Journal of Economic Modeling Research

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The aim of this paper is to examine the impact of parametric reforms on the financial sustainability of the Social Security Organization, the largest social insurance organization in the country. To this end, an overlapping generations general equilibrium model is employed. The issue is analyzed through four different scenarios. The results show that in the first scenario, increasing life expectancy by 3 years without changing the retirement age will increase the ratio of expenditures to resources of the Social Security Organization by approximately 2%. In the second scenario, increasing the retirement age by 2 years and reducing life expectancy by 1 year will decrease the ratio of expenditures to resources by about 0.8%. In this case, the share of retirees' consumption in production and the labor force participation rate will decrease by 5% and 3%, respectively. In the third scenario, raising insurance premiums by 2% will not cause significant changes in the ratio of expenditures to resources due to a reduction in labor supply. Finally, in the fourth scenario, increasing both the retirement age and life expectancy by 2 and 3 years, respectively, will raise the ratio of expenditures to resources of the Social Security Organization by approximately 2.4%.
 

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The increase in greenhouse gas emissions is one of the crises in today's world. Because it doubles global warming and environmental pollution. The increase in greenhouse gas emissions has encouraged many countries to substitute renewable energy instead of fossil fuel. The effective use of green energy such as renewable energy and nuclear energy is highly dependent on the technology used in the production of this type of energy. For this reason, the aim of this study is to investigate the impact of importing information and communication technology goods on renewable energy production in Iran. In this research, has been used the Computable general equilibrium model based on the social accounting matrix of 2014. The results show that in all scenarios, the production of fossil electricity in both peak and base times, as well as the production of ICT goods, will decrease because with the release of the import of these goods, foreign ICT goods will replace domestic ones and the production of these goods will be domestic. Also, the production of other sectors has increased and the largest increase is related to the gas sector. By applying the first scenario (10 to 100% change in tariff, without change in the productivity of production factors related to the production of renewable energies), with the further reduction of the tariff, the production of renewable electricity will also decrease in both peak and base times, but when The fact that the import of ICT goods is accompanied by a 3, 5 and 7 percent increase in the productivity of the production factors related to the production of renewable energies (scenarios two to four) will increase the production of renewable electricity in the base load. The production of renewable electricity at peak load has decreased in all scenarios and the results do not change with the increase in efficiency. By reducing the tariff on the import of ICT goods, the amount of CO2 emissions will decrease. Also, as the productivity of the production factors related to the sector of renewable energy production increases, CO2 decreases to a greater extent. It should be noted that with the reduction of the tariff on the import of ICT goods, the price of the goods has decreased in the investigated sectors. As a result, reduce the pollution caused by the consumption of fossil fuels and use them optimally.

 

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The disclosure of bank information is a requirement of the Basell Committee in global level, as well as regulations governing the disclosure of information by credit institutions in Iran. According to these regulations, banks are obligated to disclose financial information, risk management information, corporate governance and auditing information, and information related to significant events. This article examines the short-term and long-term effect of  information disclosure on financial soundness of banks, with emphasis on the size and ownership of banks and using the PMG-ARDL model during 2014 - 2021. Results indicate an inverse U-shaped relationship between information disclosure and the financial soundness of banks. So an increase in information disclosure, the level of financial soundness of banks initially improves, but then decreases after reaching an optimal level. Additionally, there is a U-shaped relationship between information disclosure and the financial soundness of banks based on size. So an increase in disclosure and bank size, the financial soundness of banks initially decreases, but then increases after reaching a minimum point.

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The main goal of this research is the impact of the wage gap between managers and workers on stock returns: the mediating role of investors' supervision. In terms of categorizing the research according to the method of data collection, the current research is of the causal and post-event type. The research method is correlation. In this research, library methods were used to collect information. Library methods have been used to collect information on the theoretical foundations and literature of the topic, library resources, articles and required books have been used, and Kodal website and Rahavard Novin software have also been used to obtain statistical information. In this chapter, using data collected from a statistical sample of 76 companies admitted to the Tehran Stock Exchange in the period of 2015-2022. Hypotheses were tested using Pearson's correlation test and Limer's F test in the Eviews13 software environment. The results of the regression test showed that the wage gap between managers and workers with the mediating role of investors' supervision has a significant effect on stock returns.
 

