moslem nilchi

Various methods have been developed to measure the persistence of inflation, which often focus on one of its two main components, i.e. the speed of convergence and the exogenous or endogenous level of the long-term equilibrium of inflation with respect to shocks. In this paper, both persistence components have been carefully evaluated. In order to measure the speed of convergence in both the first and second order moments of monthly inflation, exponential and hyperbolic speed models have been used. Also, the effect of structural break on the equilibrium level and changes in the inflation persistence coefficient have been investigated. The findings show that the occurrence of structural break in May 2017 has increased the long-term equilibrium level of monthly inflation by 1.3%. The chronicle of the time of the structural break shows its compatibility with the economic sanctions caused by unilateral withdrawal of the United States from the JCPOA and shows the role of political uncertainties in creating inflationary uncertainty. Also, the inflation persistence has increased in both the first and second order conditional moments, and the inflation persistence coefficient has increased by 8% after structural break. In such a situation, potential inflationary shocks, such as an increase in the exchange rate, spill over into expected inflation and actual inflation. As a result, the Dealing with inflation requires active and aggressive monetary policy in order to reduce the de-anchoring of inflationary expectations. Therefore, overcoming the inflation uncertainty and increasing the coefficient of inflation persistence, in addition to appropriate monetary policy, requires reducing the role of environmental variables such as severe political tensions in the country.

The long-term behavior of stock markets is of considerable importance to asset managers and financial experts due to its direct relationship with stock price valuation. Volatility persistence has a significant effect on stock price returns. Therefore, the relationship between heavy falls in the stock market can be related to the phenomenon of high volatility persistence. In the present study, using the Bayesian unit root test, the persistence of the Tehran stock Exchange volatility has been investigated in the framework of SV and SVJ. The results of this test using the Bayesian factor in different specifications of both SV and SVJ models show that although the unit root is rejected in the volatility of the Tehran stock Exchange prices in the period of 1398-1400 (2019-2021), the persistence of the volatility was very high. The increase of irrational traders in this period of time has been one of the reasons for increasing the persistence of market volatility. The findings show that the flow of information in the Tehran stock market and the absorption of information in its prices are slow. The characteristic of high volatility persistence in this market is the result of its closed structure and the concentration of market weight on a few main groups. As a result, regular release of financial statements, training of traders, and use of expert analysis along with diversification of investors and not focusing on specific pledges in portfolio formation will help to reduce volatility persistence.

The relation between inflation and inflation uncertainty is one of the most important empirical relationships in macroeconomics. Some views suggest that the rise in inflation creates real costs through the impact on inflation uncertainty. Another point of view considers the increase in inflationary uncertainty as a priority to the increase in inflation. Becoming the causal direction of this relationship will determine the optimal strategy of central banks in the implementation of monetary policies. In this paper, first, using the MS model with three regimes, future inflation forecast errors are calculated, and then, using the BIP-GARCH method, the conditional variance of inflation is extracted as a proxy of inflation uncertainty. Finally, by using Granger causality test, various hypotheses on the relationship between inflation and inflation uncertainty are investigated, and with the nonparametric regression method, this relationship is inflated in different intervals. The findings indicate that inflation uncertainty increases during periods of political turmoil and is strongly influenced by currency fluctuations. The refinement of outlier forecast errors caused by large and temporary shocks will be the choice of the IGARCH(1,1) model, which means that the effect of shocks is unlimited for inflation uncertainty over time. Also, the non-parametric estimation between inflation and inflation uncertainty shows the dependence of this relationship in which it is placed. In addition, the results show that inflation uncertainty affects future inflation in a non-linear way. As a result, the central bank can control the inflation rate by reducing the uncertainty of monetary policy by correcting inflationary expectations.

Crude oil is the main source of energy and accounts for about a third of world energy production. Turmoil in this market will have far-reaching economic and financial consequences. Because of this, investors attach great importance to predicting volatility when investing in crude oil markets to hedge risk and portfolio diversification. However, their investment strategies are often strongly influenced by volatility because, in different periods of crude oil markets, there are high and low fluctuations that are attributed to the movement of economic cycles. Accordingly, the present study compares the Markov Regime Switching (MRS) and Hidden Markov (HM) volatility models with the GJR-GARCH asymmetric model on their forecasting capabilities in the WTI and Brent crude oil markets. Empirical results show that the MRS-GJRGARCH model performs better than the HM_GJRGARCH model in predicting volatility in both markets. Accordingly, using the two criteria of value at risk and the expected deficit, the minimum loss and the expected loss for December 2021 were predicted. The results show that the expected shortfall from investing in the WTI market is greater than the Brent oil market

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.

