تیمور محمدی

Objective: The tourism sector, a significant component of the global service industry, is vital for a nation’s economic development and income generation. Therefore, it is essential to ascertain the factors influencing the demand for this type of service. The main aim of this study is to examine the impact of exchange rates on tourism demand in Iran. Methods: This study examined the long-term and short-term relationships between Iran’s tourism arrivals and variables such as the exchange rate, political risk, and other factors influencing tourism demand, including inflation, foreign investment, oil revenue, GDP, and population, employing time series data from 1995 to 2024 and the Dynamic ARDL simulation method. Results: The results indicate that political risk significantly detrimentally affects Iran’s tourism demand, whereas the exchange rate has a notable positive influence. Since 2010, when the fourth round of sanctions began, political risk has escalated owing to internal and external conflicts, negatively impacting international tourism demand. Conclusions: The interplay between exchange rate variables and political risk was found to positively influence tourism demand. The increase in the exchange rate decreases the costs of tourism services, and also stimulates creative business endeavors in the tourism industry.

Portfolio optimization has always been the main concern of investors. What differentiates different optimization models from each other is the risk measure. The main contribution of this paper is to provide a portfolio optimization model that considers systemic risk so that it can help investors make optimal investment decisions as a general model. For this purpose, two models are presented. In the first model, systemic and systematic risk were considered simultaneously, and in the second model, only systemic risk was considered. In the two mentioned models, delta conditional value at risk (∆CoVaR) and the Markowitz model are used respectively to measure systemic risk and a benchmark model. Also, the criteria used to compare the performance of the reviewed models include the ratio of reward-to-risk, along with the Sortino ratio and the Omega ratio. The problem of optimization and examination of the results was carried out on a selected sample, 38 companies listed in the Tehran Stock Exchange (TSE) from 2013 to 2023. The results of empirical analysis of out-of-sample data (during a period of 1198 days) show that based on all three mentioned criteria, the first proposed model shows the best performance among the three models. In addition, the performance of the second model is ranked second. In short, it can be said that considering systemic risk in portfolio optimization leads to better performance than the Markowitz model.

This research aims to rank the non-breakable factors influencing currency crises in Iran. It analyzes the influential variables using Bayesian averaging methods and seasonal data from 2001 to 2021. Unlike classical methods, Bayesian analysis incorporates uncertainty and prior information in model and parameter selection, providing a more accurate assessment of the significance and impact of each variable on currency crises. By integrating uncertainty into the model, Bayesian techniques enhance the precision of determining key influential variables. The findings indicate that among all the identified influencing variables, nine key variables play a significant role in exchange rate fluctuations. These variables include the export-to-output ratio, exchange rate gap from equilibrium rate, inflation, and the uncertainty index, which are crucial in determining exchange rate movements. Additionally, imbalanced liquidity growth and rising inflation exacerbate pressure on the exchange rate, increasing the likelihood of currency crises. Excessive liquidity expansion, coupled with inflationary pressures, leads to financial instability. This research not only identifies the primary determinants of currency crises but also underscores the necessity for economic policy reforms. The results from the Bayesian model show that variables consistently maintaining their impact despite other factors are recognized as "non-breakable." These non-breakable factors should be prioritized in economic policymaking to ensure financial stability. By considering these critical variables in policy decisions, economic authorities can adopt more effective strategies to prevent currency crises and mitigate adverse economic effects.

This study aims at investigating the effects of the change in the information environment from risk to ambiguity, disclosure dynamics, and the ambiguity aversion behavior of investors on the disclosure of integrated information for selected corporates operating in digital industry listed on the Tehran Stock Exchange during 2012-2022. The corporate voluntary disclosure lag, stock return volatility, and the probability of favorable returns incidence represent respectively the disclosure dynamics, firm risk, and investors' ambiguity aversion, within dynamic panel models. To investigate robustness of the estimates we have also included capital market ambiguity, firm size and stock liquidity indices in the model. The findings are consistent with existing theories. Firm risk and the capital market ambiguity have an increasing impact on the integrated information voluntary disclosure. However, the ambiguity at firm's level has decreased its voluntary disclosure. Likewise, a rise in ambiguity aversion (in-dependent of the ambiguity level), which makes investors more pessimistic about the firm's cash flows, makes the manager to increase the level of voluntary disclosure. Further, inertia in voluntary disclosure has been detected among the studied digital corporates. Depending on the magnitudes of the risk, ambiguity, ambiguity aversion, and the source of investors' ambiguity, the corporate managers decide on their voluntary disclosure policy appropriately.