Shahid Chamran University of AhvazQuarterly Journal of Quantitative Economics2008-585019420230220The Comprehensive Analysis of the Impact of Globalization on Environmental Pollution in Iran with Emphasizing on Triple Dimensions and Dual ComponentsThe Comprehensive Analysis of the Impact of Globalization on Environmental Pollution in Iran with Emphasizing on Triple Dimensions and Dual Components1411639910.22055/jqe.2021.33177.2239FAShahryar ZarokiAssociate Professor of Economics, Department of Economics, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran.Arman Yousefi Barfurushi** Master of Science in Economics Science, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, IranAmirhossein FathollahzadehMaster of Science in Energy Economics, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran.Journal Article20200406<strong>EXTENDED ABSTRACT</strong>
<strong>INTRODUCTION </strong>
In recent decades, the industrialization of countries has been one of the main factors for the increase of greenhouse gas emissions, which has led to severe environmental damage such as global warming, temperature inversion. In addition, the increase of environmental pollutants may have a negative impact on the health of individuals in the long run, thus increasing government health expenditures. In addition to the various factors affecting greenhouse gas emissions, globalization (economic, social, and political) can also affect the environment through various channels. For example, economic globalization affects the environment through the channels of income, composition, and technology. Moreover, social globalization can change the state of the environment through the media, the principle of mental distance, transportation, and access to knowledge and international events. Political globalization may also have an impact on the environment through the efficiency of intergovernmental organizations and international treaties. Considering the possible role of globalization (economic, social, and political) on the environment, this study attempts to investigate the relationship between globalization (economic, social, and political) and carbon dioxide emissions in Iran during 1979-2017 based on the Autoregressive Distributed Lag (ARDL) approach. Moreover, this study tests the hypothesis of Martens et al. (2015) about the effectiveness of the globalization index results in terms of components (de facto and de jure). For further clarification, the study examines the above hypothesis using the three dimensions of economic, social, and political. In addition, the KOF Globalization Index is used, which consists of three dimensions: Economic, Social and Political, and each of the dimensions has two de facto and de jure components. Clearly, the impact of each of these dimensions of globalization and its components on the emission of environmental pollutants may be different. It should also be noted that the two de facto and de jure components in the KOF Globalization Index are known as a new classification introduced by Martens et al. (2015).
<strong>METHODOLOGY </strong>
As mentioned in the introduction, the aim of this study is to analyze the effect of globalization on pollution in Iran. In order to make a comprehensive analysis, the dimensions (economic, social and political) and components (de facto and de jure) of globalization are also utilized. According to the theoretical and experimental studies, the regression model is specified in two formats, one is based on the dimensions of globalization (economic, social, and political) and the other is based on the components of globalization (de facto and de jure).
<strong>First model: the effect of globalization at the level of j on environmental pollution</strong>
The basis of the first model is the regression equation in equation (1). EP represents the logarithm of carbon dioxide emissions. EC represents the logarithm of energy consumption, Pop represents the logarithm of population, GDP and GDP<sup>2</sup> represent the logarithm of real gross domestic product and its square, respectively. In addition, Gloj represents the logarithm of the globalization index at level j, including total (T), economic (E), social (S), and political (P).
Based on equation (1), the ARDL(p,q,r,s,t,u) model is designed in the form of equation (2). In this regard, is the coefficient of autocorrelation, is the coefficient of globalization index intervals; And to are coefficients of interruptions of energy consumption, real GDP, square of real GDP and population, respectively.
Based on equation (2), the error correction model and long-run coefficients in the form of equation (3) is specified as follows:
<strong>Second model: the effect of Components of globalization at the level of j on environmental pollution</strong>
The basis of the model in this format is the regression equation in equation (4), where GloDeFa<sup>j</sup> and GloDeJe<sup>j</sup> are respectively the logarithm of the de facto and de jure globalization indexes at level j.
Similarly, based on equation (4), the ARDL(p,q,r,s,t,u,v) model is designed in the form of equation (5), where θ_1j and θ_2j are the coefficients of de facto and de jure globalization index at level j, respectively.
Based on equation (5), the model of error correction and long-run coefficients is specified in the form of equation (6) as follows:
<strong>CONCLUSION and POLICY REMARKS</strong>
The results of the data descriptions show that the dimensions and components of the globalization index have different trends, and carbon dioxide emissions have had an increasing trend since Iran's Islamic Revolution. The estimation results of the models indicate that the economic and social globalization indices have a positive effect on carbon dioxide emissions in the short term. Moreover, the de facto component of total and economic globalization has a negative effect on CO2 emissions. Furthermore, the de facto component of the social and political globalization index has a positive effect on carbon dioxide emissions, while the de facto component of total and economic globalization with a one-year lag has a positive effect on carbon dioxide emissions. In the long run, the total globalization index has no significant effect on carbon dioxide emissions, but two dimensions of social and economic globalization have a direct effect on carbon dioxide emissions. Besides, the de jure component of the total globalization index and the de jure component of the economic, social, and political dimensions directly affect carbon dioxide emissions, but the de facto component of the total and economic globalization index has a reverse effect on carbon dioxide emissions. Moreover, energy consumption and population have a direct effect on CO2 emissions, and the Environmental Kuznets Curve (EKC) hypothesis cannot be rejected in Iran. The hypothesis of Martens et al. (2015) is also not rejected in this study.
On this basis, it is suggested that economic, social, and political decision-makers pay attention to the negative environmental impacts of the de jure component of economic, social, and political globalization and take action to optimize the environmental impacts of the de jure component of each of the three dimensions by looking closely at each of the sub-dimensions. Moreover, considering the favorable effect of the de facto component of economic globalization on the environment, it is better to give higher priority to economic globalization from a de facto point of view than to economic globalization from a de jure one, so that the Iranian economy can benefit from the advantages of globalization in the field of economy, as well as the environment can be spared from the damages caused by the expansion of the economy.<strong>EXTENDED ABSTRACT</strong>
<strong>INTRODUCTION </strong>
In recent decades, the industrialization of countries has been one of the main factors for the increase of greenhouse gas emissions, which has led to severe environmental damage such as global warming, temperature inversion. In addition, the increase of environmental pollutants may have a negative impact on the health of individuals in the long run, thus increasing government health expenditures. In addition to the various factors affecting greenhouse gas emissions, globalization (economic, social, and political) can also affect the environment through various channels. For example, economic globalization affects the environment through the channels of income, composition, and technology. Moreover, social globalization can change the state of the environment through the media, the principle of mental distance, transportation, and access to knowledge and international events. Political globalization may also have an impact on the environment through the efficiency of intergovernmental organizations and international treaties. Considering the possible role of globalization (economic, social, and political) on the environment, this study attempts to investigate the relationship between globalization (economic, social, and political) and carbon dioxide emissions in Iran during 1979-2017 based on the Autoregressive Distributed Lag (ARDL) approach. Moreover, this study tests the hypothesis of Martens et al. (2015) about the effectiveness of the globalization index results in terms of components (de facto and de jure). For further clarification, the study examines the above hypothesis using the three dimensions of economic, social, and political. In addition, the KOF Globalization Index is used, which consists of three dimensions: Economic, Social and Political, and each of the dimensions has two de facto and de jure components. Clearly, the impact of each of these dimensions of globalization and its components on the emission of environmental pollutants may be different. It should also be noted that the two de facto and de jure components in the KOF Globalization Index are known as a new classification introduced by Martens et al. (2015).
