scholarly journals Credit Risk, Capital, and Inefficiency: An Empirical Analysis of SAARC Banking Sector

2019 ◽  
Vol 13 (1) ◽  
pp. 41-54
Author(s):  
Shumaila Zeb ◽  
Zahid Ali
Keyword(s):  
Credit Risk ◽  
Empirical Analysis ◽  
Banking Sector ◽  
Risk Capital

scholarly journals An Empirical Analysis of Factors Affecting Bank Interest Margins: Evidence from the South East European Countries

2018 ◽  
Vol 21 (2) ◽  
pp. 81-98
Author(s):  
Mehmed Ganić
Keyword(s):  
Interest Rate ◽  
Empirical Analysis ◽  
Banking Sector ◽  
Output Growth ◽  
The South ◽  
Factors Affecting ◽  
East European ◽  
Interest Margins ◽  
See Region ◽  
Bank Concentration

This paper provides an empirical analysis of factors affecting Bank Interest Margins in eight countries of the South‑East European (SEE) region between 2000 and 2014. The purpose of this paper is to examine and investigate the main drivers of Bank Interest Rate Margins across selected countries throughout the SEE region. Also, the study explored the relationship between the dependent variable Interest Rate Spread (IRS – as a proxy variable for measuring variation in Bank Interest Rate Margins) and a set of selected banks’ specific variables in SEE by employing panel data estimation methodology. This research is based on aggregate data for the whole banking sector of each country. In line with some expectations, our findings confirm the importance of credit risk, bank concentration operative efficiency, and inflation expectations in determining Bank Interest Rate Margins. Interestingly, in contrast to the majority of recent empirical research, the study found an inverse relationship between the bank concentration variable and Bank Interest Rate Margins as well as between the operational efficiency variable and Bank Interest Rate Margins. Also, the study could not find statistically significant evidence that Bank Interest Rate Margins are determined by output growth, bank profitability (measured by ROA) or liquidity risk.

Ratings-based credit risk modelling: An empirical analysis

2007 ◽  
Vol 16 (5) ◽  
pp. 434-451 ◽  
Author(s):  
Pamela Nickell ◽  
William Perraudin ◽  
Simone Varotto
Keyword(s):  
Credit Risk ◽  
Empirical Analysis ◽  
Risk Modelling

The relationship between risk, capital and efficiency in Indian banking: Does ownership matter?

2019 ◽  
Vol 11 (2) ◽  
pp. 218-231
Author(s):  
Sanjukta Sarkar ◽  
Rudra Sensarma ◽  
Dipasha Sharma
Keyword(s):  
Public Sector ◽  
Credit Risk ◽  
Large Data ◽  
Foreign Banks ◽  
Risk Capital ◽  
Lower Efficiency ◽  
Data Set ◽  
Content Type ◽  
Balance Sheets ◽  
Indian Banks

Purpose This paper aims to examine the interplay between risk, capital and efficiency of Indian banks and study how their relationship differs across different ownership types. Design/methodology/approach Panel regression techniques are used to analyze a large data set of all Indian scheduled commercial banks operating during the period 2008-2016. Findings The results show that lower efficiency is associated with higher credit risk in the case of public sector and old private sector banks (”bad management hypothesis”). However, higher efficiency leads to higher credit risk in the case of foreign banks (“cost skimping hypothesis”). The authors further find that the more efficient institutions among public sector hold more capital. Finally, they find that the better-capitalized banks among those in the public sector have lower risks on their balance sheets (“moral hazard hypothesis”). Originality/value There is a paucity of papers on the interplay between risk, capital and efficiency of banks in emerging economies. This paper is the first to study the inter-relationship between risk, capital and efficiency of Indian banks across ownership groups using a number of different measures of risk.

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

scholarly journals Analysis of The Influence of Liquidity, Credit and Operational Risk, in Indonesian Islamic Bank’s Financing for The Period 2007-2013

2015 ◽  
Vol 17 (3) ◽  
pp. 279 ◽  
Author(s):  
Ousmane Diallo ◽  
Tettet Fitrijanti ◽  
Nanny Dewi Tanzil
Keyword(s):  
Credit Risk ◽  
Causal Relationship ◽  
Risk Sharing ◽  
Banking Sector ◽  
Panel Data Analysis ◽  
Banking System ◽  
Liquidity Risk ◽  
Operational Risk ◽  
Islamic Banking ◽  
Islamic Bank

The purpose of this paper is to analyze the influence of credit, liquidity and operational risks in six Indonesian’s islamic banking financing products namely mudharabah, musyarakah, murabahah, istishna, ijarah and qardh, in order to try to discover whether or not Indonesian islamic banking is based on the “risk-sharing” system. This paper relies on a fixed effect model test based on the panel data analysis method, focusing on the period from 2007 to 2013. The research is an exploratory and descriptive study of all the Indonesian islamic banks that were operating in 2013. The results of this study show that the Islamic banking system in Indonesia truly has banking products based on “risk-sharing.” We found out that credit, operational and liquidity risks as a whole, have significant influence on mudarabah, musyarakah, murabahah, istishna, ijarah and qardh based financing. There is a correlation between the credit risk and mudarabah based financing, and no causal relationship between the credit risk and musharaka, murabahah, ijarah, istishna and qardh based financing. There is also correlation between the operational risk and mudarabah and murabahah based financing, and no causal relationship between the operational risk and musharaka, istishna, ijarah and qardh based financing. There is correlation between the liquidity risk and istishna based financing, and no causal relationship between the liquidity risk and musharaka, mudarabah, murabahah, ijarah and qardh based financing. A major implication of this study is the fact that there is no causal relationship between the credit risk and musharakah based financing, which is the mode of financing where the islamic bank shares the risk with its clients, but there is an influence of credit risk toward mudarabah mode financing, a financing mode where the Islamic bank bears all the risk. These findings can lead us to conclude that the Indonesian Islamic banking sector is based on the “risk sharing” system.

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