رامین مجاب

Technological advancements have transformed the payments industry. In the competition between banknotes and alternative instruments such as debit cards and mobile payments, the question of Central Bank Digital Currency (CBDC) has emerged in recent years and sparked much debate. This research examines the factors influencing public adoption of CBDC in two parts. From a macroeconomic perspective, demand for money encompasses the demand for the various functions of money—such as a means of payment and as a type of asset. Drawing on conventional theories of money demand, we analyze this demand over the long term. Since CBDC is essentially fiat money with characteristics similar to physical currency, but does not compete with cash due to the latter’s unique features (e.g., anonymity), it serves as a better substitute for demand deposits. With this view of CBDC, the findings related to demand deposits can be attributed to CBDC demand. In this study, using data on the number of point-of-sale (POS) terminals, debit cards, and ATMs, a logistic model is estimated to calculate a digital development index for Iran’s economy. One key result is the positive and significant effect of this digital development index on demand for demand deposits (as a proxy for CBDC demand), and its negative and significant effect on demand for banknotes. In the second part, CBDC is examined as a payment instrument. Here, the factors influencing CBDC adoption are categorized into four groups: technological, demographic, financial-economic, and operational.

Relying on the Area Under the Curve (AUC) measure, we compare the performance of the Logit regression model and the LightGBM algorithm. Despite these methods being common in the literature, our study emphasizes the role of statistical inference to evaluate and compare the results comprehensively. We use the training set of the Vesta (2018) dataset, provided by Vesta—a global fraud prevention company headquartered in the United States specializing in payment solutions and risk management. Originally released as part of a Kaggle competition focused on credit card fraud detection, this dataset comprises diverse transaction records, representing a rich source for exploring advanced fraud detection methods. Our analysis reveals that while the LightGBM algorithm generally yields higher predictive accuracy, the differences between the calculated AUCs of the two methods are not statistically significant. This underscores the importance of using inferential techniques to validate model performance differences in fraud detection.

Aim and Introduction :

Credit risk is the possibility of a loss resulting from a borrower's failure to repay a loan or meet contractual obligations. Traditionally, it refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection. Although it is impossible to know exactly who will default on obligations, properly assessing and managing credit risk can lessen the severity of a loss. Interest payments from the borrower or issuer of a debt obligation are a lender's or investor's reward for assuming credit risk.When the borrower remains financially healthy and pays the agreed instalments and interest as scheduled, the loan is said to be performing. But there is always the risk that the company or individual will not be able to repay within the agreed timespan. If this happens or looks likely to happen, the bank must classify the loan as “non-performing”. A bank loan is considered non-performing when more than 90 days pass without the borrower paying the agreed instalments or interest. Non-performing loans are also called “bad debt”. To be successful in the long run, banks need to keep the level of bad loans at a minimum so they can still earn a profit from extending new loans to customers. If a bank has too many bad loans on its balance sheet, its profitability will suffer because it will no longer earn enough money from its credit business. In addition, it will need to put money aside as a safety net in case it needs to write off the full amount of the loan at some point in time.

This study with a new approach examines the determinants of credit risk in Iranian banks from 2006 to 2019. Province, banking groups and time are three dimensions used in the modeling of this study as explanatory variables of credit risk. Furthermore, a three-dimensional panel data model is used to measure the coefficients of independent variables. In the case of two-dimensional panels, each observation is typically a vector of values of a dependent variable and one or more independent variables, and comes with two labels attached, one is frequently time and the other an individual person, business or nation. When the panel is multi-dimensional, each observation comes with many labels, for example, time, individual employee, firm, and industry. An observation could consist of values of multiple endogenous variables and multiple exogenous or predetermined variables, labeled with at least time and one other label. All of the problems and issues which arise for two-dimensional panels also exist for multi-dimensional panels.

Findings :

The results of the study indicate that access to provincial credit has a positive effect and the size of the provincial banking sector has a negative impact on the provincial credit risk. In addition, among the variables of the regional economics, the provincial unemployment rate and the provincial real economic growth rate affect positively the provincial credit risk, and the provincial Gini coefficient variable affect negatively the provincial credit risk. The index of road network accessibility as a sensitive variable has a negative influence on the credit risk of the province, which means that in regions where the index of road network accessibility is larger, the cost of access for economic enterprises is reduced, so the profit margin and the ability to repay facilities by the enterprise increases and less default occurs.

