درخت حوزه‌های تخصصی

This paper aims to investigate the portfolio optimization under various market risk conditions using copula dependence and extreme value approaches. According to the modern portfolio theory, diversifying investments in assets that are less correlated with one another allows investors to assume less risk. In many models, asset returns are assumed to follow a normal distribution. Consequently, the linear correlation coefficient explains the dependence between financial assets, and the Markowitz mean-variance optimization model is used to calculate efficient asset portfolios. In this regard, monthly data-driven information on the top 30 companies from 2011 to 2021 was the subject to consideration. In addition, extreme value theory was utilized to model the asset return distribution. Using Gumbel’s copula model, the dependence structure of returns has been analyzed. Distribution tails were modeled utilizing extreme value theory. If the weights of the investment portfolio are allocated according to Gumbel’s copula model, a risk of 2.8% should be considered to obtain a return of 3.2%, according to the obtained results.

Today, management requires a new approach in the areas of production planning and operations process with a cost management approach. Organizations and industrial units to lead their lives, by recognizing the impact points of the challenges ahead and positive impact, guide and lead them to advance the goals of the organization. Economic dispatching with particle swarm optimization algorithm approach is an approach in the field of industrial units. Dispatching tries to determine the share of production capacity in a way that optimizes the overall performance of the system economically and improve system performance, including: production and process planning, supply and demand balance, cost management, productivity growth, optimal allocation of resources according to the capacity of tanks, formulation of production and operational strategies, the impact on the strategic vision document. In this research, an attempt has been made to perform economic dispatching with the approach of particle swarm optimization algorithm with hypothetical information to measure the feasibility of implementation and its impact on the overall performance of the system and production process and operations.

The present study investigates the impact of macroeconomic and bank-specific variables on non-performing loans (NPLs). To avoid the identification problem, two models are employed to address this impact. The first one tests the effect of macroeconomic variables including the growth of oil revenues, inflation, and the growth of GDP without the oil sector on the growth of NPLs. Data is quarterly over the period 2004:3 to 2019:3. The transition variable in this setup is the growth of oil revenues and its threshold is 9 percent, which divides the sample into oil booms and oil recessions. According to the results, inflation has a significant positive effect on NPLs. During the oil boom, oil revenues decrease the NPLs. Due to the immense size of the government and its current and capital expenditures, when oil revenues are lower, the government forces banks to allocate loans to finance projects with long maturity. Furthermore, the present study used PSTR to test the impact of bank-specific variables consisting of interest rate spread, loan loss provision, loan to deposit ratio, and NPLs. To do so, monthly data of 10 banks is used over 2016:04 to 2020:12. The transition variable is the interest rate spread at 1 percent, which categorizes the banks into two groups of good and bad. Good banks collect deposits with a low-interest rate and allocate high-rate loans with less chance of default. So, interest spread is the most important prominent determinant of decreasing NPLs, while the loan to deposit ratio is dependent on the banks belonging to which group. For good banks, the loan to deposit ratio decreases the NPLs, while for bad banks, it worsens the growth of NPLs.