<strong>METHODOLOGY </strong>
As mentioned in the introduction, the aim of this study is to analyze the effect of globalization on pollution in Iran. In order to make a comprehensive analysis, the dimensions (economic, social and political) and components (de facto and de jure) of globalization are also utilized. According to the theoretical and experimental studies, the regression model is specified in two formats, one is based on the dimensions of globalization (economic, social, and political) and the other is based on the components of globalization (de facto and de jure).
<strong>First model: the effect of globalization at the level of j on environmental pollution</strong>
The basis of the first model is the regression equation in equation (1). EP represents the logarithm of carbon dioxide emissions. EC represents the logarithm of energy consumption, Pop represents the logarithm of population, GDP and GDP<sup>2</sup> represent the logarithm of real gross domestic product and its square, respectively. In addition, Gloj represents the logarithm of the globalization index at level j, including total (T), economic (E), social (S), and political (P).
Based on equation (1), the ARDL(p,q,r,s,t,u) model is designed in the form of equation (2). In this regard, is the coefficient of autocorrelation, is the coefficient of globalization index intervals; And to are coefficients of interruptions of energy consumption, real GDP, square of real GDP and population, respectively.
Based on equation (2), the error correction model and long-run coefficients in the form of equation (3) is specified as follows:
<strong>Second model: the effect of Components of globalization at the level of j on environmental pollution</strong>
The basis of the model in this format is the regression equation in equation (4), where GloDeFa<sup>j</sup> and GloDeJe<sup>j</sup> are respectively the logarithm of the de facto and de jure globalization indexes at level j.
Similarly, based on equation (4), the ARDL(p,q,r,s,t,u,v) model is designed in the form of equation (5), where θ_1j and θ_2j are the coefficients of de facto and de jure globalization index at level j, respectively.
Based on equation (5), the model of error correction and long-run coefficients is specified in the form of equation (6) as follows:
<strong>CONCLUSION and POLICY REMARKS</strong>
The results of the data descriptions show that the dimensions and components of the globalization index have different trends, and carbon dioxide emissions have had an increasing trend since Iran's Islamic Revolution. The estimation results of the models indicate that the economic and social globalization indices have a positive effect on carbon dioxide emissions in the short term. Moreover, the de facto component of total and economic globalization has a negative effect on CO2 emissions. Furthermore, the de facto component of the social and political globalization index has a positive effect on carbon dioxide emissions, while the de facto component of total and economic globalization with a one-year lag has a positive effect on carbon dioxide emissions. In the long run, the total globalization index has no significant effect on carbon dioxide emissions, but two dimensions of social and economic globalization have a direct effect on carbon dioxide emissions. Besides, the de jure component of the total globalization index and the de jure component of the economic, social, and political dimensions directly affect carbon dioxide emissions, but the de facto component of the total and economic globalization index has a reverse effect on carbon dioxide emissions. Moreover, energy consumption and population have a direct effect on CO2 emissions, and the Environmental Kuznets Curve (EKC) hypothesis cannot be rejected in Iran. The hypothesis of Martens et al. (2015) is also not rejected in this study.
On this basis, it is suggested that economic, social, and political decision-makers pay attention to the negative environmental impacts of the de jure component of economic, social, and political globalization and take action to optimize the environmental impacts of the de jure component of each of the three dimensions by looking closely at each of the sub-dimensions. Moreover, considering the favorable effect of the de facto component of economic globalization on the environment, it is better to give higher priority to economic globalization from a de facto point of view than to economic globalization from a de jure one, so that the Iranian economy can benefit from the advantages of globalization in the field of economy, as well as the environment can be spared from the damages caused by the expansion of the economy.https://jqe.scu.ac.ir/article_16399_781d0aa5a2f9ab5365189f968e8da718.pdfShahid Chamran University of AhvazQuarterly Journal of Quantitative Economics2008-585019420230220Comparison of GARCH Family Models in Estimating Value at Risk and Conditional Value at Risk on the Tehran Stock ExchangeComparison of GARCH Family Models in Estimating Value at Risk and Conditional Value at Risk on the Tehran Stock Exchange43781724010.22055/jqe.2021.33186.2240FALeila TorkiAssistant Professor of Economics, Department of Economics, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.Neda EsmaeliAssistant Professor, Faculty of Mathematics and Statistics, Department of Applied Mathematics and Computer Science, University of Isfahan, Isfahan, Iran.Masoumeh HaghparastMs. of Economics, Department of Economics, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.Journal Article20200407<strong>EXTENDED ABSTRACT</strong><br /><strong>INTRODUCTION </strong><br />Several criteria have been introduced in recent decades to measure risk, and each of them investigates the uncertainty problem from different perspectives. In 1996, thanks to the advancement of the mathematical sciences and statistics in evaluating undesirable risks, the criterion of Value at Risk (VaR) was introduced to measure risk, and it was welcomed by investors and financial analysts.<br /> Therefore, the purpose of this study is to compare the average values of Value at Risk and Conditional Value at Risk(CvaR) estimated by five GARCH patterns for the Tehran Stock Exchange(TSE) index, in which the property of Arch is seen. Since the existence of the fat tail in the probability distribution of financial data has been confirmed, the T-Student distribution has been studied in addition to the normal distribution. <br /> The results suggest that T-Student distribution has a more favorable performance for estimating Value at Risk and Conditional Value at Risk. Furthermore, there is no significant difference among the averages of Value at Risk and Conditional Value at Risk which are estimated by different GARCH patterns.<br /> <br /><strong>METHODOLOGY </strong><br />This study seeks to investigate the difference among the average values of Value at Risk and Conditional Value at Risk for the index of Tehran Stock Exchange estimated by five GARCH patterns. Since the attractiveness of parametric statistics such as ease of generalizability and the existence of powerful quantification tools, parametric approaches contain a variety of models in the field of risk. Hence, in this study, VaR and CVaR with a parametric approach have been calculated using different generalised autoregressive conditional heteroskedasticity patterns. In addition, after comparing the patterns with each other, the best pattern has been introduced. The applied research method in terms of purpose and data collection belongs to the applied and documentary studies, respectively and statistical population is the time series of daily data on the TSE index during the period 09/01/2013 to 09/01/2016 and includes 1445 observations.<br /> The method of collecting data is a research library based on the daily reports of the TSE index, which is available on the Financial Information Processing of IRAN site (Fipiran.com). Analytical tools are econometric and statistical techniques and the generalised models of autoregressive conditional heteroskedasticity have been implemented. Moreover, the used softwares are Excel, R, Eviews 11.<br /> For the ADF unit root test, a regression equation taking into the account the width in origin and the linear time trend is used. (Pesaran, 2005). The ADF regression order can be selected using the model selection criterion, such as the Akaik (AIC) or Schwartz (SIC). The Kopik test can be applied to the calculation of model accuracy to determine the value at risk. This test uses a very simple method to measure the error of the VaR calculation for past data. Since the Kopic test focuses only on the number of violations and ignores the existence of time dependencies. Christofferson (1998) developed the Kopik test and proposed a test for the level of conditional coverage by considering a separate statistic for the violation of independence test.