The Area Rate Proceedings An Unsettled Experiment in Public Control of Natural Gas Prices SMU Law Review Volume 18 | Issue 2 Article 1 1964 The Area Rate Proceedings An Unsettled Experiment in Public[.]
The dynamic relationship between gas markets and crude oil has been investigated in the extant literature. For example, Alexander (2004) finds strong correlation between returns on naturalgas futures contracts and crude oil. Villar and Joutz (2006) using the cointegration techniques and find a long-run (equilibrium) relationship between the Henry Hub naturalgas price and the WTI oil price. To model the evolution of electricity and naturalgasprices in the United Kingdom, Benth and Kettler (2011) use a bivariate non-symmetric copula and find that options prices are significantly influenced by the marginal distributions and the copula, along with the seasonality of the underlying prices. Some recent studies showed that a separation between oil prices and naturalgasprices had occurred (see, Ramberg and Parsons, 2012). Erdos (2012) found that the existing long-term equilibrium relationship between oil prices and US naturalgasprices disappeared after 2009. In addition, Loungani and Matsumoto (2012) found that the separation between US naturalgasprices and oil prices occurred as a result of the oversupply of naturalgas due to increases in the naturalgas production brought on by the US shale gas revolution. To analyze the dynamic relationships between crude oil and naturalgasprices, authors chose the co-integration methodology, the ECM (error correction model) and the causality of Granger (1969), (see, Jabir, Imad. (2006)). In this paper, we empirically investigate the time-varying linkages of daily crude oil (WTI and BRENT) and naturalgasprices (HENRYHUB) from January 01, 2004 until February 26, 2015. We use a DCC model into a multivariate fractionally integrated APARCH framework (FIAPARCH-DCC model), which provides the tools to understand how financial volatilities move together over time and across markets. Conrad et al. (2011) applied a multivariate fractionally integrated asymmetric power ARCH (FIAPARCH) model that combines long memory, power transformations of the conditional variances, and leverage effects with constant conditional correlations (CCC) on eight national stock market indices returns. The long-range volatility dependence, the power transformation of returns and the asymmetric response of volatility to positive and negative shocks are three features that improve the modeling of the volatility process of asset returns.
Crude oil as one of the main sources of energy is also the main source of income for members of OPEC. This is most noticeable in Iran because income obtained from oil and gas comprises about 60 percent of the Iranian government’s revenues and 90 percent of its export earnings (Farzanegan, 2011). Therefore, volatilities in oil price has an important role in creating economic fluctuations in oil-producing countries including Iran (MehrAra and Niki Oskuyi, 2006). The reason might be the high sensitivity of oil price to political, economic and cultural issues worldwide and consequently its volatility on the one hand, and the high influence of the volatile prices on macroeconomic variables (Kang et al., 2011). This is the reason why the Iranian economy is always exposed to receiving blows from foreign currency income and the danger of sudden changes in oil revenues. The con tinuous and lasting effect of this process on Iran’s economy especially during the recent years calls for a pressing need to make correct decisions in macroeconomic policies. Therefore, the dependence of Iran’s economy on revenues from selling fossil resources and the instability caused by their price volatility has made Iran prioritize non-oil exports (Mehrara and Mohaghegh, 2012). A remarkable portion of Iran’s non -oil exports include petrochemical products; methanol is one of the important petrochemical products. Furthermore, the relative advantage of producing and exporting petrochemical products, i.e., in its potential for creating jobs and increasing current earnings, can mitigate the negative effects of oil shocks (Mehrara and Oskui, 2007).
The following aspects of the role of gas quality in the naturalgas futures market are considered in this section. Firstly, naturalgasprices might be directly affected by gas quality (Hekkert, Hendriks et al. 2005). Secondly, market information asymmetry regarding product quality can give rise to a “lemons problem” (Akelrof 1970), which may be ameliorated by quality control measures. Quality control is largely based on requirements set by pipeline companies (Foss 2004). According to contract specification of Henry Hub gas futures, gas quality meets the specifications set forth by Sabine Pipeline Company and approved by the Federal Energy Regulatory Commission (FERC). However, even with this quality control in place, gas quality variations may be considerable, especially with spot LNG deliveries from various parts of the world since mid-1990s (EIA 2012) 7 . Therefore, in this section we test for an effect of gas quality on spot and futures price of naturalgas.