It seems that heavy economic sanctions have been the source of the instability of the unofficial foreign exchange market in the last two decades. This instability can be the result of the high resistance of turbulent shocks in the direction of damping towards the long-term average of the exchange rate, which shows the high persistence of the process. Such stability in unofficial foreign exchange market fluctuations can be caused by large changes in long-term variance due to structural breaks or the existence of long memory in exchange rate returns volatility. this paper seeks to provide a perspective of these two modes and their different aspects on the volatility of the unofficial exchange rate. For this purpose, the persistence of unofficial exchange rate volatility in three cases; We examined original data, original data with structural breaks and refined data from mass jumps and with structural break in combination with exponential (GARCH and IGARCH) and hyperbolic (FIGARCH models) autocorrelation functions. The results of this research show that the unofficial foreign exchange market is affected by collective jumps and sudden changes in the variance of returns. Also, based on the information criteria, the model compatible with the data is the FIGARCH(1,d,1) model with the original data and exposed to double structural failures in the variance, which indicates the extreme instability of the unofficial foreign exchange market and the impact of structural failures caused by sanctions mainly It refers to long-term volatility or unconditional volatility. This model clearly shows the unilateral withdrawal of the United States from the JCPOA and the return of unilateral sanctions after 2018. The uncertainty in Iran's unofficial foreign exchange market is more severe than in 2011.

The daily observations of the total index of the Tehran Stock Exchange show that in the last few years, stock prices have been very volatile. This volatility can harm the economic environment of Iran. Modeling and predicting price volatility in this market can provide important information about uncertainty and risk to actors and thus help with managing possible unwanted changes in the field of financial investments. This, along with the rise in the value of the stock market in recent years, has caused the investigation of the issue of volatility to become increasingly popular among academicians and financial policymakers. Volatility and risk measurements are essential parameters in risk management programs and can affect a country’s economic activity and public confidence. These are also key parameters in studies that examine the relationship between the stock market, economic growth, and other financial variables. Tehran Stock Exchange markets have been volatile in recent years. Controlling the negative effects caused by stock price volatility has made it necessary to predict and model price dynamics for participants in this market. In this paper, the class of Parameter-Driven volatility models (stochastic volatility models) is used to predict the price volatility and calculate the risk of the price index in the Tehran stock market. Therefore, four stochastic volatility models were used. To make a comprehensive review, the asymmetry in the volatility (leverage effect) and the heavy tail of the stock price return distribution (with t- student distribution and Skew normal) have been included in the models. To estimate the models, the Gibbs sampling method was used, and to accurately compare the models, the test based on the posterior distribution of the models and the Bayesian factor was used. The results indicated that the canonical stochastic volatility model with Skew normal distribution (SNSV) is more effective than other stochastic volatility models in predicting the price of stock market volatility based on the Bayesian factor. Therefore, to analyze stock market risks using stochastic volatility models, there is no need to include the leverage effect in the state space of the Volatility equation. The SNSV model makes it possible to observe volatility and make predictions related to it, thereby improving market transparency and ultimately making diversification and risk management easier to implement. Also, the backtests of VaR and CVaR market risk assessment using Kupiec and DQ tests do not show evidence that the estimation is over or under the risk limit. As a result, the calculation of volatility and pricing with this model will lead to more precision risk management for professionals, especially fund managers who intend to include Tehran Stock Exchange stocks for asset allocation.

One of the important factors affecting the participation of investors in the stock market is the existence of information about the trends and price volatility of this market. In the past years, the economic sanctions and the Covid-19 epidemic have affected the Iran stock market. The continuous decrease of the stock market index is one of the consequences of volatility persistence waves caused by these events. Some financial theories have shown that the decline in stock prices can be caused by the existence of a unit root in the volatility of the market's price returns. In this research, the hypothesis of the drop in stock prices due to the presence of a unit root in the volatility was investigated with the data of the Tehran Stock Exchange index between 2016.September.21 to 2022.March.19 and using the stochastic volatility model introduced by Su and Lee (1999). The findings of this paper indicate that the posterior estimate of the coefficient of ϕ in the Stochastic volatility model is equal to one, therefore, it is not possible to reject the inappropriate performance of the market and the hypothesis of the fall of the Tehran Stock Exchange index as a result of the volatility persistence.

One of the major concerns of researchers and policy makers in recent years is the nature and magnitude of the correlation between financial markets and the oil market. The focus is on the role that both markets play in developing countries. The relationship between oil prices and the stock market has been studied in many studies. The methods used to study this issue have been used in the GARCH approach. In this study, using the BMSV model of the multivariate econometric Volatility model introduced by Harvey and others (1994), a survey of the spillover of energy market Volatility (OPEC oil yield) in the Iranian stock market was presented. In the results, BMSV for stock returns-oil returns showed a positive Volatility spillover (0.838) in these two markets. The results of this survey are of great importance to investors in the stock market. In particular, it was found that the amount of Volatility spillover is highly correlated with oil yield. Therefore, the energy market news is a key factor in making decisions on investing in the stock market. Because the use of this information minimizes the risk of investing in the stock market.

