Journal of Money and Economy

In this study, we make an effort to look at the Resilience of Islamic banks across Pakistan along with how particular banking variables influence their resilience. The data set used for this study is made up of Pakistani Islamic banks from 2015 to 2021. Employing the VOLARE index, it is apparent that Meezan Bank has higher resilience compared to other Islamic banks. Regression approaches were additionally used to assess the association between banking-specific variables and bank resilience. Given that the bulk of Islamic banks have witnessed gains in recent years, the bank size showed a positive and substantial relationship in the estimation. Additionally, as prudential laws have been strengthened by the state bank of Pakistan, the capital adequacy ratio and leverage ratio also found significant and beneficial connections in the model's results. Nevertheless, the regression analysis demonstrated an adverse relationship between the leverage ratio, the non-performing loan ratio, and inflation. This study offers information that will help practitioners, scholars, and researchers to strengthen the economic and financial sources regarding the resilience of Islamic institutions.

This study examines the effects of exchange rate shocks on poor and rich households and inflation inequality in Iran over the period 2003m4-2022m4. Based on the econometric results, a one percentage point increase (depreciation) of the exchange rate (Iranian Rial versus US dollar) is expected to lead to a rise in food price inflation of about 0.1 in the short and 0.8 percentage points in the long run. The effect on non-food price inflation tends to be slightly lower, both in the short and the long run. As the share of food in the consumer baskets is higher for poor than for the rich households, depreciations of the exchange rate are more harmful at the lower end of the income scale. Likewise, poor households will benefit more, if the Rial appreciates against the US dollar.

In Iran, especially in the aftermath of economic sanctions of the 2010s, and the intensification of the government's budget deficit, since 2012, there was a diminishing investment, followed by a decrease in production which led to stagnation in the economy. The government plays its economic role by implementing fiscal policy. However, when it is associated with uncertainty, the effectiveness of fiscal policy may be undermined. Therefore, we need to measure the uncertainty of fiscal policy in Iran. We measured the uncertainty index of fiscal policy in Iran using a specified fiscal reaction function. In this function, there are two types of shocks. First, fiscal level shock and second, volatility shock which is a proxy for fiscal policy uncertainty. To this end, we employ particle-filter to estimate fiscal policy uncertainty in Iran. In this paper, by measuring the structural budget balance function for Iran, we utilize it as a dependent variable. The results indicate that the trend of balance or total budget deficit has a compliance with the trend of balance or structural budget deficit for Iran suggesting that the nature of government budget in Iran is a structural one, and in the 2010s the sanctions, adversely affected on the government budget. In 2012, the intensification of sanctions led to the increase in the uncertainty of fiscal policy until 2015 that the trend of fiscal policy uncertainty was mildly downward. In 2017, the uncertainty index of Iran's fiscal policy has increased rapidly until it reached its maximum in 2020, in which the economy did not face with such uncertainty for fiscal policy since 1979.

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.

In international trade and financial exchanges, global cryptocurrencies have a prominent and effective role. In this paper, using the KOF index, the structure of financial globalization is extracted and the effect of the expansion of global cryptocurrencies is examined in the two components: Foreign Direct Investment and Portfolio Investment. The Ordinary Least Squares (OLS) model has been used separately for 2020, 2021, and 2022 to analyze the results of the expansion of global cryptocurrencies in the foreign direct investment component. Also, the price changes of Bitcoin and Ethereum from March 10, 2016, to the end of December 2022 have been used to investigate the effect of global cryptocurrencies in portfolio investment by applying Modern Portfolio Theory (MPT). Also, according to the availability of data in research sources, the data of 111 countries have been used. OLS estimated results suggest that the adoption and expansion of global cryptocurrencies has no significant relationship with Foreign Direct Investment. Also, using MPT, the results of portfolio optimization suggest that global cryptocurrencies improve the effectiveness of the selected portfolios, and with the same corresponding returns, the risk of the portfolios including global cryptocurrencies decreases as well. Therefore, the results emphasize the role of global cryptocurrencies in financial globalization only as crypto-assets.

This paper aims to present a banking model based on Sustainability, Agility, and Resilience paradigms (SAR) employing a mixed research method. Achieving this goal, a meta-synthesis approach was used as a first phase of the study to extract codes, themes, and categories which finally lead to a conceptual model of SAR banking. A total of 114 codes were extracted by reviewing related papers. In the following, according to the similarities, interpretations, and expert opinions, 23 final themes were formed which were summed up into 9 categories including economic, social, governance, and environmental sustainability, process, strategic, service agility, structural resilience, and economic resilience. Accordingly, as the second phase of the study, the PLS-SEM (Partial least squares-structural equation modeling) method was employed for model validation. Results disclosed that the coefficient of determination for the SAR banking construct is about 0.95 which is formed by agile, sustainable, and resilient banking constructs. In addition, the other validity indices such as Good of Fitness (GOF), Average Extracted Variance (AVE), and Composite Reliability (CR) ensure the achieved model validity. Ultimately, findings depict that SAR banking which is mainly affected by sustainable banking can assist the bank to survive through today's turbulent business world.