The Relationship between Liquidity Risk and Credit Risk in Banks

Imbierowicz, Björn; Rauch, Christian

doi:10.2139/ssrn.1884917

Cited by 87 publications

(142 citation statements)

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“…To test the null hypothesis of a moral hazard problem, we have used dynamic GMM estimation, panel data techniques of fixed effect and random effect, and pool data framework, following many existing studies (Espinoza and Prasad 2010;Louzis et al 2012;Horvath et al 2014;Imbierowicz and Rauch 2014;Ghosh 2015). First of all, we used system GMM estimation technique developed by Arellano and Bover (1995) and Blundell and Bond (1998).…”

Section: Regression Frameworkmentioning

confidence: 99%

Non-performing loans (NPLs), liquidity creation, and moral hazard: Case of Chinese banks

Umar

Sun

2016

China Financ. and Econ. Rev.

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Background: This study analyzes the impact of non-performing loans (NPLs) on bank liquidity creation to investigate the existence of moral hazard problem in Chinese banks. Methods: It uses data from 197 listed and unlisted Chinese banks, spanning the period 2005 to 2014. Generalized method of moments (GMM) estimation, fixed and random effect model, and pool data techniques have been used for analysis. Results: Total liquidity creation by Chinese banks is declining, and NPLs ratio has started to increase following a continuous decline between 2005 and 2012. We find that liquidity creation by Chinese banks does not depend on NPLs ratio. We repeated the analysis for small and large banks and the results of these subsamples reinforced our findings for the aggregate sample. Conclusions: We did not find the evidence of moral hazard problem in Chinese banks.

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Section: Regression Frameworkmentioning

confidence: 99%

Non-performing loans (NPLs), liquidity creation, and moral hazard: Case of Chinese banks

Umar

Sun

2016

China Financ. and Econ. Rev.

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“…Such contractual agreements and their defaults also leads to aggregate default risk that is financially more harmful for the companies regarding their firm specific factor's as well (Imbierowicz and Rauch, 2014). Thus, such firm specific factors and industrial factors have negative relationship with credit risk (Abdullah, Parvez and Ayreen, 2014).…”

Section: Introductionmentioning

confidence: 99%

Impact of Firm Specific Factors on Credit Risk: A Case of Karachi Stock Exchange

Iqbal

Chaudry

Iqbal

2015

ILSHS

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Abstract. The current study aims to explore the relationship of firm's specific factors i-e profitability, ROA, leverage and bank size on credit risk. The population of the study consists of manufacturing sector of Pakistan. The sample of study is cement sector of Pakistan. The sample units are 22 and listed at Karachi stocks exchange. The multivariate regression analysis is used to test the data of sample. The study revealed negative significant relationship of all firm specific factors with credit risk in Pakistan. Thus, the study supported historic investigations regarding credit risk.

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“…From an economic aspect, the long-term bank liquidity is understood in the sense of bank solvency, hence like such a minimum rate of return on nonliquid assets that will provide for the ongoing payment of bank liabilities at liquidity outflow of α for any t. Bank solvency and liquidity are mutually conditioned situations within which it is not defined whether the potential insolvency of a bank temporally precedes bank nonliquidity or vice versa (conditionality of credit and liquidity risk in relation to default probability see Imbierowicz & Rauch, 2014 …”

Section: Economic View On Liquidity Shocks and Sustainability Of Bankmentioning

confidence: 99%

Systemic Liquidity Shocks and Banking Sector Liquidity Characteristics on the Eve of Liquidity Coverage Ratio Application - The Case of the Czech Republic¹

Brůna

Blahová

2016

Journal of Central Banking Theory and Practice

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Abstract:The paper contains an analysis of the economic and regulatory concept of bank liquidity in the context of systemic liquidity shock. A formal model analysis shows that the application of liquidity coverage ratio (LCR) based on Basel III will lead to a significant adaptation of banks liquidity management. LCR causes a change in bank´s liquidity allocation and funding to be less effective and more costly and restrictive for providing credits comparing with economic determinants. It is demonstrated that the application of LCR underestimates actual liquidity position of a bank and leads to allocation ineffectiveness. The empirical part contains simulation of impacts of systemic liquidity shock on the banking sector´s ability to withstand the unfavourable credit shock while solvency is maintained. The results confirm the robustness of the Czech banking system ensuing from the systemic surplus of liquidity, high volume of bank capital and its high profitability. The estimations of the VAR model show that the relations between liquidity characteristics of banks, sources of aggregate liquidity shock, interbank market illiquidity and the credit facilities of the Czech National Bank are relatively weak, supporting the conclusion that the banks face liquidity shocks of non-persistent character.

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The Relationship between Liquidity Risk and Credit Risk in Banks

Cited by 87 publications

References 5 publications

Non-performing loans (NPLs), liquidity creation, and moral hazard: Case of Chinese banks

Non-performing loans (NPLs), liquidity creation, and moral hazard: Case of Chinese banks

Impact of Firm Specific Factors on Credit Risk: A Case of Karachi Stock Exchange

Systemic Liquidity Shocks and Banking Sector Liquidity Characteristics on the Eve of Liquidity Coverage Ratio Application - The Case of the Czech Republic¹

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The Relationship between Liquidity Risk and Credit Risk in Banks

Cited by 87 publications

References 5 publications

Non-performing loans (NPLs), liquidity creation, and moral hazard: Case of Chinese banks

Non-performing loans (NPLs), liquidity creation, and moral hazard: Case of Chinese banks

Impact of Firm Specific Factors on Credit Risk: A Case of Karachi Stock Exchange

Systemic Liquidity Shocks and Banking Sector Liquidity Characteristics on the Eve of Liquidity Coverage Ratio Application - The Case of the Czech Republic1

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Systemic Liquidity Shocks and Banking Sector Liquidity Characteristics on the Eve of Liquidity Coverage Ratio Application - The Case of the Czech Republic¹