Bad Luck or Bad Management? Emerging Banking Market Experience

Podpiera, Jiří; Weill, Laurent

doi:10.2139/ssrn.965785

Cited by 36 publications

(37 citation statements)

References 17 publications

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“…To address these issues, we use the system GMM estimators developed for dynamic panel models by Arellano and Bover (1995) and Blundell and Bond (1998). Podpiera and Weill (2008) and Fiordelisi et al (2011) also use similar frameworks.…”

Section: The Granger-causality Frameworkmentioning

confidence: 99%

“…Our study is limited to a single country, as in these studies, because we require very detailed data (e.g., Podpiera and Weill 2008;Pruteanu-Podpiera, Weill and Schobert 2008, for Czech banks;Casu and Girardone 2009;and Fiordelisi et al 2011, for European banks). Measuring liquidity creation requires this kind of data because balance-sheet items need to be classified to compute liquidity creation measures.…”

Section: Introductionmentioning

confidence: 99%

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Bank Capital and Liquidity Creation: Granger-Causality Evidence

2013

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We examine the relation between capital and liquidity creation. This issue is interesting because of the potential impact on liquidity creation from tighter capital requirements such as those in Basel III. We perform Granger-causality tests in a dynamic GMM panel estimator framework on an exhaustive data set of Czech banks, which mainly includes small banks from 2000 to 2010. We observe a strong expansion in liquidity creation until the financial crisis that was mainly driven by large banks. We show that capital negatively Granger-causes liquidity creation in this industry, where majority of banks are small. But we also observe that liquidity creation Granger-causes a reduction in capital. These findings support the view that Basel III can reduce liquidity creation, but also that greater liquidity creation can reduce banks' solvency. Thus, we show that this reverse causality generates a trade-off between the benefits of financial stability induced by stronger capital requirements and the benefits of increased liquidity creation. JEL Classification: G21, G28Keywords: Bank capital, Liquidity creation, Basel III 2 Nontechnical SummaryThis paper examines the relation between capital and liquidity creation, which is a comprehensive measure of a bank's overall ability to finance relatively illiquid assets with relatively liquid liabilities and thereby serve as a financial intermediary. We test the relation between bank capital and liquidity creation by using an exhaustive dataset of Czech banks from the Czech National Bank from 2000 to 2010. This way we propose a broad perspective on the interactions between capital and liquidity creation in the banking industry. In doing so, we are able to provide evidence on capital requirements limiting liquidity creation, which is highly relevant for appraising the economic implications of the capital requirements in the Basel III reforms.We show, especially for small banks, that capital is found to negatively Granger-cause liquidity creation. However, we also observe that liquidity creation Granger-causes capital reduction. We thus support the view that there is a negative, bi-causal relation between capital and liquidity creation, which corroborates the importance of examining this causality.Our findings have two policy implications for small banks. First, they suggest that the Basel III Accords might lead to reduced bank liquidity creation by introducing tighter capital requirements. Second, our findings support the view that greater liquidity creation may hamper bank solvency.Overall, our primary conclusion is that there is a trade-off between the benefits of financial stability induced by stronger capital requirements and the benefits of greater liquidity creation. Therefore, any action in favor of one objective would deteriorate the other.A possible caveat is that our findings might be dependent on our sample and might not be easily generalizable. However, the Basel III rules are planned to be implemented for a vast array of countries, including that examined here an...

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Section: The Granger-causality Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bank Capital and Liquidity Creation: Granger-Causality Evidence

2013

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“…Most of them show that because of banks can choose their level of operating costs, monitoring cots and capitalization to manage their level of risk exposure (e.g. adequate proportion of riskier loans), the consequences of a bad management or higher risk-taking, by skimping or moral hazard incentives, would lead to both inefficiency and higher bad loans (see Williams, 2004;Altunbas et al, 2007;Lepetit et al, 2008;Podpiera and Weill, 2008;Tabak et al, 2011).In general, they find a negative relationship between NPLs and cost efficiency and concur that banking supervisors should focus on enhancing bank cost efficiency and bank capital in order to reduce the probability of bank failures and to support financial stability objectives. However, little is known about how differences in risk exposure affect the efficiency of banks with different characteristics.…”

Section: Risk-taking and Bank Efficiencymentioning

confidence: 99%

“…The seminal work of Berger and DeYoung (1997) showed that banks with lower efficiency tend to exhibit higher ratio of bad loans and in turn those banks are more prone to default than banks with higher efficiency and lower share of bad loans. Thus, bank efficiency indicators have been used as a potential measures of bank failures (Podpiera and Weill, 2008). Likewise, the modern banking theory highlights that risk-taking is an inherent element of the banking production which should be properly modeled into the efficiency measurement (Hughes et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Effects of Risk-Taking on Bank Efficiency: A Stochastic Frontier Model with Random Coefficients