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Purpose: The aim of this study is to identify and classify insurance customers in order to identify the target population for increasing the profitability of insurance companies, achieving a balance in premium payments, and examining the health questionnaire as an indicator of policyholders' preferences. Moreover, designing a marketing strategy to optimize advertising efficiency.
Method: In this paper, five machine learning algorithms, namely Decision Tree, Random Forest, Support Vector Machine, Naive Bayes, and Logistic Regression, are used to classify customers into two categories: profit-generating and loss-generating. Data from a private insurance company is utilized, consisting of 2,897 observations collected from December 1400 to December 1401.
Findings: By utilizing machine learning methods and focusing on the target population, the chances of success can be increased. The presence of a small number of individuals who significantly reduce the profitability of insurance companies is evident. The pre-existing medical conditions of individuals have a considerable impact on their insurance usage and the damage caused to insurance companies.
Conclusion: Machine-learning methods can provide a comprehensive understanding of insurance customers and their needs. By identifying the target population, insurance companies can increase their profitability and satisfy their customers by addressing their specific demands

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Bitcoin is the leader of cryptocurrencies and has the largest market value as a digital asset in most international investment portfolios. However, compared to traditional assets, the nature of this cryptocurrency is not clear from a behavioral perspective. Examining this by following the behavior of the distribution tail or limit behaviors is one of the methods that can help researchers about the nature of this cryptocurrency, because this corresponds to the investigation of limit behaviors and in critical times of this currency. In this regard, this research has used quantile regression to estimate CAViaR models. In addition, to study the effect of each variable on the Bitcoin trend, the GARCH approach has also been used.
The results of this research for the daily period from 2018 June 26 to 2022 May 11, Wednesday, showed that by analyzing the 5% percentile quantile regression, examining the behavior of the right tail of Bitcoin distribution, the behavioral similarity of this currency with all the investigated assets is confirmed. This shows that in a situation where the returns of traditional financial markets are positive and the markets are rising, the behavior of cryptocurrencies aligns with the general behavior of the markets. However, examining the behavior of the left tail of the distribution of the variables shows that Bitcoin has no similarity in behavior with the rest of the traditional assets. In other words, when markets are bearish, Bitcoin's behavior is not aligned with traditional markets. However, the return of the homogenous index does not affect the trend of Bitcoin, which was predictable due to the non-compliance of domestic financial markets with international markets due to Iran's economic isolation and international sanctions. Therefore, until the period investigated by this study, Bitcoin has shown a behavior other than known assets and investing in it is still facing the risk of capital burnout, so it is recommended that investors observe risk management in the arrangement of their portfolios.
 

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Based on the stylized fact, the behavior of price in financial markets is not a continuous process, but we observe jumps in the price that can be endogenous or exogenous. it is claimed that the source of exogenous jumps is news, and the source of endogenous jumps is internal interactions between the market agents.  Our goal is to extract these endogenous jumps as a function of the system state variable and time. First, by introducing the Langevin equation as the governing dynamics and linking its parameters with Kramers-Moyal coefficients, we show that these parameters can be extracted based on conditional moments. Next, we use the generalized Langevin equation to model the observed jumps in the data and show that in the new model, the drift coefficient is still equal to the first Kramers-Moyal coefficient, but the diffusion coefficient in this case is lower than the second Kramers-Moyal coefficient. In our model, the jump term consists of two components: jump rate and jump size. We show that these two new components can also be extracted based on Kramers-Moyal coefficients. Also, we introduce a practical criterion based on the fourth and sixth Kramers-Moyal coefficients to choose between the diffusion and jump-diffusion model. Applying the Kramers-Moyal method to extract the generalized Langevin equation shows that this method can accurately reconstruct the process. Tests to evaluate the accuracy of the reconstruction have been used from the information theory. In a practical application, we have extracted the price dynamics of an asset and then shown by simulation that this model is able to answer common statistical questions about stochastic processes with good accuracy. Also, by performing simulations, we show that this model has a good out-of-sample prediction ability. The potential function, which is calculated from the first KM coefficient, is a quadratic parabola for the studied process, and as a result, we have a stable equilibrium at the zero point.

