درخت حوزه‌های تخصصی

The present study investigated the effect of imposing international financial sanctions on international risk sharing in developing countries during 2011-2022 using the propensity score matching (PSM) approach. International risk sharing refers to the processes in which countries with different income prospects share the risk of income fluctuations with one another by conducting commercial and financial transactions internationally. Therefore, their income and consumption no longer depend solely on domestic production, but also depend on the production of other countries (countries that own their assets). Since, international capital flows are the main tools for international risk sharing. Imposing international financial sanctions on a country reduces international capital flows and therefore reduces international risk sharing in that country. As the results showed, the impact of financial sanctions on international risk sharing has been negative and significant because the measure of the international risk sharing index in countries that were not subject to sanctions (control group) was 0.5877 and in countries that were subject to sanctions (treatment group) was 0.2431. Therefore, imposing international financial sanctions has reduced risk sharing in developing countries.

We investigate the effects of the implementation of reverse factoring on the credit risk level of financially constrained suppliers within a supply chain. To simulate the desired supply chain finance environment, an agent-based framework is developed. Short-term bank financing and reverse factoring are available financial instruments for suppliers. The estimations regarding the default probability of agents are calculated using formulations of the KMV (Kealhofer Merton Vasicek) model. It incorporates market-based information and company-specific financial data to estimate the likelihood of default and potential losses based on the estimation of the market value and volatility of the firm’s asset and calculation of the distance to default. Results suggest that the adoption of reverse factoring significantly alleviates the credit risk levels of financially constrained members of a certain layer within a supply chain.

This study aimed to investigate the performance of diverse stock market indices on the Tehran Stock Exchange during two distinct phases: a boom from September 2022 to December 2022 and a recession from December 2022 to September 2022 in the foreign exchange market. The results of this study diverged from the anticipated outcomes and show that by using dynamic conditional beta and conditional Treynor ratio during the period of exchange rate boom, export-oriented companies such as metals and chemicals in the Tehran Stock Exchange did not perform better than non-export-oriented companies such as Insurance and medicine. While it is always believed that export-oriented firms outperform the others when the Rial depreciates.

Volatility and risk measurement are essential parameters in risk management programs that can affect economic activities and public confidence in the stock market. Also, these two are the keys in the studies that connect the stock market, economic growth, and other financial factors. In recent years, due to the instability in the Tehran Stock Exchange, controlling the adverse effects caused by the volatility of stock prices, predicting and modeling price dynamics, and measuring risk have become necessary for the participants in this market. In the present research, the class of hidden Markovian index models of conditional variance Heteroskedasticity (HM-GARCH) is used to predict the volatility of stock prices and accounts of the Tehran Stock Exchange. For a comprehensive review, the models are selected to include the characteristics of volatility clustering, asymmetry in volatility (leverage effect), and heavy tail of stock returns (with t-student distribution). Based on RMSE and AME criteria, the HM-EGARCH-Normal Exponential GARCH model with normal distribution is more effective than other models in predicting stock market volatility. Therefore, leverage is necessary to analyze stock market risks using hidden Markov models, but heavy tail distribution is unnecessary. The results indicate that the HM-EGARCH-Normal model appropriately assesses volatility and improves market transparency and risk management forecasts. Also, the VaR and CVaR market risk assessment post-tests using Kupiec and DQ tests do not show evidence of overestimation or underestimation.