The aim of this study is to investigate the effect of Investor Sentiment on audit quality in companies listed on Tehran Stock Exchange. In this study, to review the audit quality criteria, five criteria have been used, including qualified audit reports, auditor fees and Auditor specialist, audit tenure and accrual quality. For this purpose, hypotheses were developed and data were analyzed for Companies listed on Tehran Stock Exchange during 2009 to 2018. The statistical population of the study, according to the circumstances considered for sample selection, is for the variable of auditor's modified report, audit tenure and Auditor specialist, including 169 companies, for the variable of accrual quality, including 164, and for the variable of auditor fees, including 61 Selected by Systematic removal method. The regression models using Panel data method with fixed effects approach and Logistic regression method reviewed and tested. The results of this study showed that the Investor Sentiment has a significant positive impact on the auditor's tenure and Discretionary accruals as lowering criteria for audit quality. Also, the results indicate that the Investor Sentiment had a significant negative impact on the qualified audit reports as increasing audit quality criteria. In addition, the results indicate that the Investor Sentiment does not have a significant impact on auditor fees and Auditor specialist.

This study investigates the dynamic relationship among diversification, capital structure and profitability of firms listed in Tehran Stock Exchange. The research sample selected using systematic elimination method, consists of 140 firms listed in TSE from 2011 to 2017. The hypotheses are analyzed using the panel VAR approach. Impulse response functions illustrate significant dynamic interrelationships in the financial sector. The results indicate that any coincident increase in finance diversification and capital structure of a firm optimizes a its profitability, and any increase in profitability results in increase of diver sification and capital structure. With increasing diversification, profitability will increase and capital structure will decrease. Further, if capital structure increases, the profitability will increase and diversification will decrease in firms listed in Tehran Stock Exchange.

One of the most important challenges in examining the behavior of investors in financial markets is measuring the volatility of financial assets. This is because stock price volatility is a latent variable. There are two basic approaches to modeling volatility in financial economics that differ in their probabilistic structure. In the first approach, volatility is modeled using shocks to stock returns, and in the second approach, volatility is transformed based on a stochastic process that can be independent of stock return dynamics over time. The models presented in the first approach of the GARCH class and in the second approach of the class constitute random volatility and Markov regime change. Despite the superiority of the probabilistic structure of these models, the calculation of model parameters and volatility prediction is very complex, which makes it necessary to use Bayesian methods and MCMC simulations. The results of this study indicate that in the period of this study, the existence of a leverage effect in the Tehran stock market is not confirmed and the MSGJR-GARCH method is more efficient in predicting fifty more active companies of Stock Exchange return volatility based on Bayesian information deviation criteria. Finally, based on the more efficient model, the out-of-sample VaR was calculated for the first seven days.

The aim of this study is to compare the New Hybrid Method with the usual Markowitz method in creating an optimal portfolio. To this end, at the first stage, the future stock prices were predicted using a deep DNN learning method and stock technical variables for the period 1397/4/2 to 1397/6/2. Then, based on future stock prices, stock return and risk were calculated and, by using Gravitational Algorithm, portfolio profits were maximized. This results in creating low risk to high risk portfolios on the Pareto efficient frontier. After that, the future return of portfolios was calculated for the next two months, and the process was repeated for 30 weeks in the form of weekly Rolling Window. These results were compared with the results of usual Markovitz method for 30 periods. The results indicated that both Markowitz and New Hybrid methods showed only better performance in predicting stock prices of risk averse portfolios than average market index.

Nowadays, bank deposits are one of the most important economic and social tools that depend on various business, service and economic conditions and have an important role in sustainable economic growth and development in countries. In order to provide financial assistance to depositors with privileged positions, equip the existing resources, and provide funds and create planning opportunities for the banking system, it is especially important to have access to adequate bank deposits.With a regard to the necessity of investigating and measuring the impact of economic variables on the amount of attracted deposits, we have used panel data to study the impacts of several factors on the amount of deposits in the banking system. These factors include the number of the employed, value added in various economic sectors and provinces, paid loans, active population, provincial household expenses, inflation rate, liquidity and weighted interest rate. The results are indicative of the fact that independent variables including service sector value added, paid loans and household expenses have positive impacts on the dependent variable. In contrast, the other variables including inflation and active population have a significant negative impact on the dependent variable. Different statistical tests were also conducted using the panel data to choose the random effect model as the final model. According to this model, there is no need to estimate separate regressions for each province.