The banking system is subject to some risks in attaining its goals; one of the most important of which is encountering non-performing loans and ultimately write-offs. The emergence and accumulation of NPLs can become a systemic problem when this affects a considerable part of the financial system, threatening its stability and/or impairing its core function of facilitating financial intermediation. A significant increase in NPLs throughout the system can have a negative impact on the resilience of the banking sector to shocks, thus increasing systemic risk. NPLs may also be associated with higher funding costs and a lower supply of credit to the real economy. This may result from negative market sentiment towards banks with high levels of NPLs, which decreases banks’ ability to access liquidity and capital markets (potentially leading to credit supply constraints). In order to reduce credit risk, the necessary policies should be adopted to take into account the considerations of the regional economics in payment of facilities.

The high volume of non-performing loans and its trend over time is one of the main problems of banks and central banking in Iran. This high volume of non-current receivables and the destructive effects that this phenomenon has, has made it very important to identify and examine the role of the factors affecting it. In the present study, determinant factors from the Borrower, lender and regional economic factors affecting the non-performing loans of the country's banking network for four banking groups: commercial government, specialized government, privatized and private in 31 provinces of Iran for the years 1385-1398 has been studied. The regional dimension in the analysis of non-performing loans is the distinguishing feature of this study from similar studies that have been questioned by the authors of the article. In a geographical area, economically, the most important aspect of appearance is the density of population and the agglomeration of industrial activities, which has arisen due to the agglomeration economies, and with its expansion, the production costs of the enterprise are reduced. Therefore, the enterprises that can benefit the most from these agglomeration economies are expected to be more successful in repaying bank loans. For this purpose, data on non-performing loans, payment facilities and bank deposits for all provinces of Iran were mentioned separately for the four banking groups and data on other related variables were collected provincially. The industrial agglomeration index was also calculated using the Gorter-kok equation for the provinces of Iran. Then, using the econometric technique, the fixed effects of unbalanced panel data were determined more payment facilities, more bank deposits and higher unemployment rate cause a significant increase in non-performing loans, while higher inflation and higher industrial agglomeration reduce non-performing loans. The clearer conclusion is that the size of industrial agglomeration has a negative effect on the size of non-performing loans. These findings suggest that stress tests designed to measure the quality of banks' payment facilities and probability of default of borrowers, in addition to the capital adequacy and credit quality of banks, should also consider the impact of regional economic conditions on non-performing loans.

After the financial crisis of 2007-2009, in which liquidity problems led to insolvency and consequently the bankruptcy of many large banks and financial institutions such as Lehman Brothers, Basel Committee on Banking Supervision introduced liquidity requirements for the most part to reduce the possibility of bank insolvency caused by liquidity shocks. This research develops an agent-based model of a banking system to be used to analyze the impact of the liquidity requirements on the solvency position of banks. The model devises a banking system with 12 heterogeneous banks in which banks perform their traditional activities namely taking deposits and making loans. Banks can fulfill their liquidity needs by engaging in interbank lending, selling their securities, and using central bank lending assistance. The model aims to study the behavior of different banks in response to imposing liquidity requirements. This model is calibrated using the data of Iranian listed banks during 2018-2020. Liquidity requirements are measured using liquidity coverage ratio, and the solvency position of a bank is measured using the capital adequacy ratio. The results of the simulations demonstrate that as liquidity requirements increase, the solvency position of some banks improves, some banks deteriorate, and some remain unchanged. Regarding this reaction among other various factors, profitability, inflow, and outflow of liquidity, and finally, the outflow rate parameter play an essential role.

According to the Basel III regulatory framework, uniform minimum liquidity requirements have been imposed on all types of banks. Using an agent-based model of a banking system, we investigate the effects of liquidity requirements on banks' insolvency under two policy experiments in one of which the minimum liquidity requirements are applied uniformly and in the other differentially across banks. The model introduces a banking system with 12 heterogeneous banks that must also comply with two liquidity requirements while performing their daily activities of taking deposits and making loans. The model is applied to the Iranian banking system. Results illustrate that because banks respond differently to liquidity requirements, applying one size minimum liquidity requirements to all kinds of banks, strengthens the likelihood of a liquidity shock turns into banks' insolvency and could increase banking system instability. Thus our findings highlight that to achieve financial stability at the national level, policymakers should revise the current one size fits all approach when designing liquidity requirements.

This paper addresses the link between payment systems development and Economic growth and also investigates the role of technological growth and especially digital adoption status in this relationship. In this order, we use cross country data to compare Iranian payment systems situation with other countries in different aspects. Data analysis shows that Iran's payment systems are weaker in some respects, such as the dominant role of the old settlement systems in large-value payments, the integration of sales terminals, and the small share of new payment instruments (such as the Internet and mobile banking) in transactions. We apply this panel database to estimate the effect of infrastructure development in payment systems on economic growth.  Based on the estimation results, robust evidence confirms the positive impact of payment system development on economic growth. Although the role of digital adoption in this relationship induces the payment system development positively affects real sector activities only in countries with high or medium digital adoption. Moreover, the impact is stronger in the group of countries categorized at a high level of digital adoption.