<br /> <br /><strong>FINDINGS </strong><br />The results of the reliability test for the variables indicate that at all levels of reliability, the data are not rooted and are reliable. Therefore, the ambiguity related to creating false regression due to data instability is removed.<br /> The Lagrangian coefficient test for the TSE index proves the presence of the arch effect on the remainder of the autocorrelation equation for MA (1,1). Therefore, it is quite obvious that the work does not have the property of white noise despite the remaining arch, and generalized autoregressive conditional heteroskedasticity patterns should be used, which removes the arch effect from the data.<br />The results of estimating GARCH (1,1) show that the mean equation for the selected index in all cases is ARMA (1,1). Furthermore, the sum of the estimated coefficients in GARCH (1,1) for both distributions is close to one, indicating the existence of stability in the variance process. As a result of estimating the EGARCH model (1,1), It has been observed that the gamma parameter for both normal and T-student distributions are 0.4 and 0.3, respectively, which displays the presence of a positive leverage effect. By estimating the GARCH-M (1,1), we get GARCH-M (1,1) coefficients for both normal and T-student distributions, which are 0.5 and 0.2, respectively. This coefficient is the same as the risk margin, which indicates that the return is positively dependent on the volatility. The presence of this risk margin and its significance means the existence of a consistent correlation in past asset returns. The results of estimating two models APARCH (1,1), J GJRGARCH (1,1), which are other types of GARCH models used to model leverage effects, show that in GJRGARCH model the gamma coefficients for both normal and T-student distributions are -0.1 and -0.07, respectively. These coefficients indicate the presence of a negative leverage effect. Moreover, In the APARCH model the gamma coefficients for both normal and T-student distributions are -0.2 and -0.1, respectively, which proves the attendance of a negative leverage effect.<br /> All in all, by applying the different Garch models for VaR and CVaR, the highest values for the TSE index Tehran at two confidence levels of 95% and 99% belong to the GARCH-M model (1,1) with the T-student distribution. The obtained values are 0.4850% and 0.8619%, respectively. In addition, for the normal distribution at the 95% confidence level, the GARCH-M, EGARCH, GJRGARCH, APARCH and GARCH models show the highest risk values, respectively. However, for the T-student distribution, GARCH-M, APARCH, EGARCH, GJRGARCH, and GARCH models present the highest risk values, respectively.<br /> Furthermore, for the normal distribution at the 99% confidence level, GARCH-M, EGARCH, APARCH, GJRGARCH, and GARCH models show the highest risk values, respectively, and for the T-student distribution, GARCH-M, APARCH, EGARCH, GJRGARCH and GARCH models have the highest risk value, respectively. At two levels of 95% and 99% confidence, the highest value of conditional risk is related to the GARCH-M model (1,1) with T-student distribution and with rates 0.6492% and 2863%, respectively. Considering the normal distribution at both 95% and 99% confidence level, GARCH-M, EGARCH, APARCH, GJRGARCH and GARCH models show the highest CVaR, respectively. However, assuming T-student distribution for both levels of 95% and 99% confidence results that the GARCH-M, APARCH, EGARCH, GJRGARCH and GARCH patterns have the highest conditional risk value, respectively.<br /> The accuracy and adequacy of different models were evaluated by back testing methods. The results indicate that for the calculated VaR at 95% confidence level, the null hypothesis is rejected for the normal distribution. This means that none of the patterns with normal distribution have sufficient validity to calculate VaR. On the other hand, the null hypothesis for all models with the T-Student distribution is not rejected at the 95% level of confidency. and all the models with the T-Student distribution are valid to calculate VaR. At the 99% confidence level for both normal and T-student distributions, the results show that based on the corresponding ps of Kopic and Christofferson tests, all patterns have the necessary validity and adequacy to calculate VaR, and the null hypothesis is not rejected.<br /> <br /><strong>CONCLUSION </strong><br />The results propose the T-Student distribution as a favorable probability distribution for estimating Value at Risk and Conditional Value at Risk through the five introduced GARCH models. Moreover, there is no significant difference among the averages of Value at Risk and Conditional Value at Risk which are estimated by different GARCH patterns. The findings of this research aim to help better investment in the stock market. Therefore, investors and agents can also benefit from the applied methods in this research to calculate the daily risk value for different industries in TSE.<br /> For future research, it is recommended to use very recent GARCH models such as 3D-GARCH, COGARCH, GARCH-MIDDAS, etc. Furthermore, according to the significant effect of probability distribution selection, it is suggested to check other probability distributions. In the present paper, the adequacy and accuracy of different models have been evaluated. However, due to the possibility of VaR overestimation by a model which results in the loss of resource allocation in a bank or company, ... , it is suggested to rank different methods for value at risk by using different loss function methods.<strong>EXTENDED ABSTRACT</strong><br /><strong>INTRODUCTION </strong><br />Several criteria have been introduced in recent decades to measure risk, and each of them investigates the uncertainty problem from different perspectives. In 1996, thanks to the advancement of the mathematical sciences and statistics in evaluating undesirable risks, the criterion of Value at Risk (VaR) was introduced to measure risk, and it was welcomed by investors and financial analysts.<br /> Therefore, the purpose of this study is to compare the average values of Value at Risk and Conditional Value at Risk(CvaR) estimated by five GARCH patterns for the Tehran Stock Exchange(TSE) index, in which the property of Arch is seen. Since the existence of the fat tail in the probability distribution of financial data has been confirmed, the T-Student distribution has been studied in addition to the normal distribution. <br /> The results suggest that T-Student distribution has a more favorable performance for estimating Value at Risk and Conditional Value at Risk. Furthermore, there is no significant difference among the averages of Value at Risk and Conditional Value at Risk which are estimated by different GARCH patterns.<br /> <br /><strong>METHODOLOGY </strong><br />This study seeks to investigate the difference among the average values of Value at Risk and Conditional Value at Risk for the index of Tehran Stock Exchange estimated by five GARCH patterns. Since the attractiveness of parametric statistics such as ease of generalizability and the existence of powerful quantification tools, parametric approaches contain a variety of models in the field of risk. Hence, in this study, VaR and CVaR with a parametric approach have been calculated using different generalised autoregressive conditional heteroskedasticity patterns. In addition, after comparing the patterns with each other, the best pattern has been introduced. The applied research method in terms of purpose and data collection belongs to the applied and documentary studies, respectively and statistical population is the time series of daily data on the TSE index during the period 09/01/2013 to 09/01/2016 and includes 1445 observations.<br /> The method of collecting data is a research library based on the daily reports of the TSE index, which is available on the Financial Information Processing of IRAN site (Fipiran.com). Analytical tools are econometric and statistical techniques and the generalised models of autoregressive conditional heteroskedasticity have been implemented. Moreover, the used softwares are Excel, R, Eviews 11.