Sarmiento

Galán

2014

SSRN Journal

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We estimate a stochastic frontier model with random inefficiency parameters, which allows us not only to identify the role of bank risk-taking on driving cost and profit inefficiency, but also to recognize heterogeneous effects of risk exposure on banks with different characteristics. We account for an integral group of risk exposure covariates including credit, liquidity, capital and market risk, as well as bank-specific characteristics of size and affiliation. The model is estimated for the Colombian banking sector during the period 2002-2012. Results suggest that risk-taking drives inefficiency and its omission leads to over (under) estimate cost (profit) efficiency. Risk-taking is also found to have different effects on efficiency of banks with different size and affiliation, and those involved in mergers and acquisitions. In particular, greater exposures to credit and market risk are found to be key profit efficiency drivers. Likewise, lower liquidity risk and capital risk lead to higher efficiency in both costs and profits. Large, foreign and merged banks benefit more when assuming credit risk, while small, domestic and non-merged banks institutions take advantage of assuming higher market risk.

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“…External factors have been seen as macro-economic variables such as currencies, real interest rates or unemployment rates or the indexes (Podpiera and Weill, 2008), whereas internal factors are seen as bank specific factor like size, profitability or cost efficiency measurements (Berge and Boye, 2007) There are also other interesting researches which are concentrated on the internal process to determine the non-performing loan factors. Li et all (2007) in their research scrutinized the incentive contracts and managerial efforts.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Macroeconomic Variables on Non-performing Loans in Turkish Banking Sector

Karahanoğlu¹

2015

jisr

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Non-performing loans are one of the most important data which is used to determine efficiency of the banks as well as the health of the banking sector and the financial crisis effects. In this study, the relationship between the non-performing loans in Turkish banking sector and the macro economic variables which point out the general economic condition as stock exchange index over biggest 100 companies (BIST100), currency values as EUR and USD and the industry production index (SUE) over the years between 2005 and 2015 is analysed. It was seen that there is causal relationships between those variables with non-performing loans which is measured as non-performing loans / total loans.Keywords: Non-performing Loans, BIST100, SUE, VAR. IntroductionThe nonperforming loans and their patterns are not only the issue of the banks itself as a measure of the performance and profitability also it is central concern of the regulatory bodies as well as the policy makers which would be accepted as a general sign for the current state of the economy (Reinhard and Rogoff,2010). Such variable is one of the basic indicators for the economic crisis as well.In the literature it is seen that in the most of the studies which aimed at determining the indicator of non-performing loans, those indicators are classified into two main groups as the internal and external factors (Louzis et all, 2011). External factors have been seen as macro-economic variables such as currencies, real interest rates or unemployment rates or the indexes (Podpiera and Weill, 2008), whereas internal factors are seen as bank specific factor like size, profitability or cost efficiency measurements (Berge and Boye, 2007) There are also other interesting researches which are concentrated on the internal process to determine the non-performing loan factors. Li et all (2007) in their research scrutinized the incentive contracts and managerial efforts. They have found that such incentive and efforts would decrease the nonperforming loans significantly.When the Turkish economical history is investigated through the big economic crisis which were reinforced by the deteriorations of the banks in years 1999 and 2001, it is seen that such crisis were reinforced with the non-performing loans.In this research, the relationship between the nonperforming loan and macro-economic variables such as currencies, stock exchange index, and industrial production index were investigated and VAR methodology is used to enlighten the direction of the relationship. Moreover the shock effect of each of those variables on non-performing loans are also analysed by impulse response functions. Literature ReviewNon-performing loans can be defined as the corruption of the payment relationship between the bank and the borrower which might cause the delays in payments and also the loss (Golin and Delhaise,2000). Non-perfoming loans have always been the main research points of the different scholars. Louzis et all (2011) in their study concentrated on the nonperforming loans in Greece a...

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Bad Luck or Bad Management? Emerging Banking Market Experience

Cited by 36 publications

References 17 publications

Bank Capital and Liquidity Creation: Granger-Causality Evidence

Bank Capital and Liquidity Creation: Granger-Causality Evidence

Heterogeneous Effects of Risk-Taking on Bank Efficiency: A Stochastic Frontier Model with Random Coefficients

The Effect of Macroeconomic Variables on Non-performing Loans in Turkish Banking Sector

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