درخت حوزه‌های تخصصی

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.

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of them has some limitations. Therefore, these methods are not recommended in the case of complications in financial modeling since they require considering a series of assumptions, such as symmetric distributions in return on assets. Because the stock exchange data in the present study are skewed, asymmetric distributions along with symmetric distributions have been used for estimating VaR in this study. In this paper, the performance of fifteen VaR models with a compound of three conditional volatility characteristics including GARCH, APARCH and GJR and five distributional assumptions (normal, Student’s t, skewed Student’s t and two different Lévy distributions, include normal-inverse Gaussian (NIG) and generalized hyperbolic (GHyp)) for return innovations are investigated in the chemical, base metals, automobile, and cement industries. To do so, daily data from of Tehran Stock Exchange are used from 2013 to 2020. The results show that the GJR model with NIG distribution is more accurate than other models. According to the industry index loss function, the highest and lowest risks are related to the automotive and cement industries.

Generally, international flows of capital and foreign direct investment attraction are challengeable issues in the literature of economic growth and development in emerging market countries. However, the fluctuations in foreign direct investment, including sudden flood and stop, will affect emerging markets' output and macroeconomic variables. Using an econometric model with unbalanced panel data during 1990-2014 for 38 emerging countries, this study tries to evaluate the determinants of output losses from the sudden stop of foreign direct investment and consider the role of macroeconomic policies. The results show that the sudden stop phenomena and the financial crises have been identified as the main explanatory variables for the output collapse in the selected countries. Moreover, the role of macroeconomic policies is important, and the output losses can be controlled by using active monetary and exchange rate policies.

Economic growth is the most common goal in any economy, and capital is one of the most important determinants of growth. In the last few decades, the use of securities in various countries' capital markets has expanded and has become an essential part of the economic system supplying the capital need for investors and other institutions. This study aims to analyze the effect of securities used to finance banks (securitization) on economic growth. For this purpose, the theoretical analysis method is used in the framework of a Dynamic Stochastic General Equilibrium (DSGE) model. The theoretical model used is based on Frank Ramsey's (1928) economic growth model. To transform this model into a suitable model for research, the shadow banking system and securitization have been added. The model is then simulated using the calibration method and using the real data of the US economy; then, the macroeconomic changes and fluctuations created by bank securities are explained and analyzed. According to the research findings, issuing securities by banks will lead to slower economic growth. Therefore, it is recommended to avoid the use of securitization in banking.

Stock Exchange Investors have paid more attention to the banking group in recent years so that in many cases, the direction of the banking index has changed the general direction of the market. Therefore, exploring the banking index fluctuation is important from the point of view of investors as well as the direction of the market. The purpose is to examine the effectiveness and direction of news, as one of the most important factors in the formation of volatility, on the banking group index in Tehran Stock Exchange by using 1460 daily records during 2018-2019 and the GARCH family's method. The data were collected for four different newsgroups, including economic, political, psychological, and financial. After that, the news that had more relationship with the banking group was separated to underscore in the weighting stage and divided into the positive and negative news, according to the perspectives of capital market experts and economic elites. The results indicated that newsgroups have a significant effect on the group index, and only the negative political model on the banking group has an asymmetric effect. The results indicated that political and economic news has a positive and significant impact on banking index fluctuations.