2022
DOI: 10.1016/j.jbankfin.2020.105818
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How do bank-specific characteristics affect lending? New evidence based on credit registry data from Latin America

Abstract: Paper produced as part of the BIS Consultative Council for the Americas (CCA) research project on "Changes in banks' business models and their impact on bank lending: an empirical analysis using credit registry data" implemented by a Working Group of the CCA Consultative Group of Directors of Financial Stability (CGDFS).

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Cited by 17 publications
(6 citation statements)
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References 26 publications
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“…This effect is observed in the full sample (columns 1 and 3) and new loans' sample (column 4), indicating that internationalization lowers the potency of the bank lending channel by reducing the effects of monetary policy on loan rates. In the case of liquidity, we find that banks with higher liquidity transmit less the policy rate changes to loan rates compared to banks with less liquid assets (columns 3 and 4), confirming our findings in Table 3, and in line with the evidence in Kashyap and Stein (2000), Jiménez et al, (2014); Cantú et al, (2020). We also observe that, for the same level of liquidity, banks with higher subsidiaries transmit less the changes in policy rates to the interest rates of new loans than more domestic banks (column 4).…”
Section: The Bank Lending Channel Of Monetary Policy and The Internationalization Of Bankssupporting
confidence: 89%
See 1 more Smart Citation
“…This effect is observed in the full sample (columns 1 and 3) and new loans' sample (column 4), indicating that internationalization lowers the potency of the bank lending channel by reducing the effects of monetary policy on loan rates. In the case of liquidity, we find that banks with higher liquidity transmit less the policy rate changes to loan rates compared to banks with less liquid assets (columns 3 and 4), confirming our findings in Table 3, and in line with the evidence in Kashyap and Stein (2000), Jiménez et al, (2014); Cantú et al, (2020). We also observe that, for the same level of liquidity, banks with higher subsidiaries transmit less the changes in policy rates to the interest rates of new loans than more domestic banks (column 4).…”
Section: The Bank Lending Channel Of Monetary Policy and The Internationalization Of Bankssupporting
confidence: 89%
“…Our work contributes to three strands of literature. First, we show that banks' internationalization contributes to insulating their credit supply due to changes in policy rates, extending thereby the evidence on the traditional bank-lending channel through the strength of banks' balance-sheets (i.e., capitalization and liquidity) Stein, 1995, 2000;Altumbas et al, 2010;Jiménez et al, 2012;Gertler and Kiyotaki, 2010;Cantú et al, 2020;Altavilla et al, 2020a). We also observe that internationalization is associated with a lower search for yield, suggesting that banks with higher operations abroad have more access to foreign investment opportunities (i.e., higher diversification), and reduce their risk-taking during periods of easing domestic monetary policy.…”
Section: Introductionsupporting
confidence: 65%
“…Gambacorta and Shin (2018) found that a 1-percentage point increase in the equity-to-total assets ratio is associated with a 0.6 percentage point increase in lending growth per year. These findings indicate that a larger capital base reduces the banks' financial constraint, allowing them to grant more loans to the economy (see also Cantú et al 2019) and provide the grounds for our third hypothesis:…”
Section: Literature Review Theoretical Framework and Hypothesis Devel...mentioning
confidence: 72%
“…The sample data were collected from Thompson Reuters Data stream daily closing price, , of BANKSEK, BANKSLA, BRIC (BANKSBC), PIIGS (BANKSPI) and BANKSFE to compute continuously compounded 1-trading day returns, i.e., .Given our understanding of the past literature, other authors have used these five groups to analyse the non-western banking sector. Other authors who used these sorts of groupings are BANKSEK ( Bui et al., 2021 ; Tian et al., 2021 ), Latin America (BANKSLA) ( Cantú et al., 2020 ; Nagels, 2021 ), BRIC (BANKSBC) ( Karagiannis et al., 2014 ), Portugal, Ireland, Italy, Greece, and Spain – PIIGS (BANKSPI) ( Miguélez et al., 2019 ) and Far East (BANKSFE) ( Miguélez et al., 2019 ). The researchers applied the Dynamic Conditional Correlation (DCC) Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models.…”
Section: Methodsmentioning
confidence: 99%