Dendrograms are considered as the most important visualization technique of Hierarchical Clustering. We studied the stability of different dendrograms on the Tehran Stock Exchange and then stabilized the system of TSE indices using Principal Component Analysis (PCA). It seems that the comparison of dendrograms before and after PCA is a quantitative measure of system stability. ( 43% in our study of 21 different indices of TSE which are the representations of the economic sectors of Iranian financial systems. We measured the similarity of different dendrograms according to Baker's Index and the Cophenetic Correlation Index. The results show that stabilized hierarchical clusterings have got better Baker's Index and more reliable dendrogram. Therefore, It is highly recommended to investors, portfolio managers and risk hedgers to denoise and stabilize their clustering with efficient methods like PCA before financial decision making.

One of the most common causes or credit phenomenon that is taken into account for credit risk is the customer’s noncompliance with the commitments. Thus, by predicting the behavior of loan applicants, the growth rate of debts can be decreased. Hence, this study is conducted on corporate applicants for loans in one of the public banks in Iran. In this paper, the main elements comprising the customers’ behavior are selected with the help of categorized sample collection of 521 random samples from all corporate applicants. the process of data preparation, then, is accomplished by summarization, integration, and interpolation of some lost data. In the next step, 85 key performance indicators are selected for modeling. In order to measure the importance degree of the affecting elements on the customers’ behavior, the decision tree، neural net algorithms and Support Vector Machine were applied, the decision tree algorithm with 14 percent average absolute error, having the highest degree, was recognized as the top algorithm capable of assessing the probability of defaults. Finally, based on the available data and according to the results of the CHAID decision tree, contract maturity, amount of interest, number of installments, operating profit to asset, type of contract, average debt  in 3 months ago and loan amount are the most important indicators affecting the customers’ behavior. Taking these indicators into account, before granting the loans, could have a significant role in the prediction of the customers’ behavior and the related decision making.
 

In recent decades, the development of Islamic banking industry has experienced significant growth at the international level. And a variety of Islamic financial institutions (including Islamic banks) in Islamic and non-Islamic countries were formed and are working. Islamic banks in Islamic countries, have been in a competitive environment, their capabilities in financial intermediation in accordance with the religion, to meet the banking needs of Muslim clients (and even non-Muslim customers interested in Islamic banking) are well represented. The study attempts to examine the experiences and style of implementation of the Islamic banking in a sample of countries covered and the impact of Islamic banking on Resistance economic efficiency (growth). The result was the development of Islamic banking, the protection efficiency.

Looking at the economic definition of efficiency as optimal use of resources to produce possible maximum output, it can be understood the importance of this concept in management systems. Basically, Managers are trying to meet the satisfaction of all their stakeholders by optimally utilize of their resources to produce outputs. Due to high cost of holding money this point has more importance to the banking industry in Iran. In this paper, by looking at the structure of bank activities in Iran, a model with five different parts is provided that depicts the flow of affairs in banks. A mathematical model based on data envelopment analysis is presented to evaluate the efficiency of proposed structure and by using fuzzy approach, a method has been proposed to solve it. The results of applying the proposed model to 210 branches of one bank show that despite relative acceptable efficiency in resource attracting, and management, the efficiency of service, resource allocation and profitability parts are facing with important problem.

Risk and return of active mutual funds in Iran capital market. We apply multiple regression analysis, correlogram tests and GARCH model between 2008 – 2014 years to test the data. Findings show that there are some positive and negative relationships in return and risk of some mutual funds, however it is not generalized to all mutual funds as a regularity or behavior. In other words, there is no irrational trend in mutual funds’ risk and return. According to the results, there is no irrational return which has significant effect on all mutual funds in the sample of this study. Therefore, the public opinion including a decrease in return of mutual funds during Ramazan and Muharram months is invalid.

Behavioral finance is a new branch of finance that has been added valuable points of view on financial subjects, including the impact of special days on the financial market indexes. Hence, examining the effects of the religious months in Iran - the months of Ramadan and Muharram - is particularly important and has considerable results. In this paper examined the effects of the months of Ramadan and Muharram on the return and the return volatility of investment industry on the Tehran Stock Exchange. For this purpose, GARCH models and data in the period of 08/22/2009 till 08/21/2015 was used. The achieving results indicate that if the time trend effect doesn’t take into account, the investment industry volatility decreases in the months of Ramadan and Muharram. But by taking into account the time trend, the return and the return volatility of investment industry increases with the start of the month of Ramadan and the effect gradually decreases during the Month.