ENERGY ECONOMICS REVIEW
Year:2020 | Volume:15 | Issue:63|Papers Count:8

Upstream and downstream activities of the oil industry have to deal with mitigating risks of material and human loss associated with the use of industry assets, including through insurance cover. One of the important issues in insuring oil assets, is determination the value at risk of the asset in question. The main purpose of the present study is to fill the gap in terms of a scientific methodology for estimating the monetary value at risk (MVR) of a petroleum property and thus help in setting the correct insurance premium for any given oil asset. We use our method to calculate the optimal value at risk of an oil asset insured by Iranian insurance companies. Our estimated result is close to the actual premium charged. It thus offers us a scientific method, with high accuracy, which can be used to calculate risk-based valuation of oil assests and the associated insurance premium estimate.

Natural gas has an advantageous position compared to other energy carriers in policy making of energy consumption due to its many advantages, especially in terms of environmental indicators. In Iran, the industrial sector is considered as one of the important sectors of natural gas consumption. The main purpose of this paper is to investigate the relationship between natural gas prices and industries gas consumption during the period 1374-1393 in Iran. Natural gas consumption, price of electricity, price of natural gas and value added of industry were the variables of interest. By using a two-factor panel model, we estimated the natural gas price coefficients for various industries (according to the international standard industrial classification of all economic activities) separately. Empirical results show natural gas price coefficient is significant for all industries except the wood industry and wooden products. The coefficient of value added of industry is equal to 0. 18, which indicates one percent increase in value added of industry leads to eighteen hundredths percent increase in natural gas consumption. Also, the positive coefficient of electricity price shows that electricity is considered as an alternative energy for the industries.

This paper investigates the effects of oil price shocks on real GDP and inflation in selected oil exporting and importing countries using the Global VAR approach. We have used the data for 47 countries over the 38 years from 1979 to 2015 in this research. We have grouped the countries into 21 regions. Our results indicate that firstly that the impact of shocks on the real GDP of oil-exporting countries varies, depending on the volume of proven oil reserves and the ratio of reserves to oil production. For those oil-exporting countries that have a higher ratio of reserves to oil production, the effect of rising oil prices on their gross domestic product has been stronger, due to their greater ability to increase production in response to rising prices. Secondly, those oil-importing countries that have been major trading partners of oil-exporting countries are less affected by the shock of rising oil prices. Thirdly, the inflationary impact of rising oil prices has been relatively strong in oil-importing countries, but the persistence of these effects has varied depending on choice of monetary policy and flexibility of labor markets. On the other hand, inflationary impact of rising oil prices in oil-exporting countries has been negligible or even negative, possibly due to the appreciation of the exchange rates of these countries.

It is important for us to study the factors that impact the petrochemical industry, given its importance in nonoil exports in Iran. The petrochemical industry requires heavy investments and most countries can not directly fund all the required investment, hence the importance of foreign direct investment in the sector. Foreign direct investment also brings with it transfer of technology and employment. This paper assesses the responsiveness of foreign direct investment to the extent of openness of the economy and its impact on export of four selected products, using data for the period 1998 to 2017 and the SUR method. Our results indicate that foreign direct investment has a positive effect on petrochemical exports, with the impact most marked for Polyethylene. We also find that trade openness has a positive impact on foreign direct investment.

In this paper, we develop a dynamic system model to predict the effects of shale oil production on demand and supply of oil. We study the interaction between demand and supply of OPEC and non-OPEC member countries and supply of shale oil, oil price, growth of the global economy, development of new oil reserves and alternative energies. In our model, OPEC acts passively and covers the market demand deficit, while other conventional and unconventional oil producers operate in a competitive market. Shale oil production is estimated so as to maximize longterm profit and extraction of shale oil reservoirs. Oil price is a non-linear function of OPEC's spare capacity. The results show that oil demand will rise to 117 million barrels per day in 2030, with an average annual growth rate of 1. 4%. Oil supply from non-OPEC countries will grow at an average of 0. 9 percent per year to 64 million barrels. Shale oil supply will reach 19 million barrels per day, indicating an average annual growth of 10%. OPEC's oil supply will fluctuate around 34 million barrels a day. Oil prices will fluctuate between $ 20 to $ 130 per barrel.

Optimal management and planning in the petrochemical industry will bring about many economic benefits, including depended industries. In this research we examine technical and operational planning in the petrochemical supply chain network to assess how to optimize periodic decisions such as inventory of raw materials and products, pricing, transportation and flow of materials and products. In the proposed mode we take into account the uncertainty of various parameters, in order to propose a low risk and robust planning approach. The objective function of the proposed model is to maximize the annual profit of petrochemical complexes, within the capacity constraints of production and transportation facilities. The proposed model is continuous and convertible into a quadratic convex problem, in order to allow the use of commercial programs such as CPLEX to find the optimal solution. In order to demonstrate the applicability of the model, we present a numerical study in the final section of the paper, which includes some sensitivity analysis, numerical results and managerial insights.

This paper presents a differentiated approach for assessing the effect of oil price changes on gold price and the stock index, during upward and downward movements, using the Markov Switching Bayesian VAR model to analyze data for Iran over the period 2009 to 2016. We study the non-linear relationship between the price of oil and gold and the stock market index during periods of price decrease (called data set 1) and periods of price increase (data set 2). We use the Brock Dechert and Shinkman test and the likelihood ratio to prove that the relationship between the price of gold and the Tehran stock exchange index with oil price is nonlinear. We aim to examine the relationship between the prices of gold and the value of the Tehran stock exchange index, as two key financial assets, and oil prices during upward and downward price movements in a manner that leads to specific predictions. This research should allow investors to avoid losses due to decisions based on the thinking that the direction of causality between oil prices and asset prices remains the same during increasing and decreasing phases. The estimated values of our model suggest that during periods of declining oil prices gold and the stock exchange index both increase in value, while the impact on gold is greater than on the stock exchange index. We find that during periods of rising oil prices, gold prices also increase, while the value of the Tehran stock exchange index tends to be negative, though the rate of decrease slows down over time.

Accurate forecasting of annual gas consumption of the country plays an important role in energy supply strategies and policy making in this area. Markov chain grey regression model is considered to be a superior model for analyzing and forecasting annual gas consumption. This model Markov is a combination of the Markov chain and grey regression models. According to this model, the residual errors generated from the grey regression model are divided into a number of equal portions. We will add the calculated error terms to the values obtained through the grey regression in order to increase its accuracy. We use the box-bench design to calculate the optimal value of the error term which produces the most accurate forecasts when using the Markov chain grey regression model. The experimental study of the forecasting of natural gas consumption indicates that the proposed Markov chain grey regression model is more accurate than they grey or conventional Markov chain regression models.