Reducing the moral hazard of contracts due to more symmetrical information between the borrower and the bank and reducing the efficiency of the customer validation process, respectively, are the most important advantage and disadvantage of an increase in credit institutions investment. In this regard, we examine the relationship between the bank health and bank investment in different aspects. The data include Iranian banks financial data during the period of 2006- 2015. Since policy maker set a threshold for banks investment ratios, we use non-linear estimation methods to examine this relationship. Also, we control influential factors impact such as the bank's size, the role of the government in banks ownership structure, the nature of the activities of shareholders and related companies, the ownership concentration. The results show that bank investment has a positive effect on profitability, asset quality, liquidity and capital adequacy of the bank. However, it affects negatively on the role of the bank as a financial intermediation, In other words, three goals of 1) financial repression, 2) regulating the non-financial activities of the banks, and 3) which can maximize soundness of the banking system, are impossible trinity in the Iranian economy.

In this paper, we analyze the effects of oil revenue shocks on different sectors of Iranian economy using the quarterly data of the Iranian economy and a time varying parameter VAR model. Oil revenue is modeled to be an exogenous variable and the parameters are estimated in Bayesian context using Gibbs sampling. The results show that the value-added of industry and mining, and services increase after a positive oil revenue shock. The response of agriculture, and oil and gas sectors is not significant. The results also show that after the war, the response of industry and mining, and oil and gas sectors are higher, relatively.

Identification of business cycles is important because formulating a proper economic policy depends crucially on correct identification of the economic conditions. Einian and Barakchian (1393 Hijri-Shamsi) identified business cycles in the Iranian economy based on a univariate statistical method. In this paper، the sensitivity of the results to the usage of the statistical methods of Hodrick and Presscott (1997)، Baxter and King (1999)، Christiano and Fitzgerald (2003)، Beveridge and Nelson (1981) and Stock and Watson (1988) is analyzed. The results show that identifying business cycles based on NBER definition of expansion and recession is not sensitive to the statistical method used، while the size of the output gap does critically depends on it.

The goal in this research is to obtain some stylize facts about the nature of monetary and fiscal policies in Iranian economy. Different economic and political factors are selected as the potential explanatory variables in monetary base (MB) and government expenditure (GE) regressions and the posterior inclusion probabilities are calculated using a Bayesian model averaging approach. The results show that oil revenue is dominant in GE variations, GE and MB are not sensitive to output variations of different sectors, GE is not sensitive to a change in inflation, MB increases when inflation rises and MB growth rate is higher in average during 2005-2010 period. Therefore, it seems that the policy makers respond to a change in output and prices are not consistent with the macroeconomics recommendations and the economic attitude of the president is more effective.

In this study, the relationship between uncertainty and non-oil GDP is analyzed. A Threshold Autoregressive (TAR) model with exogenous variables is estimated by using Iranian Economy's data of 1988:2-2008:3. The uncertainty index is calculated by using ARCH modeling and Factor Analysis. The results show that there is a non-linear relationship between non-oil output and uncertainty in Iran. As the uncertainty passes the estimated threshold level, it reduces non-oil GDP, especially the output in services sector.

Assuming that in long-run, money is the main determinant of price level, we can state that long-run is a period in which money and prices are highly correlated. Given this assumption, we have calculated the length of the long-run periods of different economies by using Christiano-Fitzherald (2003) Filter. The results show that we cannot consider the same period as long-run for different economies. We have calculated the length of this period for different economies. These periods are longer for economies facing more supply shocks.

Uncertainty results from economic and noneconomic government policies. This paper tries to identify the benefits or the costs of these uncertainties by analyzing the relationship between Inflation uncertainty and Investment in Iran in quarterly period of 1368:1-1387:2 (1989:1-2008:2). The results confirm a negative relationship between inflation uncertainty and investment.

By applying conditional variance modeling and Granger causality test, we specified some important determinants of real and nominal uncertainty in Iran (1960-2006). In addition we investigated the negative effects of these uncertainties on output growth. Our results show that an increase in the rate of inflation and a decrease in the level of oil revenues can raise nominal uncertainty, while real uncertainty is arisen by oil revenues. This study could not reject the hypothesis that nominal and real uncertainty has no effect on output growth.