<br /> For the ADF unit root test, a regression equation taking into the account the width in origin and the linear time trend is used. (Pesaran, 2005). The ADF regression order can be selected using the model selection criterion, such as the Akaik (AIC) or Schwartz (SIC). The Kopik test can be applied to the calculation of model accuracy to determine the value at risk. This test uses a very simple method to measure the error of the VaR calculation for past data. Since the Kopic test focuses only on the number of violations and ignores the existence of time dependencies. Christofferson (1998) developed the Kopik test and proposed a test for the level of conditional coverage by considering a separate statistic for the violation of independence test.<br /> <br /><strong>FINDINGS </strong><br />The results of the reliability test for the variables indicate that at all levels of reliability, the data are not rooted and are reliable. Therefore, the ambiguity related to creating false regression due to data instability is removed.<br /> The Lagrangian coefficient test for the TSE index proves the presence of the arch effect on the remainder of the autocorrelation equation for MA (1,1). Therefore, it is quite obvious that the work does not have the property of white noise despite the remaining arch, and generalized autoregressive conditional heteroskedasticity patterns should be used, which removes the arch effect from the data.<br />The results of estimating GARCH (1,1) show that the mean equation for the selected index in all cases is ARMA (1,1). Furthermore, the sum of the estimated coefficients in GARCH (1,1) for both distributions is close to one, indicating the existence of stability in the variance process. As a result of estimating the EGARCH model (1,1), It has been observed that the gamma parameter for both normal and T-student distributions are 0.4 and 0.3, respectively, which displays the presence of a positive leverage effect. By estimating the GARCH-M (1,1), we get GARCH-M (1,1) coefficients for both normal and T-student distributions, which are 0.5 and 0.2, respectively. This coefficient is the same as the risk margin, which indicates that the return is positively dependent on the volatility. The presence of this risk margin and its significance means the existence of a consistent correlation in past asset returns. The results of estimating two models APARCH (1,1), J GJRGARCH (1,1), which are other types of GARCH models used to model leverage effects, show that in GJRGARCH model the gamma coefficients for both normal and T-student distributions are -0.1 and -0.07, respectively. These coefficients indicate the presence of a negative leverage effect. Moreover, In the APARCH model the gamma coefficients for both normal and T-student distributions are -0.2 and -0.1, respectively, which proves the attendance of a negative leverage effect.<br /> All in all, by applying the different Garch models for VaR and CVaR, the highest values for the TSE index Tehran at two confidence levels of 95% and 99% belong to the GARCH-M model (1,1) with the T-student distribution. The obtained values are 0.4850% and 0.8619%, respectively. In addition, for the normal distribution at the 95% confidence level, the GARCH-M, EGARCH, GJRGARCH, APARCH and GARCH models show the highest risk values, respectively. However, for the T-student distribution, GARCH-M, APARCH, EGARCH, GJRGARCH, and GARCH models present the highest risk values, respectively.<br /> Furthermore, for the normal distribution at the 99% confidence level, GARCH-M, EGARCH, APARCH, GJRGARCH, and GARCH models show the highest risk values, respectively, and for the T-student distribution, GARCH-M, APARCH, EGARCH, GJRGARCH and GARCH models have the highest risk value, respectively. At two levels of 95% and 99% confidence, the highest value of conditional risk is related to the GARCH-M model (1,1) with T-student distribution and with rates 0.6492% and 2863%, respectively. Considering the normal distribution at both 95% and 99% confidence level, GARCH-M, EGARCH, APARCH, GJRGARCH and GARCH models show the highest CVaR, respectively. However, assuming T-student distribution for both levels of 95% and 99% confidence results that the GARCH-M, APARCH, EGARCH, GJRGARCH and GARCH patterns have the highest conditional risk value, respectively.<br /> The accuracy and adequacy of different models were evaluated by back testing methods. The results indicate that for the calculated VaR at 95% confidence level, the null hypothesis is rejected for the normal distribution. This means that none of the patterns with normal distribution have sufficient validity to calculate VaR. On the other hand, the null hypothesis for all models with the T-Student distribution is not rejected at the 95% level of confidency. and all the models with the T-Student distribution are valid to calculate VaR. At the 99% confidence level for both normal and T-student distributions, the results show that based on the corresponding ps of Kopic and Christofferson tests, all patterns have the necessary validity and adequacy to calculate VaR, and the null hypothesis is not rejected.<br /> <br /><strong>CONCLUSION </strong><br />The results propose the T-Student distribution as a favorable probability distribution for estimating Value at Risk and Conditional Value at Risk through the five introduced GARCH models. Moreover, there is no significant difference among the averages of Value at Risk and Conditional Value at Risk which are estimated by different GARCH patterns. The findings of this research aim to help better investment in the stock market. Therefore, investors and agents can also benefit from the applied methods in this research to calculate the daily risk value for different industries in TSE.<br /> For future research, it is recommended to use very recent GARCH models such as 3D-GARCH, COGARCH, GARCH-MIDDAS, etc. Furthermore, according to the significant effect of probability distribution selection, it is suggested to check other probability distributions. In the present paper, the adequacy and accuracy of different models have been evaluated. However, due to the possibility of VaR overestimation by a model which results in the loss of resource allocation in a bank or company, ... , it is suggested to rank different methods for value at risk by using different loss function methods.https://jqe.scu.ac.ir/article_17240_0e01b0ed7b04c99e693f1fd3ac734e49.pdfShahid Chamran University of AhvazQuarterly Journal of Quantitative Economics2008-585019420230220The Impact of Carbon Taxes and Fossil Fuels Subsidies on the Development of Renewable Energy in Selected OECD CountriesThe Impact of Carbon Taxes and Fossil Fuels Subsidies on the Development of Renewable Energy in Selected OECD Countries791091657910.22055/jqe.2021.33321.2243FASajjad Faraji DizajiAssociate Professor of Economics, Department of Economics, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.0000-0001-8413-4580Mohammadreza ArefianPh.D Student in Economics, Department of Economics, Faculty of Management and Economics , Tarbiat Modares University, Tehran, Iran.0000-0001-8015-8833Abbas Assari AraniAssociate Professor of Economics, Department of Economics, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.0000-0002-4995-867XJournal Article20200420<strong>EXTENDED ABSTRACT </strong>
<strong>INTRODUCTION </strong>
Fossil fuels and the process of their exploitation and consumption have led to serious challenges and concerns in the field of energy and environmental issues. These challenges include; the security of supplying these fuels, the depletion of its resources and most importantly their impact on environmental pollutants and global warming. Therefore, providing practical solutions and adopting optimal policies in this field are necessary. The development of using renewable energies that are compatible with nature and the environment, producing less air pollution and are not terminate in the near future, can be the most important option to resolve this crisis. There are different tools to encourage replacing the fossil fuels with renewable energies. One of the most effective of which is to impose carbon taxes on polluting units that use these fuels. Furthermore, limiting the support and subsidies in the fossil fuel sector is another driver of this replacement. The present study is an attempt to investigate the existing relationships between the effects of these two tools among other variables on the development of renewable energy.
