درخت حوزه‌های تخصصی

This study employs a quantitative approach, utilizing the gravity model and FMOLS estimation, to examine how economic sanctions affect the trade relationship between Iran and Nigeria. Additionally, it explores the influence of factors such as GDP, exchange rate, strong sanctions, and weak sanctions. By doing so, this research contributes to the existing knowledge on bilateral trade between these two nations and provides valuable insights into areas that require attention for fostering trade development between them. The research findings reveal that in the bilateral trade relationship between Iran and Nigeria, there exists a positive correlation between GDP and weak sanctions (LIM) with trade. An increase of 1% in GDP leads to a 7.79% increase in trade, while a 1% increase in weak sanctions contributes to a 3.91% increase in trade. Conversely, strong sanctions and exchange rate have a negative impact on trade, with a 1% increment in strong sanctions resulting in a 1.18% decrease in trade, and a 1% increment in exchange rate leading to a 1.96% decrease in trade.

During the last three decades, financial sanctions have been imposed on Iran by the United States, the European Union, and the United Nations Security Council. This paper aims at estimating the effect of financial sanctions on the import of capital and intermediate goods in Iran, which was carried out for two independent time periods. The first period (2010-2013) includes multilateral financial sanctions, and the second period (2016-2019) includes multilateral sanctions and the withdrawal of the United States from the JCPOA. We examined the impact using the difference-in-difference (DID) method. The results of the first period indicate that the decrease in the imports of capital and intermediate goods in Iran depends more on the countries that "provided the sanctions plan" than the countries that did not provide the sanctions plan, because the coefficient of dummy variable for implementation in the random effects model is statistically significant. The negative effect of 0.007 on imports shows that the effect is weak, because this group of countries behaved differently. In the second period, the random effects model is statistically significant. In this model, the negetive effect of 0.22 on imports indicates a significant effect. Therefore, the reduction of Iran's imports in this period depends more on the countries that provided the sanctions plan than the countries that did not provide the plan.

As a trading strategy, pairs trading is performed based on the arbitrage opportunities extracted from statistical models. It is an outcome of the distance between an asset pair and the equilibrium state. Consequently, selecting a pair with the potential to form long-term relationships and reverting to the mean is the main challenge associated with pair trading. Cointegration is one of the most famous statistical tests for selecting a pair's trading. The present study uses Empirical Mode Decomposition (EMD) to decompose the time series of an asset pair price into its constituent elements (intrinsic mode functions). This study examined the property of cointegration across different levels and the corresponding levels of 2- time series to find the cointegration pairs in different decomposition levels and finally examine the resulting profitability. To this end, the profitability of the pairs trading system related to 14 stocks of the Tehran Stock Exchange throughout 2012-2021 was investigated based on EMD. The results showed that the outputs are pretty noticeable for the first level of decomposition (the first intrinsic mode function), and the number of trading opportunities increased by more than two times compared to the normal pair trading with cointegration; the daily returns increased by four times; and the Sharpe ratio increased by about two times compared to the normal pairs trading. The system formed based on the second mode function also outperformed the normal cointegration, and the performance of the third intrinsic mode function is almost on par with that of cointegration. Moreover, the mean transaction duration decreased remarkably in the first and second mode functions.

High transaction costs have been cited as limiting access to credit in developing countries. This issue is much more critical for knowledge-based companies due to their position in accelerating economic growth and the particular characteristics of these companies. Therefore, this research aims to evaluate the transaction cost of financing knowledge-based companies from an official fund in Iran that provides financial support for innovations and technologies. The data was collected through interviews and questionnaires in a sampling of knowledge-based companies in 2022. In this study, after calculating the transaction costs of financing, the effect of the factors affecting it has been investigated. The investigated sample was 123 companies from the fund's customers. The results showed that, on average, the ratio of transaction cost to the facility, transaction cost rate, total cost rate (transaction cost and interest rate), and the ratio of transaction cost to interest rate are equal to 3.33, 7.04, 15.66, and 0.81, respectively. Based on the results, the facility amount, the number of payment steps, the distance between the request for the facility and the contract, the interest rate, and dummy variables for the type of facility have a positive and significant effect on the transaction cost