فصلنامه مطالعات اقتصاد انرژی
پیاپی 65 (تابستان 1399)

In this paper we study the effect of volatility in Brent oil prices on the important indices of financial markets in Iran, as well as the return on gold, from 2008 to 2018 using the Multivariate Exponential GARCH Model (MVEGARCH). We also use the ADCC-FIGARCH model to examine the asymmetric dynamic conditional correlation between Brent oil prices and financial markets in Iran. The results of this study indicate significant volatility spillovers and dynamic correlation between Brent oil volatility and financial markets and oil and petrochemical industries in Iran. In addition, portfolio diversification is effective in reducing risk, and the optimal asset weight and hedging are entirely dependent on financial market conditions. The experimental results of this study reveal that adding the Brent index to the model increases portfolio yields and decreases their risk.  We find the addition of Brent prices to be most helpful in risk management during periods of financial crisis. In addition, the dynamic correlation coefficient between Brent oil and gold fluctuations is about 0.26 and this coefficient for Brent oil and Tehran stock exchange index is about 0.08.

In this study, we model the long-term and dynamic relationships between spot oil and exchange rates  and gas prices by applying the Markov switching vector self-regression model in three regional gas markets in USA, Europe and Asia. Price behavior is analyzed using Bayesian estimation to take into account the transition from an existing relationship and the delayed and recurring effects of price changes. We then apply shocks in sample periods that contain critical historical events that lead to the breakdown of price structure and regime switches to study price formation in each regional market and the mechanism of transfer between price regimes. According to the findings, although exchange rate volatility and variance has increased in recent periods, the effect of exchange rate shocks on spot prices in regional gas and oil markets has decreased, and price responses are less sensitive to inflation, compared to the past.  Finally, we take into account the nature of pricing in each market to study the relative stability of each market with respect to exchange rate changes.  

This study investigates the relationship between renewable energy consumption, agricultural value added and per capita GDP with CO2 emissions in selected countries of MENA region. The study estimates the values of cointegration amongst the selected varaiables  using panel data from selected countries for the period 1990 to 2014. The results indicated a long-run relationship between these variables. Estimation of long-term relationships showed that consumption of renewable energies and agricultural value added had a significant negative effect on CO2 emissions and per capita GDP had a positive and significant effect. This indicates the usefulness of investments in renewable energies. In the short run it is estimated that agricultural value added  has no significant effect on emissions, while renewable energy consumption and per capita GDP have significant effects. Given the significant short-run adjustment coefficient of these two variables, ti will take approximately four years for an accidental shock to be corrected. Therefore, it is recommended that economic activities that use fossil fuels be subject to the maximum permissible limits of pollution emission, in order to protect the environment.

The paper examines the issue of hedging in energy markets. The objective of this study is to select an optimal model that will provide the highest price risk reduction for the selected commodities. We apply the ordinary least squares methods, autoregressive model, autoregressive conditional heteroscedasticity and copula to calculate the appropriate dynamic minimum-variance hedge ratio. The objects of hedging are the spot and futures prices. We use weekly data for the period 2013 to 2018 to estimate our values. Empirical results show that the Copula model is the most effective method for hedging against price risks‏.

This research investigates the main factors that influence green marketing in Iran’s oil industry that can result in sustainable development, based on expertschr('39') opinions. The methodology of this research is grounded in theory and adopts a qualitative research approach.  We use semi structural deep interviews to collect our data.  We analyze the data using three stages of coding: open, axial and selective coding.Our results show that  we need to implement green marketing in the energy sector, as soon as possible, in order to protect people’s health and the environment.  The main factors that affect green marketing include: political, social and economic factors, existing conditions like organization and lack of technical knowledge, energy efficiency, core energy policies, technology, human resources, investment, and culture. We conclude by stating that sustainable development can be promoted through implementing green marketing in the oil industry.

The volatility of oil revenues in rentier countries, due to the large share of oil revenue in their economies, affects many variables. Oil revenues can also affect social behavior and culture. Therefore, social capital, as the result of social networks and institutions, can be directly and indirectly affected by oil rents. The review of economic literature suggests that oil revenues could potentially be one of the determinants of social capital in oil-exporting countries. This study seeks to empirically investigate the relationship between oil revenues and social capital in oil-exporting countries using an unbalanced panel data and Leamer sensitivity analysis for the period 1990-2015. In this regard, we estimate 60 regressions according to the extreme bounds analysis. Our study confirms the hypothesis of significant effect of oil revenues on the social capital of oil exporting countries, though this relationship was found to be sensitive to changes in specification and value of the social capital index.  Our results indicate that an increase in oil revenues strongly reduces social capital in rentier countries.

Issues related to environment are one of the most important issues that confront communities in recent decades.   Resource scarcity and the necessity of development for Iran and other similar countries requires us to deal with carbon dioxide emissions as a manifestation of environmental crisis. In this study, we assess the relationship between carbon dioxide emissions as a dependent variable with population, gross domestic product, electrical energy productivity, electrical energy structure, and household consumption as independent variables, using panel data.  We first develop a model with a spatial lag of both the explanatory and dependent variables, which is called the Spatial Durbin Model (SDM). In this study, we modeled the  carbon dioxide  flow over the period 2005 to 2014 for thermal power plants using a dynamic spatial regression model and a spatial Hausmann test. This test highlights the strong preference for the fixed effect model.  We modified the Wald test for group wise hetero skedasticity in the fixed effect regression model and the Durbin-Watson test for the residual autocorrelation detection results.   Effects of the Spatial Durbin Models (SDM) confirm the assumption of the effectiveness of spatial effects. We estimate the elasticity of the overflow effects through positive and significant proximity.  Our research findings indicate that an increase in  gross domestic product of adjacent provinces increases carbon dioxide emissions the concerned province and neighboring provinces. We also observe that increased electrical energy productivity, electrical energy structure and increased share of electricity consumption by households reduces carbon dioxide emissions in the concerned province and neighboring provinces.

Investment in renewable energies especially solar energies encounters numerous uncertainties considering the increased dynamism in economic and financial conditions. It is thus important to use modern methods of economic appraisal for such investments. Our study provides a framework for use of the real option theory for economic appraisal of a 2MW photovoltaic plant in Isfahan province (as a case study) in comparison with traditional methods. We first distribute self-made questionnaires to a sample of 36 experts, to test the impact of different  options on project value with the help of statistical methods.  We used Cronbachchr('39')s alpha coefficient to test the validity of our 9 item questionnaire. Our results indicated a significant relationship between the use of real option and increased value of investment in this plant considering expansion, termination and deferral options. We compared the results of our options between use of traditional methods and real option approach with the help of Fuzzy Binomial lattice. We observed that the fuzzy real option approach leads to increased value of investment and offers greater flexibility than traditional methods.