<strong>METHODOLOGY </strong>
The main purpose of this research is to investigate the applied policies and solutions and their results in order to develop renewable energy consumption, reduce environmental pollution and move towards sustainable development. The period of this study is from 2004 to 2014 and tit covers the selected countries of the Organization for Economic Co-operation and Development (OECD) which are the leaders in the field of environmental taxation and have higher ratio of environmental taxes to GDP compared to other OECD countries. The method used to analyze the model is the vector autoregression with panel data, which captures the interactive behavior and interactions among the variables. The two important applies tools of this model are impulse response functions and analysis of variance decomposition. The GMM estimator is also used to estimate the model.
<strong>FINDINGS </strong>
The findings show that carbon tax has a positive and significant effect on the development of renewable energy and the impact of carbon tax shock on renewable energy consumption is positive among the sample countries. Thus, the increase in carbon tax has had an immediate and increasing effect on the consumption of renewable energy. Also, imposing a carbon tax which makes negative impact on fossil fuel consumption by reducing carbon emissions, has positive environmental effects. On the other hand, the impact of fossil fuel subsidies on renewable energy is negative. Increasing fossil fuel subsidies and the shock caused by it will immediately reduce the consumption of renewable energy. In contrast to the carbon tax variable, which increases the cost of fossil fuel consumption, the fossil fuel subsidies reduce the price and increase the consumption of fossil fuels. Therefore, carbon tax and fossil fuel subsidies play important roles in replacing the fossil fuels with renewable energies.
<strong>CONCLUSION </strong>
According to the estimates made in this research, the shock created by the carbon tax on the consumption of renewable energy in the studied countries was positive, so that the increase in the carbon tax had an immediate and increasing effect on the consumption of renewable energy. Carbon tax has a negative and significant impact on oil consumption as a representative of fossil fuels, because the shock caused by carbon tax causes the consumption of fossil fuels to decrease due to the increase in the cost of its use and by creating The replacement relationship with the consumption of renewable energy is effective both in reducing the consumption of fossil fuels and in the emission of carbon dioxide gas. Due to the creation of an income gap and due to the dependence of industries on fossil fuels, this shock causes a decrease in the GDP in the initial stages, but after a few periods, this effect increases and eventually causes an increase in the GDP. In contrast, subsidizing fossil fuels to consumers can create dependence on fossil fuels and prevent consumers from switching to clean energy sources.
<strong>EXTENDED ABSTRACT </strong>
<strong>INTRODUCTION </strong>
Fossil fuels and the process of their exploitation and consumption have led to serious challenges and concerns in the field of energy and environmental issues. These challenges include; the security of supplying these fuels, the depletion of its resources and most importantly their impact on environmental pollutants and global warming. Therefore, providing practical solutions and adopting optimal policies in this field are necessary. The development of using renewable energies that are compatible with nature and the environment, producing less air pollution and are not terminate in the near future, can be the most important option to resolve this crisis. There are different tools to encourage replacing the fossil fuels with renewable energies. One of the most effective of which is to impose carbon taxes on polluting units that use these fuels. Furthermore, limiting the support and subsidies in the fossil fuel sector is another driver of this replacement. The present study is an attempt to investigate the existing relationships between the effects of these two tools among other variables on the development of renewable energy.
<strong>METHODOLOGY </strong>
The main purpose of this research is to investigate the applied policies and solutions and their results in order to develop renewable energy consumption, reduce environmental pollution and move towards sustainable development. The period of this study is from 2004 to 2014 and tit covers the selected countries of the Organization for Economic Co-operation and Development (OECD) which are the leaders in the field of environmental taxation and have higher ratio of environmental taxes to GDP compared to other OECD countries. The method used to analyze the model is the vector autoregression with panel data, which captures the interactive behavior and interactions among the variables. The two important applies tools of this model are impulse response functions and analysis of variance decomposition. The GMM estimator is also used to estimate the model.
<strong>FINDINGS </strong>
The findings show that carbon tax has a positive and significant effect on the development of renewable energy and the impact of carbon tax shock on renewable energy consumption is positive among the sample countries. Thus, the increase in carbon tax has had an immediate and increasing effect on the consumption of renewable energy. Also, imposing a carbon tax which makes negative impact on fossil fuel consumption by reducing carbon emissions, has positive environmental effects. On the other hand, the impact of fossil fuel subsidies on renewable energy is negative. Increasing fossil fuel subsidies and the shock caused by it will immediately reduce the consumption of renewable energy. In contrast to the carbon tax variable, which increases the cost of fossil fuel consumption, the fossil fuel subsidies reduce the price and increase the consumption of fossil fuels. Therefore, carbon tax and fossil fuel subsidies play important roles in replacing the fossil fuels with renewable energies.
<strong>CONCLUSION </strong>
According to the estimates made in this research, the shock created by the carbon tax on the consumption of renewable energy in the studied countries was positive, so that the increase in the carbon tax had an immediate and increasing effect on the consumption of renewable energy. Carbon tax has a negative and significant impact on oil consumption as a representative of fossil fuels, because the shock caused by carbon tax causes the consumption of fossil fuels to decrease due to the increase in the cost of its use and by creating The replacement relationship with the consumption of renewable energy is effective both in reducing the consumption of fossil fuels and in the emission of carbon dioxide gas. Due to the creation of an income gap and due to the dependence of industries on fossil fuels, this shock causes a decrease in the GDP in the initial stages, but after a few periods, this effect increases and eventually causes an increase in the GDP. In contrast, subsidizing fossil fuels to consumers can create dependence on fossil fuels and prevent consumers from switching to clean energy sources.
