenues and income to repay their debts. However, rapid growth in an economy is often associated with a deterioration in lending standards.
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  47. Start
    39886
    Prefix
    As a result, when a recession sets in, the risk of insolvency significantly increases, as weaker and less efficient businesses and consumers with burdensome mortgages fail to pay back their loans (e.g.,
    Exact
    Louzis et al., 2012; Klein and Olivei, 2008; Klein, 2013; and Claessens et al., 2014).
    Suffix
    In models that explain non-performing loans under uncertainty, we find no impact due 14Lepetit et al. (2008) argued that non-interest income activities such as cash withdrawal fees, bank account management, or data processing are considered to be riskier than traditional credit extension, as customers can easily switch banks for these types of services rather than standard banking ac
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  48. Start
    40057
    Prefix
    As a result, when a recession sets in, the risk of insolvency significantly increases, as weaker and less efficient businesses and consumers with burdensome mortgages fail to pay back their loans (e.g., Louzis et al., 2012; Klein and Olivei, 2008; Klein, 2013; and Claessens et al., 2014). In models that explain non-performing loans under uncertainty, we find no impact due 14
    Exact
    Lepetit et al. (2008)
    Suffix
    argued that non-interest income activities such as cash withdrawal fees, bank account management, or data processing are considered to be riskier than traditional credit extension, as customers can easily switch banks for these types of services rather than standard banking activities. 18 to inflation, changes in GDP and bank concentration.
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  49. Start
    40998
    Prefix
    We also find that the ratio of foreign debt to GDP also has a positive effect on non-performing loans. The presence of foreign banks has a negative impact on non-performing loans, possibly due to their ability to sift firms with low quality investment projects from their loan portfolio
    Exact
    (Claessens and Horen, 2014).
    Suffix
    In summary, our results reveal that uncertainty affects the stability of global financial institutions negatively, with higher liquidity asset holdings and lower asset quality. 5.4 Further evidences on country-income splits We have examined three different aspects of financial markets, financial depth, efficiency and stability, to provide a broader view on the impact of uncertainty on the heal
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  50. Start
    44424
    Prefix
    During periods of banking crises, both high and lowincome countries experienced significant lower profitability. In addition, we find that foreign banks were less profitable only in developed countries (Demirg ̈u ̧c-Kunt and
    Exact
    Huizinga, 1999).
    Suffix
    When we inspect the role of control variables, we find similar results compared to our earlier observations in most cases. One of the most interesting differences relates to the role of changes in GDP.
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  51. Start
    46316
    Prefix
    As columns 1 and 2 show, for high-income countries there is a strong positive relationship between foreign ownership and credit, while for low-income countries, we find a significant negative relationship. This may be because foreign banks increase access to financial services and enhance financial and economic performance of their borrowers
    Exact
    (Claessens et al., 2001).
    Suffix
    However, research has also shown that foreign banks can target certain type of borrowers undermining the consumers general access to financial services. Such actions in return worsen the credit pool and lower financial development in emerging countries where relationship lending is important (Claessens and Horen, 2014).
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  52. Start
    46646
    Prefix
    However, research has also shown that foreign banks can target certain type of borrowers undermining the consumers general access to financial services. Such actions in return worsen the credit pool and lower financial development in emerging countries where relationship lending is important
    Exact
    (Claessens and Horen, 2014).
    Suffix
    Although there is some variation in the impact of uncertainty between low and highincome countries, the results provide support for our claims that uncertainty adversely affects the health of financial intermediaries.
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  53. Start
    50700
    Prefix
    Given these results, it is clear that an increase in uncertainty significantly undermines the health of the financial system. Although the responses of some variables may appear to be low, it is useful to recall that seemingly small shocks can lead to large fluctuations in aggregate economic activity (see
    Exact
    Bernanke, 1983 and Bernanke and Gertler, 1989).
    Suffix
    For instance, the calculated change in financial depth, although small, may be sufficiently large enough to push the economy into a recessionary phase. Furthermore, the fact that we observe the highest response to a one sigma change in uncertainty on variables which we use to measure stability of the system (bank liquidity and non-performing loans) is worrisome as this confirms Bloom
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  54. Start
    51139
    Prefix
    Furthermore, the fact that we observe the highest response to a one sigma change in uncertainty on variables which we use to measure stability of the system (bank liquidity and non-performing loans) is worrisome as this confirms
    Exact
    Bloom (2009),
    Suffix
    who argues that sharp drops in economic activity happen in response to shocks to volatility. At this point, one can also consider the extent to which uncertainty would affect the financial system of a specific country of interest.
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  55. Start
    52503
    Prefix
    For Spain, we compute that financial 18Rather than examining the effect of a one standard deviation change in uncertainty, we calculate maximum effects. We follow this approach because uncertainty happens in bursts, for short periods of time, affecting the whole of the economy as discussed in
    Exact
    Bloom (2009).
    Suffix
    23 depth would decline by 3.1% and in the USA by 2%. When we compute expected changes in bank returns, we find that bank returns would declines by 5% in Spain and 3.4% in the USA. To ascertain these estimates it would be useful to carry out country-specific analyses as our predictions are based on elasticities obtained for a large cross-country panel dataset.
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  56. Start
    55123
    Prefix
    Our empirical 24 analysis also shows a 10% increase in non-performing loans of banks and 4% increases in bank liquid assets if uncertainty were to increase by one standard deviation. These figures suggest that uncertainty negatively affects the health of the financial sector. Given that small changes in the availability of credit can induce large fluctuations in an economy, as
    Exact
    Bernanke (1983) and Bernanke and Gertler (1989)
    Suffix
    discuss, attention should be paid to the overall health of the financial system rather just one aspect when examining factors that affect the financial markets. Further research focusing on country-specific bank level data would be desirable to understand uncertainty effects on the financial sectors of many economies. 25
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