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Objective: The aim of this research is to provide a practical guide for investing in the Tehran Stock Exchange by combining technical analysis techniques with advanced machine learning methods. Focusing on the analysis of buy and sell signals in selected indices of the Tehran Stock Exchange, the study seeks to evaluate the effectiveness of machine learning models in predicting market trends. Materials and Methods: In this study, the daily data of six selected indices of the Tehran Stock Exchange, including financial, petroleum products, automotive, pharmaceutical, food, and basic metals indices, were analyzed from 2020 to January 2025. Four machine learning models, including Linear Model, Random Forest, Artificial Neural Network, and Support Vector Regression, were utilized alongside two technical analysis strategies, TEMA and MACD, to generate and evaluate buy and sell signals. Results: The results indicated that machine learning models, particularly Random Forest and Artificial Neural Network, performed better in identifying buy and sell signals when combined with TEMA and MACD strategies. These models were able to predict market trends with higher accuracy, and the signals they generated were mostly consistent with actual price changes. The food, automotivation and financial and basic metals indices demonstrated greater sensitivity to these analyses. Conclusion: The combination of machine learning methods with technical analysis strategies can provide investors with a powerful tool for decision-making in the Tehran Stock Exchange. This research demonstrated that using these methods can not only improve the accuracy of buy and sell signals but also reduce investment risk and increase returns. Utilizing these models can be recommended as part of an investment strategy for analysts and investors. Originality: This research is the first quantitative study that seeks to conceptualize buy and sell signals using the combined method of machine learning and technical analysis as one of the basic tools to guide investors.

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The stock market, as one of the vital components of the capital market, is an important part of the country's economy that can manage the flow of capital, optimize capital allocation, and thereby contribute to economic growth and development. More accurate prediction of the stock market trend can help investors' decision-making for higher returns by reducing risk. In general, the stock market is constantly changing and many factors influence the trend of this market, so predicting the patterns of movement in the stock exchange requires sufficient information about the past and influencing factors of the market. This article is part of the forecast of the stock market index of Iran, seeking to interpret the model and identify the most influential economic variable on the price index prediction. For this purpose, daily stock market and economic data, during the period 1394-1401 were used. Machine learning models are also used for prediction and the Shapley Additive exPlanations (SHAP) to interpret how to predict and determine the most important variables in the predictive model. Based on results from tree-based ensemble methods, the proposed model in this study, ExtraTrees, performed best based on predictive error criteria. In the study of the feature importance is also based on the ExtraTrees model, in order of the dollar rate (Nima), unemployment rate, dollar rate of market and liquidity, the most important economic variables influencing the forecast model. Also, according to other models used in the research, liquidity is the most effective variable on the stock index trend. Finally, it can be said that the most effective monetary variables on the stock market index in Iran are liquidity and exchange rate variables, so monetary policymakers and stock market investors should be more sensitive to these variables in their decisions.

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Economy and environment are two interdependent systems; In recent decades, the global environment, as the most important global public good, has been heavily influenced by the negative external effects of economic growth, including climate change. In order to internalize these external effects, the use of tracking tax is a recommended method. One of the most important models designed for the integrated study of economy and climate is the Nordhaus RICE model; Of course, with the limitation that in this economic growth model, it is included exogenously. In this study, the aim of endogenizing the economic growth of the RICE model and determining the tax rate in 6 scenarios including 1) the base scenario 2) the optimal emission control rate application scenario 3) the 2°C temperature limit scenario 4) the discounted Stern scenario 5) the calibrated Stern scenario and 6) Copenhagen scenario. The results show that in the endogenous growth model, the ratio of taxes to net domestic production and CO2 emissions should increase over time. In all scenarios of Iran's endogenous growth model (except the base scenario), tax increases between 2022 and 2122 will reduce industrial CO2 emissions and reduce atmospheric carbon concentration. Finally, by applying the specified optimal tax in all scenarios, temperature changes have increased by less than two degrees Celsius.
 