https://jqe.scu.ac.ir/article_16579_f512203cca992969c1b250f7a728ffe6.pdfShahid Chamran University of AhvazQuarterly Journal of Quantitative Economics2008-585019420230220Applying Bayesian modification with Doan, Litterman and Sims prior (DLS) in the autoregressive distributed lags model (BARDL): A case study of the short-term and long-term impact of banking, insurance and financial intermediation on capital markets in IranApplying Bayesian modification with Doan, Litterman and Sims prior (DLS) in the autoregressive distributed lags model (BARDL): A case study of the short-term and long-term impact of banking, insurance and financial intermediation on capital markets in Iran1111451669710.22055/jqe.2021.34566.2268FAMahdi Ghaemi AslAssistant Professor, Faculty of Economics, Kharazmi University, Tehran, Iran0000-0002-2246-2914Seyyed Mahdi MostafaviAssistant Professor, Faculty of Administrative and Economic Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.Mohammad BonyadiM.A. of Islamic Economics, Economics Faculty, Kharazmi University, Tehran, Iran M.A. of Islamic Economics, Economics Faculty, Kharazmi University, Tehran, Iran.Journal Article20200806<strong>EXTENDED ABSTRACT</strong><br /><strong>INTRODUCTION </strong><br />Efficient financial institutions and markets could increase economic growth, and mutually, the financial sector shold also reflect the economic indicators changes in real sector. Given the sub-sectors of financial markets and the importance of each in the economy, the two financial structures "bank-based" and "market- based" are generally visible in countries with increasing financial development and depth. In Iran, which has mainly a bank-based financial system, banks play a key role in establishing a link between the capital market, the money market and insurance market. Creating new capacity in the real sector of the economy is one of the tasks and characteristics of financial market sub-sectors. Moreover, the precondition of this role-taking procedure is to establish organizational connection and cohesion between financial market sub-sectors.<br />In this study, impact of the banking, insurance and financial intermediation sector with an emphasis on value-added of financial and monetary institutions services on the capital market is examined. In fact, the main question is whether the creation of new added value in banking, insurance and financial intermediation services can affect the main indicators of the capital market? The presence or absence of this effect in the short-term and long-term can have important implications for money market and capital market policymakers. For this purpose, “TEPIX” and “financial index” as capital markets representative indices (the dependent variable) and Bayesian ARDL (BARDL) method based on Doan, Litterman and Sims prior and Bušs (2010) is used in period of 1991-2020.<br /> <br /><strong>METHODOLOGY </strong><br />In a Bayesian model, prior density is used in order to use the information contained in the distribution of observations and previously available information. The prior density is a tool for reflecting all the information that the researcher has in mind from observing the data. Therefore, the prior densities can be very important to be able to accurately reflect the distributive properties of the sample used within the framework of Bayesian analysis. When the prior function is combined with the likelihood function, a posterior density is obtained that embodies the nature and properties of the prior function, indicating the special importance of the prior functions in Bayesian analysis. These features mean that the prior density information provides researchers with similar interpretations to the interpretations derived from the likelihood function information. In other words, the interpretations of the prior density function of real data will be the same as the interpretations of the prior density function of the new data. We specify an ARDL model as follows:<br />The prior function will be written as:<br /><br />Then will be a diagonal matrix:<br /><br /><strong>FINDINGS </strong><br />Results of modeling research data in the framework of a Bayesian model, show that monetary and financial institutions services in the short term could affect stock price index (“TEPIX” and “financial index”), therefore The short-term relationship between the banking, insurance and financial intermediation sector of economy and the financial sector (Stock Exchange market) is established but the statistical significance of this relationship in the long run is not approved and no feedback in stock price indices based on the changes in the banking, insurance and financial intermediation sector is observed.<br /> <br /><strong>CONCLUSION </strong><br />These results on one hand indicate a significant impact of monetary variables and tools such as liquidity and price inflation on the stock market, and on the other hand is a sign of weakness in the relationship between the banking, insurance and financial intermediation sector and the stock market. Therefore, it is suggested that in critical situations (with short-term targets), monetary and price tools used to adjust stock market but in contrast, by correction of structural flaws of Stock Exchange market, the context of short term and long-term impact of the banking, insurance and financial intermediation sector on stock indices will be provided.<strong>EXTENDED ABSTRACT</strong><br /><strong>INTRODUCTION </strong><br />Efficient financial institutions and markets could increase economic growth, and mutually, the financial sector shold also reflect the economic indicators changes in real sector. Given the sub-sectors of financial markets and the importance of each in the economy, the two financial structures "bank-based" and "market- based" are generally visible in countries with increasing financial development and depth. In Iran, which has mainly a bank-based financial system, banks play a key role in establishing a link between the capital market, the money market and insurance market. Creating new capacity in the real sector of the economy is one of the tasks and characteristics of financial market sub-sectors. Moreover, the precondition of this role-taking procedure is to establish organizational connection and cohesion between financial market sub-sectors.<br />In this study, impact of the banking, insurance and financial intermediation sector with an emphasis on value-added of financial and monetary institutions services on the capital market is examined. In fact, the main question is whether the creation of new added value in banking, insurance and financial intermediation services can affect the main indicators of the capital market? The presence or absence of this effect in the short-term and long-term can have important implications for money market and capital market policymakers. For this purpose, “TEPIX” and “financial index” as capital markets representative indices (the dependent variable) and Bayesian ARDL (BARDL) method based on Doan, Litterman and Sims prior and Bušs (2010) is used in period of 1991-2020.<br /> <br /><strong>METHODOLOGY </strong><br />In a Bayesian model, prior density is used in order to use the information contained in the distribution of observations and previously available information. The prior density is a tool for reflecting all the information that the researcher has in mind from observing the data. Therefore, the prior densities can be very important to be able to accurately reflect the distributive properties of the sample used within the framework of Bayesian analysis. When the prior function is combined with the likelihood function, a posterior density is obtained that embodies the nature and properties of the prior function, indicating the special importance of the prior functions in Bayesian analysis. These features mean that the prior density information provides researchers with similar interpretations to the interpretations derived from the likelihood function information. In other words, the interpretations of the prior density function of real data will be the same as the interpretations of the prior density function of the new data. We specify an ARDL model as follows:<br />The prior function will be written as:<br /><br />Then will be a diagonal matrix:<br /><br /><strong>FINDINGS </strong><br />Results of modeling research data in the framework of a Bayesian model, show that monetary and financial institutions services in the short term could affect stock price index (“TEPIX” and “financial index”), therefore The short-term relationship between the banking, insurance and financial intermediation sector of economy and the financial sector (Stock Exchange market) is established but the statistical significance of this relationship in the long run is not approved and no feedback in stock price indices based on the changes in the banking, insurance and financial intermediation sector is observed.<br /> <br /><strong>CONCLUSION </strong><br />These results on one hand indicate a significant impact of monetary variables and tools such as liquidity and price inflation on the stock market, and on the other hand is a sign of weakness in the relationship between the banking, insurance and financial intermediation sector and the stock market. Therefore, it is suggested that in critical situations (with short-term targets), monetary and price tools used to adjust stock market but in contrast, by correction of structural flaws of Stock Exchange market, the context of short term and long-term impact of the banking, insurance and financial intermediation sector on stock indices will be provided.https://jqe.scu.ac.ir/article_16697_aacba7f903898b5d393595ea4c722a3b.pdfShahid Chamran University of AhvazQuarterly Journal of Quantitative Economics2008-585019420230220Simulation of Nordhaus Model (1975) in the Economy of Iran: Optimal Control ApproachSimulation of Nordhaus Model (1975) in the Economy of Iran: Optimal Control Approach1471761640310.22055/jqe.2021.34975.2276FAYaser Balaghi InaloPh.D.student of International Economics, Department of Economics, Faculty of Management & Economics, Shahid Bahonar university, Kerman, Iran.Sayyed Abdolmajid Jalaee EsfandabadiProfessor of Economics, Department of Economics, Faculty of Management & Economics, Shahid Bahonar university, Kerman, Iran.0000-0001-8154-9123Journal Article20200910<strong>EXTENDED ABSTRACT </strong>
<strong>INTRODUCTION </strong>
The effect of economy on politics and the effect of politics on economy has always been considered by economists and politicians. A significant question in this area is related to the effects of elections on economy and the effect of the current economy status on individual choices. According to Nordhaus model, governments can provide political developments, especially victory in elections using economic policies, leading to economic instability. The empowered party selects economic policies during its tenure of office to maximize its popularity on the eve of the next election. Voters are influenced by the performance of macroeconomic variables, persuading officials to manipulate economic variables so that they can achieve desirable outcomes on the eve of election. Inflation and unemployment are among the most critical economic variables considered by voters and observed in their utility function.
