Asset-Liability Management (ALM) of banks is defined as simultaneous planning of all bank assets and liabilities under different conditions and its purpose is to maximize profits and minimize the risks in banks by optimizing the parameters in the balance sheet. Most of the studies `and proposed models in the ALM field are based on an objective function that maximizes bank profit. It is not easy to apply changes in these models in order to reach the optimal values of the parameters in the balance sheet. In this article, an attempt has been made to propose a linear model using constraints to achieve optimal values of balance sheet parameters using ALM objectives and considering balance sheet, system and regulatory constraints. It has also been tried to design the model according to the most possible mode and with the least changes and to minimize the size of the balance sheet. The analysis of the model presented in this article has been conducted using the parameters of the balance sheet and income statement of one of the famous Iranian banks. The results obtained from the proposed model show that the values of cash and receivables from banks and other credit institutions have decreased by 30% and increased by 200%, respectively, compared to the actual values of these parameters. Also, Total Income, Operating Income and Non-Operating Income have grown by 30% compared to the actual values of these parameters. Also, the values of a number of parameters are estimated to be zero after optimization. According to the results, it is obvious that the performance of bank managers, especially in the management of bank assets, is significantly different from the optimal values of the balance sheet, and the results obtained from the proposed model can help the management of banks as much as possible.
The aim of this article is to investigate the dynamic correlation between the Global Economic Policy Uncertainty index (GEPU) and Non-Performing Loans (NPL) in Iran. The relationship between economic uncertainty and banking performance indices is significant because of the systemic importance of banks in every economy. We evaluated this relationship in this developing country, especially under economic sanctions. In this study, we used the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) to assess the relationship between Global Economic Policy Uncertainty and Non-Performing Loans of Iranian banks using the statistics of these two indicators by R and Eviews programming and statistical software in the period from 2004 to 2021. Our results show that Iranian banks' Non-Performing Loans (NPL) are rather associated with Global Economic Policy Uncertainty (GEPU) during major global shocks such as the global financial crisis in 2008 or the Covid-19 pandemic. However, despite fluctuations in the correlation between Non-Performing Loans and Global Economic Policy Uncertainty over time, this study also illustrates that these correlations in some periods are generally somewhat low that some of the reasons could be the sanctions imposed on Iran's economy and banking system, imposed loans to banks by the government, forced interest rate, etc., which led to a limited connection among Iranian banks and global banking system. To prove this claim we estimate the model for some countries with an open economy, like Japan, Singapore, the US, Turkey, and Spain. The result shows that this correlation is much higher in comparison to Iran.
Background: To achieve the objectives of social security and health system, it is essential to enhance the level of efficiency of social insurance funds. To hit this target, the challenges of regulation improvement should be identified and addressed in these funds. Therefore, the present study aimed at identifying the key challenges of regulation and modeling the network of management and its effective factors. Methods: This was a cross-sectional qualitative study. In the qualitative part, a systematic review and thematic analysis were conducted using MaxQDA 10 software. Qualitative data were collected by reviewing the relevant documents and related studies. In the quantitative part, the structural-interpretative modeling and Mick-Mac analysis methods were employed. The data were collected by referring to the 14 experts selected by targeted sampling method using a questionnaire. Results: 9 factors were identified as the key challenges in regulation of social insurance funds, including 1) management inconsistency; 2) non-clear insurance approach in population coverage; 3) multiplicity of insurance funds; 4) failure to organize the funds; 5) lack of integrity of rules and regulations; 6) weakness of regulatory authority; 7) weak policy making in management of financial resources; 8) inconsistencies in the country’s insurance tariff system; and 9) insurance cover for informal jobs. The content network was formed in a 4-level model by structural- interpretative modeling method. Conclusion: Regulation challenges of the social insurance funds include a combination of factors showing the need for the formation of a unit regulator to organize these funds. The aim is to meet the key challenges in 4 layers of the proposed model and modify the regulatory and supervisory structure with a comprehensive, impartial, and specialized approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.