信贷风险管理对孟加拉国商业银行盈利能力的影响:一个动态面板数据模型的估计

Raad Mozib Lalon, Farhana Morshada
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引用次数: 4

摘要

本文试图在考虑随机效应、固定效应和非随机效应估计的计量经济模型的情况下,揭示几个信贷风险因素如何影响商业银行的盈利能力,合并OLS和横截面广义最小二乘法,然后用一步GMM(广义矩方法)方法估计动态面板数据模型,以纳入从孟加拉国2010年至2019年银行年报中收集的数据集的内生性、未观察到的异质性和利润持久性问题。我们还采用了几种诊断检验,如模型规范检验、异方差检验、截面相关性检验、自相关检验和单位根检验,来检验本研究所选模型的有效性。我们对考虑所有方法的估计模型的第一部分实证研究表明,在所有独立的信贷风险因素中,如总贷款与总资产比率、总贷款与股权比率、不良贷款与总贷款比率、贷款损失准备金与总股本、总股本与总资产的比率,贷款总额与存款总额的比率和贷款损失准备金与不良贷款比率,只有贷款损失准备金对不良贷款比率对固定效应法下银行净息差比率衡量的因变量有显著影响。考虑相同方法的估计模型的下一部分实证结果表明,不良贷款占贷款总额的比率、不良贷款占总股本的比率和贷款损失准备金占总股本也显著影响了以银行净资产收益率衡量的因变量。我们对考虑相同方法的估计模型的第三部分实证结果表明,在所有方法下,只有不良贷款占总贷款的比率在统计上是显著的,但在固定效应和GLS方法下,不良贷款占股本的比率是显著的衡量银行的盈利能力。进一步研究表明,考虑到所有估计条件,一步系统GMM方法成功地采用了上述信用风险因素对银行ROE衡量的盈利能力的动态影响。
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Impact of Credit Risk Management on Profitability of Commercial Banks in Bangladesh: An estimation of Dynamic Panel Data Model
This paper attempts to reveal how several credit risk factors are affecting the profitability of commercial banks considering the econometric models estimated with Random effect, Fixed effect, Pooled OLS and Cross-sectional Generalized least square (GLS) method followed by dynamic panel data model estimated with one-step GMM (generalized methods of moments) approach to incorporate the issue of endogeneity, unobserved heterogeneity and profit persistence of data set collected from annual report of banks covering from year 2010 to 2019 in Bangladesh. We have also adopted several diagnostic checks such as Model specification test, test of heteroskedasticty, cross sectional dependence test followed by test of autocorrelation and unit root test to examine the validity of the models selected for this study. The first part of our empirical investigation of the estimated models considering all methods reveals that out of all the independent credit risk factors such as Total loans to total assets ratio, Total loans to equity ratio, NPL to total loans, NPL to Total equity ratio, Provision for loan losses to total equity, total equity to total assets ratio, Total loans to total deposits ratio and provision for loan losses to NPL ratio, only provision for loan losses to NPL ratio is significantly affecting the dependent variable measured with NIM (Net interest margin) ratio of banks under fixed effect method. The next part of our empirical results of estimated models considering same methods divulges that NPL to total loans ratio, NPL to Total equity ratio and Provision for loan losses to total equity are also significantly affecting the dependent variable measured with ROE (Return on equity) of banks. The third segment of our empirical findings of estimated models considering same approaches shows that only NPL to total loans ratio is statistically significant under all methods but the NPL to total equity ratio is significant under fixed effect and GLS method and Provision for loan losses to total equity is significant under GLS method only in explaining the changes in ROA (Return on equity) measuring profitability of banks. Further investigation reveals that the dynamic impact of the said credit risk factors on profitability measured with ROE of banks has been successfully adopted by one-step system GMM approach considering all conditions required for estimation.
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