Unemployment is considered as a destructive social, economic, and cultural phenomenon and its elimination has always been one of the main concerns of planners. In addition, unemployment has negative and adverse consequences in different fields. Unemployment is considered as a highly significant economic phenomenon for governments which always attempt to solve the socio-economic problem of unemployment. Furthermore, inflation, as one of the main variables, plays a considerable role in economic performance at the macroeconomic level. In general, unemployment and inflation are two target variables in economic policies and policymakers often have to sacrifice one of them to achieve the other one. Inflation and unemployment variables are more sensitive to electoral candidates and voters compared to other variables.
<strong>METHODOLOGY </strong>
This study simulated the optimal path of inflation and unemployment based on Nordhaus model in GAMS software for eight years during 1981-2029 and compared to their actual values during 1981-2020. Comparing the simulated values and actual values of inflation and unemployment, the strengths and weaknesses of different governments, as well as their opportunism rate are identified and can be used as a policy recommendation.
<strong>FINDINGS </strong>
The results of this study indicated that governments have often attempted to increase voter satisfaction and the possibility of their re-election by adopting different economic policies. In addition, the average inflation during the eight-year periods have been mostly lower in the first half than the second half. Further, it can be regarded as evidence of Nordhaus model, although proving this claim requires further investigation in the economic policies of different governments and may not be intentional.
<strong>CONCLUSION </strong>
In order to separate from this opportunism by governments, achieve ideal inflation and unemployment, and increase their likelihood of re-election, it is necessary to accurately identify the parameters affecting the Phillips curve and having information about the type of policy, implementation, policy size, and effectiveness of policy. Selecting the policy type can only be a necessary condition while implementation, policy size, and the effectiveness of policy are considered as sufficient conditions.
<strong>EXTENDED ABSTRACT </strong>
<strong>INTRODUCTION </strong>
The effect of economy on politics and the effect of politics on economy has always been considered by economists and politicians. A significant question in this area is related to the effects of elections on economy and the effect of the current economy status on individual choices. According to Nordhaus model, governments can provide political developments, especially victory in elections using economic policies, leading to economic instability. The empowered party selects economic policies during its tenure of office to maximize its popularity on the eve of the next election. Voters are influenced by the performance of macroeconomic variables, persuading officials to manipulate economic variables so that they can achieve desirable outcomes on the eve of election. Inflation and unemployment are among the most critical economic variables considered by voters and observed in their utility function.
Unemployment is considered as a destructive social, economic, and cultural phenomenon and its elimination has always been one of the main concerns of planners. In addition, unemployment has negative and adverse consequences in different fields. Unemployment is considered as a highly significant economic phenomenon for governments which always attempt to solve the socio-economic problem of unemployment. Furthermore, inflation, as one of the main variables, plays a considerable role in economic performance at the macroeconomic level. In general, unemployment and inflation are two target variables in economic policies and policymakers often have to sacrifice one of them to achieve the other one. Inflation and unemployment variables are more sensitive to electoral candidates and voters compared to other variables.
<strong>METHODOLOGY </strong>
This study simulated the optimal path of inflation and unemployment based on Nordhaus model in GAMS software for eight years during 1981-2029 and compared to their actual values during 1981-2020. Comparing the simulated values and actual values of inflation and unemployment, the strengths and weaknesses of different governments, as well as their opportunism rate are identified and can be used as a policy recommendation.
<strong>FINDINGS </strong>
The results of this study indicated that governments have often attempted to increase voter satisfaction and the possibility of their re-election by adopting different economic policies. In addition, the average inflation during the eight-year periods have been mostly lower in the first half than the second half. Further, it can be regarded as evidence of Nordhaus model, although proving this claim requires further investigation in the economic policies of different governments and may not be intentional.
<strong>CONCLUSION </strong>
In order to separate from this opportunism by governments, achieve ideal inflation and unemployment, and increase their likelihood of re-election, it is necessary to accurately identify the parameters affecting the Phillips curve and having information about the type of policy, implementation, policy size, and effectiveness of policy. Selecting the policy type can only be a necessary condition while implementation, policy size, and the effectiveness of policy are considered as sufficient conditions.
https://jqe.scu.ac.ir/article_16403_72136dfb503bdded57374be24e7d8246.pdfShahid Chamran University of AhvazQuarterly Journal of Quantitative Economics2008-585019420230220EEstimation of Capital Mobility by Focusing on Home Bias Based on the Comparative Role of Trade Openness and Kof Index in a Selection of Oil-Exporting CountriesEEstimation of Capital Mobility by Focusing on Home Bias Based on the Comparative Role of Trade Openness and Kof Index in a Selection of Oil-Exporting Countries1772041757510.22055/jqe.2022.38617.2416FAMaryam MehraraPh.D candidate of Economics, Department of Economics, Faculty of Management and Social Science,Tehran North Branch, Islamic Azad University, Tehran, Iran.0000-0001-8151-3462Amir GholamiAssistant Professor of Economics, Department of Economics, Faculty of Management and Social Science,Tehran North Branch, Islamic Azad University, Tehran, Iran0000-0002-0815-9791Seyed Mohammad Mehdi AhmadiAssistant Professor of Economics, Department of Economics, Faculty of Management and Social Science,Tehran North Branch, Islamic Azad University, Tehran, Iran.Journal Article20210922<strong>EXTENDED ABSTRACT </strong>
<strong>INTRODUCTION </strong>
The degree of international capital mobility is a decisive and vital factor in the economic, political, and social life of countries and low capital mobility is considered as a concern for them. Capital mobility among countries has always been the focus of many policymakers and observers of the international economy. The main issue in this research is to address a new approach in measuring the degree of capital mobility and solving the Feldstein-Horioka (FH) puzzle in the studied countries. The main purpose of this study is to investigate the status of international capital mobility between 10 developing oil-exporting countries in the period 2000-2018 using the dynamic panel data technique. Theoretically in an open economy, saving and investment are more affected by capital flows and global interest rates. Hence, the relationship between these two variables is not expected to be strong in an economy that is open to capital flows. This analysis contradicts the results of the FH study. Their experimental findings in 1980, using cross-sectional analysis, showed that the correlation between savings and investment for the 16 OECD countries during the period 1960-1974 was close to one (between 0.85 and 0.95). They interpreted the value of this coefficient as the reason for the low mobility of capital. This finding was contrary to the expectations of capital mobility in these countries, because, in fact, the degree of integration of these countries in international capital markets has been high. This became known as the FH puzzle and became the source of discussions about the degree of financial integration and the degree of trade openness in the industrialized world. The results of research in many cases indicate that experimental models that do not take into account the degree of financial openness and economic globalization, lead to an upward bias in the savings coefficient. Therefore, our empirical approach includes adding the variables of the degree of trade openness and the Kof index as indicators of traditional and modern globalization, respectively, as well as the interactive effect of the Kof index on the original equation, which has been used for the first time in domestic and foreign studies. Also, for the first time, by adding new variables on the initial form of the FH equation, a new specification of the initial equation for solving the puzzle in internal studies has been investigated. Given the characteristics of the countries under study that have sufficient financial resources to finance and do not need external resources, the study of international capital mobility for them can be important because it clarifies the role of home bias in accurately estimating international capital flows.The results indicate the elimination of home bias in estimating the relationship between saving and investment and the realization of the relationship between the two variables.
