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Natural gas infrastructure is growing and global LNG volumes are set to expand substantially. This results in more trade between different regions of the world and emergence of a more competitive and relatively more integrated global gas market. In addition, several key markets are currently undergoing structural reform with the aim of opening them to competition. In line with these changes in the global market, gas pricing methods also need to be adapted. This paper discusses the challenges of natural gas pricing and price review in this new market environment. Firstly, the current structure of the global and regional gas markets is analyzed. Secondly, challenges in natural gas pricing and price review are discussed, and in this context oil-indexation and hub-indexation are analyzed in detail. Thirdly, the recommended framework for pricing and price review in the more competitive global market are presented. The pricing mechanism and price review framework should be tailored to the characteristics of the gas market and the stages of growth and maturity of the market.

Optimal Portfolio Selection is one of the most important issues in the field of financial research. In the present study, we try to compare four various different models, which optimize three-objective portfolios using “Postmodern Portfolio Optimization Methods”, and then to solve them. These modeling approaches take into account both multidimensional nature of the portfolio selection problem and requirements imposed by investors. Concretely different models optimize the expected return, the down side risk, skewness and kurtosis given portfolio, taking into account budget, bounds and cardinality constrains. The quantification of uncertainty of the future returns on a given portfolio is approximated by means of LR-fuzzy numbers, while the moments of its returns are evaluated using possibility theory. In order to analyze the efficient portfolio, which optimize three criteria simultaneously, we build a new NSGAII algorithm, and then find the best portfolio with most Sortio ratio from the gained Pareto frontier. Thus, in this paper we choose 153 different shares from different industries and find their daily return for ten years from April of 2006 till March of 2017 and then we calculate their monthly return, downside risk, skewness, kurtosis and all of their fuzzy moments. After designing the four models and specific algorithm, we solve all of the four models for ten times and after collection of a table of the answers, compare all of them with Treyner ratio. At last, we find that using fuzzy and possibistic theory make higher return and better utilized portfolios.

Value at Risk model based on a switching regime approach was used in this study to optimize portfolios consisting of industry index (petroleum products, investment, chemical products, and metal products). For this purpose, the VaR of returns on index should first be extracted through parametric models of the (GARCH) family in each of the above industries by using regime transitions. After the risk of return on index is obtained for each industry, the optimal portfolio is created in the next step based on VaR minimization, and the optimal value of each industry is determined in the portfolio. According to the results, (MRS-FIEGARCH) model had no superiority in VaR estimation over the other parametric models of the GARCH family. In fact (MS-EGARCH-t) was introduced as the optimal model. Among the designated industries, returns on indices followed regime transitions only in chemical products and investment by showing asymmetric reactions to external shocks. Moreover, the optimal weights were on the rise in the industries where VaR decreased over time, whereas the optimal weight of the portfolio decreased in the industries where VaR increased over time. The higher share of an optimal portfolio belonged to the industries where stock returns had lower rates of VaR. The risk-return-ratio was employed to show that the optimal portfolio with a risk rate was measured by considering the switching regime was superior over the optimal portfolio with a risk rate extracted without considering the switching effects. To create an optimal portfolio, it is then recommended to make investments in the industries characterized by higher stability in prices and lower fluctuations in stock returns in the long run. This approach can be employed to obtain the best results from optimal portfolio preparation in the worst-case scenario of the market fluctuations.

Currently, the global community is on the threshold of a transition from fossil fuels as a result of transformations such as climate change, entailing the strict implementation of carbon setoff policies and quick progress in green technologies. A transition to clean, low-carbon energies will not only disrupt the global energy system, it will also impact the global economy and political dynamism within and without all states. Based on these transformations, the present article asks the question of how the energy transition will impact the geopolitical future of the global oil economy. The authors hypothesize the following in answer to this question: Given the superior, more advanced position of economic powers such as China and the US in clean energy technologies, we are likely to witness the weakening of traditional oil powers in a post-carbon world, such as the Middle East and North African countries (MENA) and Russia; in contrast, the balance of power will tilt heavily towards current energy consumers. Technology-savvy countries have the knowledge to reproduce and stabilize their power in the global economy. The traditional countries of the energy sector, however, face shortfalls in their infrastructure and modern technologies and will lose their geopolitical position to a large extent.