Redirigiendo al acceso original de articulo en 23 segundos...
Inicio  /  Computation  /  Vol: 10 Par: 10 (2022)  /  Artículo
ARTÍCULO
TITULO

The Impact of Financial Development and Macroeconomic Fundamentals on Nonperforming Loans among Emerging Countries: An Assessment Using the NARDL Approach

Aamir Aijaz Syed    
Muhammad Abdul Kamal    
Simon Grima and Assad Ullah    

Resumen

The relationship between financial development indicators and non-performing loans (NPLs) has garnered significant attention, especially in emerging countries. The puzzle of whether financial sector development increases or decreases Non-performing Loans (NPL)s has not been resolved to the satisfaction of the curious mind. This research attempts to answer the above question by studying the asymmetric and symmetric association between financial sector development and NPLs, by utilizing the novel non-linear autoregressive distribution lag (NARDL) and the linear autoregressive distribution lag (ARDL) approach. Moreover, to make the study inclusive, we have added a series of proxies to measure financial sector development and macroeconomic vulnerabilities. Our main findings confirm that financial sector development and NPLs move together in the long run, and there is significant evidence of the asymmetric relationship. We infer that NPLs react differently to the negative and positive shocks of financial development and macroeconomic variables both in the short and long run. In the long-run positive shocks in financial intermediation, banking efficiency, banking depth, banking stability index, and banking non-interest income significantly impact the NPLs in emerging countries. The positive shocks of financial sector development (financial intermediation and size of banks) increase NPLs in emerging countries and vice-versa. Furthermore, regarding the macroeconomic variables, the positive shock of inflation, unemployment, and interest rate positively affect NPLs. The empirical analysis also concludes that in the long-run foreign bank presence is an insignificant factor affecting NPLs in the selected countries. This study emphasizes that, unlike the linear model, the non-linear model provides a more realistic and robust result by highlighting hidden asymmetries, which will help policymakers make appropriate strategic decisions.

 Artículos similares

       
 
Tasha Austin and Bharat S. Rawal    
The purpose of this study is to show how machine learning can be leveraged as a tool to govern social impact and drive fair and equitable investments. Many organizations today are establishing financial inclusion goals to promote social impact and have b... ver más
Revista: Algorithms

 
Olegs Cernisevs, Yelena Popova and Dmitrijs Cernisevs    
Risk management is a highly important issue for Fintech companies; moreover, it is very specific and puts forward the serious requirements toward the top management of any financial institution. This study was devoted to specifying the risk factors affec... ver más
Revista: Informatics

 
Pungky Lela Saputri, Muhammad Faisal Yul Zamrudi     Pág. 73 - 81
This study aims to analyze the effect of financing disbursed by Islamic banks on reducing the poverty rate in Indonesia. This study used timeseries data tested using multiple linear regression analysis. Sharia bank financing data was obtained from Sharia... ver más

 
Andrea Grilli and Alex Balzi    
Local Road Administrations (LRA) manage wide and fragmented road networks with constrained financial and human resources. Though LRA manage the most road networks and the relative development and maintenance have a huge impact on environment and society,... ver más
Revista: Infrastructures

 
Jinhong Wu, Konstantinos Plataniotis, Lucy Liu, Ehsan Amjadian and Yuri Lawryshyn    
Synthetic data, artificially generated by computer programs, has become more widely used in the financial domain to mitigate privacy concerns. Variational Autoencoder (VAE) is one of the most popular deep-learning models for generating synthetic data. Ho... ver más
Revista: Algorithms