International Journal of Applied Economics, Finance and Accounting

The influence of bank-specific variables on banks’ stability: Evidence from Saudi Arabia

2025-01-15T05:24:10+00:00

The goal of this study is to find out what makes Saudi Arabian banks unstable by using a panel data analysis with ten banks’ carefully chosen annual data from 2009 to 2022. Based on the fixed effect model, this study indicates that Saudi Arabian bank stability is unaffected by liquidity risk but is statistically and negatively impacted by credit risk and bank size. Conversely, capital adequacy and funding risk positively and statistically impact bank stability in Saudi Arabia. In light of these findings, we strongly recommend making capital adequacy requirements obligatory for bank management, given their beneficial effect on bank stability. This study recommended that bank management adopt practices such as safe loan provision and prompt customer repayment to mitigate credit risk. Bank managers have to guarantee liquidity adequacy in their banks and improve credit standards by increasing client supplemental requirements. While our study found that liquidity risk does not directly affect banks' financial stability, we propose that bank management should also focus on finding effective ways to generate client deposits to enhance financial stability further.

Extending the economic framework to model correlations between PD, LGD, and EAD

2025-01-15T05:49:16+00:00

This study examines the extension of the economic framework to correlations between PD, LGD, and EAD. We build on a framework that has already been used to figure out and adjust the relationships between loan portfolios’ Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Our analysis explores the implications of incorporating these correlations in portfolio losses, arguing that this structure enables institutions to apply forward-looking correlation models to assess the likelihood of obligor credit quality deterioration, commonly referred to as a significant increase in credit risk (SICR). According to International Financial Reporting Standards (IFRS)-9 regulations, the estimation of SICR and forward-looking information should not entail excessive cost or effort. In line with this principle, we contend that only a limited number of inputs are necessary to implement this robust framework, which allows users to evaluate meaningful forward-looking correlations, identify obligors likely to experience SICR, and ultimately measure a more accurate Expected Credit Loss (ECL). The adoption of this approach will allow institutions to better understand their credit risk and better assess their credit risk practices while adhering to regulatory requirements.

How funding liquidity influences bank lending: Empirical evidence from Vietnam

2025-01-15T06:07:35+00:00

This paper investigates the impact of funding liquidity on bank lending at 26 Vietnamese commercial banks in the period 2003–2023. Our paper uses panel data regression methods combined with endogeneity tests and robustness tests to produce consistent research results. The econometric methods used in the paper include multiple fixed-effects regression, the generalized method of moments (GMM), Prais-Winsten regression, Newey-West regression, and two-way clustering regression. Accordingly, the empirical results indicate that funding liquidity has a negative impact on Vietnamese commercial banks' loan growth. In particular, the results from the quantile regression model show that the negative impact of funding liquidity on bank lending becomes stronger for banks with higher loan growth. Furthermore, factors such as bank size, capitalization, and the cost-to-income ratio also have a negative impact on bank lending, whereas income diversification enhances banks' capacity to provide loans. Based on empirical research, this article also proposes some solutions to help Vietnamese commercial banks lend more safely and effectively, including: (i) improving funding liquidity management strategies to minimize negative impacts on lending activities; (ii) encouraging banks to diversify their income rather than relying solely on credit activities; (iii) enhancing banks' ability to manage costs and control their size. Investors, managers, and policymakers can all benefit from our conclusions and ramifications.

Macroeconomic effects of quantitative easing in the United States: New evidence between the global financial crisis and the COVID-19 periods

2025-01-21T10:48:26+00:00

This paper examines the impact of unconventional monetary policies, such as quantitative easing, on the U.S. unemployment rate during the financial crises and the Covid-19 pandemic. Most studies focus on the factors and monetary policies affecting unemployment during financial crises. Nevertheless, these policies may vary during health and social crises. In order to conduct our study, we used the ARDL (Autoregressive Distributed Lag) model, covering two distinct periods: from January 2007 to December 2018 for the first and from January 2019 to December 2022 for the second. The ARDL model is best suited for this study since it allows testing cointegration and estimating short- and long-term relationships when the series are not integrated of the same order. The study reveals that, during the Covid-19 period, the unemployment rate increases in the short and long term due to expansionary monetary policy. However, during financial crises, quantitative easing leads to a decrease in the unemployment rate over the same time horizons. The findings provide valuable insights into the effects of unconventional monetary policies and their influence on labour market reforms depending on the nature of the crisis.