<strong>METHODOLOGY </strong>
After performing the unit root test on the model variables, through the Leimer test, the H0 hypothesis was rejected for the pooled data model, so we used cross-sectional analysis to estimate panel data for 10 developing oil-exporting countries (Iran, UAE, Oman, Saudi Arabia, Azerbaijan, Ecuador, Kazakhstan, Indonesia, Egypt, and Sudan). Then we used two cointegration techniques called the Pedroni panel-data cointegration test and the Kao panel-data cointegration test to detect the existence of a long-term relationship between variables. Finally, two fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimators were used to estimate the long-run equilibrium parameters in estimating the models. All research variables except the Kof index are extracted from World Bank (WDI) database. KOF Globalization Index statistics are taken from the 2021 time series on the website of the Swiss Economic Institute.
<strong>FINDINGS </strong>
As expected, the Kof variable has a significant positive coefficient in determining the investment ratio. The degree of openness has not helped to explain the investment ratio. The interaction effect of the Kof index with the savings ratio has a significant negative coefficient. The savings ratio variable with a significant (close to one) positive and significant coefficient indicates the importance of the savings ratio coefficient in determining the investment ratio. Under these conditions, domestic investment is made only through domestic savings.
<strong>CONCLUSION </strong>
The inclusion of the Kof index, the degree of trade openness, and the interaction of the Kof index in the original FH equation eliminated home bias and made the value of the savings ratio coefficients a reality. Therefore, the very low degree of capital mobility among the studied countries can be conclusively concluded. In general, our main conclusion is that there is no evidence of the confirmation of the FH conundrum for the selected study countries. The degree of openness of trade as a factor reducing trade friction did not play a role in determining the investment ratio and reducing home bias, while increasing the Kof index as a factor reducing trade and financial friction played a decisive role in determining the investment ratio. The changes made to the FH equation play an important role in solving this important puzzle of the international economy. The degree of trade openness is not the best representative for reducing trade friction. Therefore, the results of our research confirm the introduction of the Kof index as an advanced and modern version of the degree of trade openness for studies on the FH puzzle.<strong>EXTENDED ABSTRACT </strong>
<strong>INTRODUCTION </strong>
The degree of international capital mobility is a decisive and vital factor in the economic, political, and social life of countries and low capital mobility is considered as a concern for them. Capital mobility among countries has always been the focus of many policymakers and observers of the international economy. The main issue in this research is to address a new approach in measuring the degree of capital mobility and solving the Feldstein-Horioka (FH) puzzle in the studied countries. The main purpose of this study is to investigate the status of international capital mobility between 10 developing oil-exporting countries in the period 2000-2018 using the dynamic panel data technique. Theoretically in an open economy, saving and investment are more affected by capital flows and global interest rates. Hence, the relationship between these two variables is not expected to be strong in an economy that is open to capital flows. This analysis contradicts the results of the FH study. Their experimental findings in 1980, using cross-sectional analysis, showed that the correlation between savings and investment for the 16 OECD countries during the period 1960-1974 was close to one (between 0.85 and 0.95). They interpreted the value of this coefficient as the reason for the low mobility of capital. This finding was contrary to the expectations of capital mobility in these countries, because, in fact, the degree of integration of these countries in international capital markets has been high. This became known as the FH puzzle and became the source of discussions about the degree of financial integration and the degree of trade openness in the industrialized world. The results of research in many cases indicate that experimental models that do not take into account the degree of financial openness and economic globalization, lead to an upward bias in the savings coefficient. Therefore, our empirical approach includes adding the variables of the degree of trade openness and the Kof index as indicators of traditional and modern globalization, respectively, as well as the interactive effect of the Kof index on the original equation, which has been used for the first time in domestic and foreign studies. Also, for the first time, by adding new variables on the initial form of the FH equation, a new specification of the initial equation for solving the puzzle in internal studies has been investigated. Given the characteristics of the countries under study that have sufficient financial resources to finance and do not need external resources, the study of international capital mobility for them can be important because it clarifies the role of home bias in accurately estimating international capital flows.The results indicate the elimination of home bias in estimating the relationship between saving and investment and the realization of the relationship between the two variables.
<strong>METHODOLOGY </strong>
After performing the unit root test on the model variables, through the Leimer test, the H0 hypothesis was rejected for the pooled data model, so we used cross-sectional analysis to estimate panel data for 10 developing oil-exporting countries (Iran, UAE, Oman, Saudi Arabia, Azerbaijan, Ecuador, Kazakhstan, Indonesia, Egypt, and Sudan). Then we used two cointegration techniques called the Pedroni panel-data cointegration test and the Kao panel-data cointegration test to detect the existence of a long-term relationship between variables. Finally, two fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimators were used to estimate the long-run equilibrium parameters in estimating the models. All research variables except the Kof index are extracted from World Bank (WDI) database. KOF Globalization Index statistics are taken from the 2021 time series on the website of the Swiss Economic Institute.
<strong>FINDINGS </strong>
As expected, the Kof variable has a significant positive coefficient in determining the investment ratio. The degree of openness has not helped to explain the investment ratio. The interaction effect of the Kof index with the savings ratio has a significant negative coefficient. The savings ratio variable with a significant (close to one) positive and significant coefficient indicates the importance of the savings ratio coefficient in determining the investment ratio. Under these conditions, domestic investment is made only through domestic savings.
<strong>CONCLUSION </strong>
The inclusion of the Kof index, the degree of trade openness, and the interaction of the Kof index in the original FH equation eliminated home bias and made the value of the savings ratio coefficients a reality. Therefore, the very low degree of capital mobility among the studied countries can be conclusively concluded. In general, our main conclusion is that there is no evidence of the confirmation of the FH conundrum for the selected study countries. The degree of openness of trade as a factor reducing trade friction did not play a role in determining the investment ratio and reducing home bias, while increasing the Kof index as a factor reducing trade and financial friction played a decisive role in determining the investment ratio. The changes made to the FH equation play an important role in solving this important puzzle of the international economy. The degree of trade openness is not the best representative for reducing trade friction. Therefore, the results of our research confirm the introduction of the Kof index as an advanced and modern version of the degree of trade openness for studies on the FH puzzle.https://jqe.scu.ac.ir/article_17575_a088aea1071964cdd324faf0acd7ca74.pdf