نشریه مدلسازی اقتصاد سنجی
سال نهم شماره 1 (پیاپی 33، بهار 1403)

One of the most destructive fluctuations in the stock market is the crash in stock prices. The accurate calculation of the probability of the stock price crash can greatly help investors choose the right investment portfolio. In this article, the one-dimensional convolutional neural network technique is used to calculate the probability of the stock price crash, and the three-factor Fama and French model and Capital asset pricing model are used to calculate stock returns.The sample used in this paper includes 80 import- and export-oriented exchange companies in the period 2008-2021 According to the obtained results, convolutional neural networks predict the probability of the stock crash with high accuracy, and there is an inverse relationship between the probability of the stock price crash and its expected return, which means that by calculating the probability of a stock price crash, it is possible to predict the future demand for it and the direction of its future price. Calculating the probability of stock price crashes, using convolutional neural networks is a new method for calculating the risk of stock price crash and can help us measure portfolio risk.

In this study, using monthly data from 5/2005 to 5/2023 and the QARDL approach, the asymmetric effects of geopolitical risks on the price of heavy crude oil in Iran emphasizing the two characteristics of non-linearity and asymmetry in different quantiles are investigated. The findings show that the long-term relationship between geopolitical risks and the price of Iranian crude oil is significant and asymmetric in different quantiles. The effects of geopolitical risks on crude oil prices are negative when the crude oil market is decreasing and positive when it is increasing. The negative effects can be attributed to the existence of fear and panic among the investors of the global crude oil market, as well as the reduction of economic activities due to the existing risks and as a result, the reduction of crude oil demand. The positive effects can also be attributed to the concern about the future supply of crude oil and the disruption in supply and the increase in precautionary demand and finally the price increase.

Although there are many studies on the drivers of FDI inflows, very little attention has been paid to the impact of population health as a specific form of human capital that affects labor productivity. Therefore, according to this necessity, the aim of the current research is to analyze the sensitivity of health indicators on the flow of foreign direct investment for the top 47 science-producing countries that were selected based on the report of the SC imago-Institutions Rankings system. In this regard, the research model was estimated based on the dynamic panel data approach for the period from 2011 to 2020 and the obtained results showed that life expectancy at birth and death rate as two indicators showing the health of the population have significant positive and negative effects on are foreign direct investment inflows; So that with the increase in life expectancy at birth and the decrease in the death rate, the inflow of foreign direct investment increases and vice versa; Therefore, it is concluded that foreign direct investment has a strong and non-fragile sensitivity to health components.

The current study aims to test the relationship between inflation rate and exchange rate in Iran as two key indicators of economic performance. For this purpose, the monthly data of the period 1370:1-1402:4 and the quantile-by-quantile regression method have been used. The results show that there is a positive, symmetrical and non-linear relationship between the two variables, and the effect of the increase in the exchange rate on inflation is more severe than the effect of the increase in the inflation rate on the exchange rate. An increase in inflation causes an increase in the exchange rate, but this issue is not the same in different quantiles of the exchange rate. In the low quantiles of the exchange rate (quantile 0 to 4/0), the effect of inflation on the exchange rate is greater than in the median quantile (5/0). In this way, in the low quantiles, its coefficient is around 8/0 and in the middle quantile, it is less than 4/0. Also, in the quantiles above the middle, the effect of inflation on the exchange rate increases again, and its greatest effect is in the high quantiles of the exchange rate, where this coefficient reaches more than 2/1. Also, the effect of increasing the high quantiles of the exchange rate (4/0 to 95/0) on the low quantiles of the inflation rate (0 to 2/0) has been more intense than its effects on the high quantiles of the inflation rate.

This study aims to investigate the impact of trade and investment risks on the real effective exchange rate from the first quarter of 2014 to the fourth quarter of 2021 using the Structural Vector Autoregression (SVAR) method. Gross Domestic Product (GDP) per capita, inflation rate, and Globalization Index are controlled variables. Results indicate that trade and investment risks have a significant positive effect on the real effective exchange rate throughout the study period, leading to an increase in Iran's real effective exchange rate in the long run. Additionally, the variance decomposition reveals that 23.37% of the variation in the real effective exchange rate in the tenth quarter is attributable to trade and investment risks. Moreover, inflation rate, GDP per capita, and globalization also positively influence Iran's real effective exchange rate.

Over the past four decades, increasing forecast emphasis has come to be placed on the potential importance of different sectors and their contribution to the sustainable growth of the Nigerian economy. A more common strand of research is existing studies that consider the resource curse hypothesis to show empirically, the actual relationship between natural resources and economic growth. However, these studies have looked at various resource endowment especially oil and metals, with just a few focusing on the non-oil export sector. A preliminary graphical study of the contribution of the non-oil sector to Nigeria’s GDP shows that over time, the linkage of non-oil export to the economic growth has been low (less than 20%). Hence, this study will analyze the long-run relationship and also the importance of both oil and non-oil exportation and their impact to sustainable economic growth. The model introduced in this article uses the time series data for non-oil export, oil export, alongside other macroeconomic variables on gross domestic product (GDP). In order to adequately investigate the long-run relationship, statistical forecast was adopted to project the data for future years and thereafter econometric method was adopted to validate the projected data and analyze the direction of causality between the sectors (oil and non-oil) export and economic growth. The result reveals the possible short and long run interaction amidst sectors’ export and economic growth, based on the different tests conducted, in order to recommend policies aimed at boosting the level and significance of the sectors’ export.