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Today, information and communication technology affects all aspects of human life. The result of which is a transformation in all methods of production and distribution to education, exchanges and human relations. On the other hand, the requirement for the realization of economic development and growth is the higher growth rate in poor and underdeveloped areas than in rich and developed areas, which is proposed as the hypothesis of convergence. In this regard, regional inequalities are a fundamental challenge for the development of regions and these inequalities are a serious threat to create a balanced development of regions. Therefore, the main goal of the current study is to investigate the convergence of Fava among the provinces of the country. The results using the Nahar and Inder method during the period of 2002-2013 showed that out of the 30 investigated provinces, divergence in land use occurred in 22 provinces. Also, at the infrastructure level, the average slope of 31 provinces is positive, but the t-value is significant for the provinces of South Khorasan, Khuzestan, Alborz and Fars, which shows that digital divergence has occurred in these provinces during the period under review. Therefore, it can be seen that although in terms of infrastructure, we have had less divergence at the level of the provinces, but in terms of usage, this gap and divergence has increased.

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Although, input-output (I-O) and general equilibrium (CGE) models are systems based on interrelations between sectors and economic agents. However, they are very different in terms of design complexity, model solving techniques, required input data, outcomes, theorical structure and adopted assumptions.
Therefore, the results of simulating the effects of an economic policy using each of these models will definitely be different. But, avoiding complexity, difficult solving techniques, collecting large amount of data and so on, sometimes make us to use simpler, although less accurate, models. In this case, obviously, the assumptions and restrictions of the model are to be fully noticed, when interpreting the outcomes of simulation. Otherwise, incorrect decision making will be inevitable.In this paper, we assess the impact of price liberalization of electricity, distribution of natural Gas and water on the output and prices of the main sectors of Iran economy. This is done by using a I-O and a CGE, both designed by autors. The simulation is implemented by cutting subsidy payed to final and intermediate consumption of electricity, distribution of natural Gas and water. The outcomes are largely different regarding the amount and the direction of sectoral output and price changes. While the outcomes of I-O model are against this policy, the results of CGE model, in which total output and household consumption increase, are in favor of it. The rise of demand in tradeable agriculture and industries and mining are accompanied with more import, so that their new equilibrium output will be lesser. But, output of non-tradeable construction and low tradeable services increase. The increase of crude oil and natural gas are totaly exported.

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Although many factors in economic growth and development are scientific, but the global impact and energy consumption have a prominent role in the economy according to the evidence. In the meantime, we should not ignore the consequences of environmental destruction. In the present study, the effect of uncertainty of globalization and energy consumption on CO2 gas emission has been investigated with the help of fuzzy regression model with symmetric and asymmetric coefficient for the time period of 1369-1400. According to the average scale of the phased vessel model, the three boundaries and the bottom are calculated for each of the investigated changes under different uncertainty conditions using the particle swarm algorithm. Examining the effect of the limits related to the uncertainty of globalization and energy consumption on the amount of CO2 gas emissions indicates that as the degree of membership approaches 0.1 to the degree of membership 0.9, first, the amount of CO2 gas emissions up to be Membership increased by 0.4 and then decreased in a downward trend of CO2 emissions. This impressive trend is also true for the middle and lower limits. From this, it can be stated that the effect of the uncertainty of energy consumption on the amount of CO2 emissions is similar to an inverted U. It is noteworthy that the trend of energy consumption compared to globalization increases the amount of CO2 emissions, so it can be said that the amount of CO2 emissions is not the result of the refugee hypothesis.

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In Islamic and conventional banks, there may always be differences in efficiency, cost gap ratio and credit risk; Therefore, the purpose of this study was to investigate the relationship between credit risk, cost gap ratio and efficiency of banks in selected Islamic and conventional countries. For this purpose, credit risk, efficiency and cost gap ratio were calculated for 50 Islamic banks and 50 non-Islamic (conventional) banks during the years 2013-2019 and regard, the results of the t-test showed that the credit risk, inefficiency and cost gap ratio are higher in Islamic banks than conventional banks. Also, the results of the Granger causality test showed that there is a bidirectional causality relationship between inefficiency and credit risk. But this relationship is a little weak. There is no causal relationship between credit risk and cost gap ratio, and there is a strong two-way causality relationship between inefficiency and cost gap ratio. The results in Islamic banks are almost similar to all banks. However, in Islamic banks, credit risk is not the Grangerian cause of inefficiency. Also, the significance level of other causal relationships is much higher. For conventional banks, there is no causal relationship between inefficiency and credit risk. There is no causal relationship between credit risk and cost gap ratio, and there is a two-way causality relationship between inefficiency and cost gap ratio. In addition, the results of variance analysis indicate that the cost gap and inefficiency have a close relationship with each other and their effectiveness is high. Credit risk also has a more or less effect on inefficiency during future periods. In Islamic banks, the effects of inefficiency and cost gap on credit risk are slightly higher. But the inefficiency of the cost gap has a high effectiveness. In conventional banks, credit risk has the same effectiveness as Islamic banks, but its effectiveness is somewhat less. Also, inefficiency has less effects than the cost gap. The cost gap, like Islamic banks, has a high effectiveness of inefficiency, and this effectiveness decreases over time.
 

