Monetary & Financial Economics

Abstract Nowadays, supply chain management has become more important due to the globalization of business markets. As complexity increases, the level of uncertainty and risk in the chain also increases. Therefore, supply chain risk management is one of the issues that has received attention from organizations. Digital transformation in the supply chain is associated with benefits such as improved efficiency, increased transparency, and accelerated processes, but at the same time it also brings with it numerous risks such as cyber threats, technology dependency, and organizational resilience. This research presents an empirical model for assessing the risks of digital transformation in a sustainable supply chain using backpropagation neural network (BPNN). Research data is collected from 120 companies active in manufacturing and logistics industries, and the model is evaluated with RMSE, MAE, and classification accuracy criteria. The results show that the proposed model is able to predict the level of digital risk with 93% accuracy. Sensitivity analysis also shows that cybersecurity has the greatest impact (coefficient 0.45) on the overall risk.

Abstract This paper examines central bank interventions in the context of government debt sustainability, focusing on the inflation targeting model. Using quarterly data from 1989 to 2023 for Iran and countries with inflation targeting policies, this study examined the impact of central bank interventions on government debt sustainability in the context of inflation targeting using the Markov regime shift and policy evaluation (PSM) approach. The results of the Markov model showed that monetary interventions, such as increasing liquidity, reduce debt sustainability (debt-to-GDP ratio) and weaken the relationship between government spending and revenues. Gross domestic product had a positive effect (0.61 in the high-volatility regime and 0.98 in the low-volatility regime), and central bank independence (0.96 and 0.54), exchange rate (0.48 and 0.54), oil revenues (0.43 and 0.66), and tax revenues (0.78 and 0.67) had a negative effect on debt. Liquidity (0.53 and 0.65) and inflation (0.67 and 0.54) had positive effects, with a stronger effect in the low-volatility regime. The transition probability matrix also showed that the high-volatility regime (persistence probability 0.98) was more stable than the low-volatility regime (0.91), with an average of 3 quarters of high volatility and 1 quarter of low volatility. The PSM method with the logistic model confirmed that variables such as GDP, inflation, and central bank independence affect the probability of intervention, but interventions reduce debt sustainability.

Abstract This study investigates the impact of public debt and foreign direct investment (FDI) on the economic growth of four selected Islamic countries (Iran, Turkey, Malaysia, Egypt) during the period 1960-2023. To achieve the research objectives, the panel autoregressive distributed lag (Panel ARDL) econometric method was employed to simultaneously analyze the short-term and long-term relationships among the variables. In the empirical model, in addition to the key research variables (public debt and FDI), the role of control variables including national savings, inflation rate, real exchange rate, human capital, political stability, and the degree of trade openness was also considered. Findings from the model estimation indicate that in the long run, both public debt and inward FDI have a positive and significant effect on the economic growth of the studied countries. Furthermore, the results show a positive and significant effect of inflation and domestic savings, and a negative effect of the degree of trade openness on economic growth. In the short term, the error correction coefficient was negative and significant, indicating the stability of the model and its convergence towards long-term equilibrium. Based on these findings, efficient and productive management of public debt, improving the business environment and macroeconomic stability to effectively attract foreign investment, along with policies supporting national savings and managed trade openness, are presented as the main policy recommendations of this research.

Abstract The purpose of this study is to investigate the effect of uncertainty of the government's macroeconomic policies on the speed of adjustment of trade credit . Considering that financing through commercial credit is of great interest to managers of companies due to the high cost of financing through debt, therefore, achieving commercial credit as soon as possible is of great importance. The statistical population of the study is Tehran Stock Exchange and the study period has been since 2011 until 2023‌. By applying the conditions of systematic exclusion, 136 companies have been included in the final screening from the statistical population as the final sample‌. In the present study, the generalized moment approach‌ has been used to test the research hypotheses. Three factors of changes in the inflation rate, exchange rate changes, and changes in the country's economic growth rate were used to identify the uncertainty of macroeconomic policies.
The statistical results showed that the rate of adjustment of trade credit in the sample used in the study is about 75%, which in fact shows the high willingness of companies to use credit financing, and companies actively reduce 75% of the gap between the target and real business credit annually.
Also, changes in the exchange rate, changes in the inflation rate, and changes in the country's economic growth rate affect the rate of adjustment of trade credit. It has positive and meaningful. In fact, with the increase in uncertainty in economic policies, the speed of adjustment of trade credit towards optimal commercial credit will increase, and in this situation, managers will move towards credit financing.

Abstract Exchange rate volatility through instability in the economy causes uncertainty in the economy at the macro level and decision-making and planning of economic factors face a crisis. Also, the exchange rate is affected by several factors and the requirement for managing exchange rate volatility is to identify the factors that cause it. Given the special conditions of the Iranian economy and the intensification of economic sanctions in the last decade and the dependence of the government budget on oil revenues, it is important to examine the impact of oil revenues and the growth of the budget deficit on the exchange rate volatility under severe sanctions. The main purpose of this study is to investigate the interactive effects of oil revenues fluctuations and severe economic sanction on exchange rate volatility using quarterly data in the period (1996-2021) and the combined method of Autoregressive Distributed Lag and Exponential Generalized Autoregressive Conditional heteroskedasticity (ARDL-EGARCH) in Iran. The results show that oil revenues fluctuations and budget deficit growth have positive impacts on exchange rate volatility. Also severe economic sanctions have indirectly increased exchange rate volatility by affecting fluctuations of oil revenues. Economic growth has reduced exchange rate volatility.