Short-Term Debt and Bank Risk

Du, Brian; Palia, Darius

doi:10.1017/s0022109017001132

Cited by 17 publications

(8 citation statements)

References 68 publications

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“…Contrary to our expectations, we find a negative and statistically significant relationship between our measure of funding structure and idiosyncratic risk. To explain this result, we rely on the study of Du and Palia [ 44 ] which suggests that the market perceives as positive the ability of a bank to sustain a funding structure unbalanced towards short-term debt.…”

Section: Baseline Resultsmentioning

confidence: 99%

Capital and asset quality implications for bank resilience and performance in the light of NPLs’ regulation: a focus on the Texas ratio

2022

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Based on a sample of 63 listed European banks, this paper investigates the relationship of capital and asset quality, in terms of provisioning and coverage policies, with bank risk and performance during the period 2005Q1-2018Q4. Our results point out different relationships between risk-based and non-risk-based measures of capital with bank risk and performance profiles. In particular, the information content of the leverage ratio appears to be merely related to the bank dimensional feature, whereas the total capital ratio shows a positive and statistically significant relationship with bank stability and is also negatively related to insolvency risk, thereby suggesting a crucial role for capital for the overall bank resilience. In addition, more capitalized banks are associated with higher bank performance. Regarding asset quality, hefty coverage and provisioning policies are generally associated with both lower bank resilience and performance. These results are relevant for disentangling the implications that the regulatory overhaul set out to address the NPLs issue is having on banking activity.

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Section: Baseline Resultsmentioning

confidence: 99%

Capital and asset quality implications for bank resilience and performance in the light of NPLs’ regulation: a focus on the Texas ratio

2022

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“…Many recent studies adopted the two-way cluster regression in the finance and economics literature such as Benlemlih and Bitar (2018), Chemmanur et al (2018), Ciftci and Darrough (2016), and Prado et al (2016). Similar to our study, Du and Palia (2018) use this approach to examine bank risk and Perryman et al (2015) investigate the influence of board gender diversity on firm performance and risk. Table 1 provides descriptive statistics of all variables.…”

Section: Estimation Methodologymentioning

confidence: 99%

Does board gender diversity matter in the banking sector? Evidence from Tunisia

Othmani

2021

African Development Review

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The purpose of this paper is to investigate the effects of board gender diversity on banks' performance and risk for the case of a developing African country. Our sample includes a unique data set of Tunisian banks during the period 2005-2018. We use the two-way cluster regression proposed by Petersen. This approach corrects for the unobserved firm effect (time-series dependence) and time effect (cross-sectional dependence). It gives robust standard errors adjusted for heteroscedasticity, serial correlation, and cross-sectional correlation. Our results support a positive relationship between gender diversity and banks' performance measured by ROA and ROE, while women board members are associated with more default risk measured by Z-score. Our results remain robust to various measures of gender diversity, banks' performance and risk. The findings contribute to the literature by providing empirical evidence from Tunisia, an African emerging economy, where the examination of the role of board gender diversity on bank governance is unexplored. 1 | INTRODUCTION Gender equality in rights, education and employment represent most development challenges facing countries globally,

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“…It is important to account for credit risk as it helps to isolate the effect of capital on liquidity creation from the role of capital in supporting the risk transformation function of banks (Berger & Bouwman 2009). We proxy for credit risk using the ratio of non-performing loans to total loans (NPL) as in Du & Palia (2018). We also control for bank capitalization, denoted by CAP, which is measured by the ratio of equity to total assets (Berger & Bouwman 2009).…”

Section: Control Variablesmentioning

confidence: 99%