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This study explores how monetary and fiscal policies influence certain macroeconomic variables through a multi-sector stochastic dynamic general equilibrium (DSGE) model that includes input-output (IO) analysis. The focus is on the industrial sector, taking into account the specific conditions for Iran. The research uses quarterly data from Spring 2006 to Spring 2023 and references the 2016 input-output table provided by the Central Bank. In the nonlinear model, the original 89 activities from the input-output table have been simplified to 9, which includes the industrial sector and eight other sectors. Model parameters are estimated based on previous studies of the Iranian economy and data from the input-output table. The model's effectiveness is assessed by comparing simulation results with real-world data, which shows a strong correlation. The simulations indicate that increases in the money supply result in only a small rise in both total and industrial output. This leads to a slight decrease in total employment, while employment in the industrial sector experiences a minor increase. Similarly, increases in government spending show tiny improvements in overall and industrial output, accompanied by a slight drop in total employment and a small rise in the industrial sector. The findings suggest that the effects of monetary and fiscal policy shocks on output and employment, when accounting for input-output relationships and dividing the economy into nine sectors, better reflect the realities of the Iranian economy. Given the minimal influence of these policies on boosting production and economic growth, it is essential for them to be targeted and supported by additional measures and strategies.
 

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The main purpose of this study is to investigate the effect of factors affecting the ecological footprint with an emphasis on the role of energy consumption intensity in Iran using the Vector Autoregression Model with Variable Parameters Over Time (TVP-VAR). The ecological footprint reflects the environmental constraints of communities and the extent to which the environment is destroyed by exceeding these limitations. Due to the increasing intensity of energy consumption, Iran is faced with a significant ecological footprint in its economic activities, which requires the root causes of the factors affecting it. Other research variables include the degree of urbanization, human development, financial development, trade openness, and GDP per capita in the period from 1990 to 2021. The results show that increasing the intensity of energy consumption causes a positive and significant increase over time on the ecological footprint. The effect of other research variables on the ecological footprint was also in accordance with theoretical expectations. These findings emphasize that the type and source of energy consumed, as well as the production processes, play an important role in this relationship. Also, the analyses show that environmental sustainability decreases with increasing energy consumption and the ecological footprint of

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The fundamental aim of this study is to investigate the structural dependence between the cryptocurrency and the stock market index. In this study, the total index of Tehran Stock Exchange has been used as a representative of the developing stock market and the index (S&P500) has been used as a representative of the developed stock market. using daily data during the period from 8 August 2015 to 21 February 2023. The results show that there is no structural dependence between the return Bitcoin and Iran stock market , either in the short term or in the long term. In other words, the changes domain in return of Bitcoin during the low and high ranges on the return of the mentioned index are insignificant. The results indicates that the cryptocurrency market is separated from the main class of financial and economic assets and hence offers various benefits to investors. Also, in the long term, for the return of Bitcoin cryptocurrency and the S&P500 stock index, Clayton's copula function was chosen in the first place as the appropriate model to explain the correlation. There is no correlation between the returns of Bitcoin and the s&p500 stock index in the short term. The findings of this study indicate the important role of cryptocurrencies in investors' portfolios as they act as a diversified option for investors and confirm that cryptocurrencies are a new investment asset class. Furthermore, it analyzes the upside and downside risk spillovers between stock markets and the cryptocurrency market by quantifying market risk measures, namely the conditional VaR (CoVaR) and the delta CoVaR (ΔCoVaR). The results indicate that Bitcoin, Ethereum and Ripple cannot be considered a strong hedge during the time of crisis. The speculative nature of cryptocurrencies and risks embedded in Bitcoin, Ethereum, and Ripple increases the risk flow to stock markets during a crisis, thus rendering the hedging costlier.  increases the risk flow to stock markets during a crisis, thus rendering the hedging costlier.
 

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Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.
Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.