The Paper by Brown and Yücel (2008), attempts to explain driving factors behind naturalgasprices in the U.S. The authors compare the relationship between west Texas intermediate crude oil (WTI) and the Henry Hub spot price for naturalgas. They examine the viability of rules of thumb for predicting the relationship of naturalgas and oil such as the 10-1 rule and the burner tip parody. The study includes seasonality, weather, storage, and hurricane supply shocks as possible factors in determining the price of naturalgas. An error correction model (ECM) was used to examine the long and short-run changes in naturalgas price and oil. Weekly data was used for the time interval June 13, 1997 through June 8 th , 2007. Their results found a significant long-run cointegrating relationship between naturalgas and oil in the long-run, and that
the IMF provides the Henry Hub monthly prices for this study, the IMF records only go back to January 1991 (International Monetary Fund 2013b). However, the EIA provides U.S. naturalgas wellhead monthly prices between January1980 and December 1990 (U.S. Energy Information Administration 2013b). But there are two problems with the naturalgas data that must be addressed. First is that wellhead prices are measured in dollars per thousand cubic feet (Mcf) and must be converted to dollars per million British Thermal Units (MMBtu) by dividing the $/Mcf prices by 1.023 to equal $/MMBtu prices (U.S. Energy Information Administration 2013c). Second is that Henry Hub prices are measured downstream of wellhead prices and thus are not the same. However according to the EIA, wellhead prices and Henry Hub prices correlate nicely, with Henry Hub prices averaging 10.8% higher (Budzik 2013). Thus, the wellhead prices are increased accordingly to approximate Henry Hub where necessary. Monthly naturalgasprices in May 2013 dollars per MMBtu are shown in Figure 2.
As Figure 14 illustrates, petroleum prices are far more volatile than coal prices. The chart below reports the mean and standard deviations (denoted as S.D. in Figure 14) in prices paid by electricity producers for coal, pe- troleum, and naturalgas. Based upon the standard devia- tion, naturalgasprices are nearly seven-times more vola- tile than coal prices. As we increase our reliance on naturalgas in power generation, the cushioning effect that low-cost PRB coal has on average fuel costs dimin- ishes and average electricity rates become more sensitive to changes in naturalgasprices. For regions like the Northeast and Pacific coast, this transition will further increase average electricity rates, which are already well above the national average. But for the industrial heart- land, increasing the use of naturalgas in electricity gen- eration could dramatically increase electricity rates.
Abstract: This paper is on the development of adequate mathematical model of electricity price via Fourier series. Fourier series is the representation of a function as an infinite series in sine and cosine terms. Our choice of Fourier series model for electricity price is as result of its volatility, fluctuation trends of hydro flow and poor market designs and we use actively- traded naturalgas to hedge against electricity price in Nigeria. The naturalgasprices are volatile but do not have a clear seasonal pattern, thus eliminating naturalgas price volatility through hedging substantially reduce the electricity price, this development of logical mathematical frame work in the form of hedging tools assures an investor of his or her safety in the power sector.
Other studies also found strong links between the exchange rate and oil price in the long run. For example, Benhmad (2012) examined the long run causality between oil price and the exchange rate using wavelet analysis. Further, Beckmann and Czudaj (2017) stressed the strong association between the exchange rate and oil price in the long and short run. Furthermore, palm oil provides is a contributing factor for the ex- change rate behavior (Aprina 2014). Oladipo and Akinbobola (2011) found that an in- crease in the price of palm oil may affects its export value, which in turn leads to strengthening the currency. Although past studies focused on global or country-specific commodity price indexes, this paper mainly focuses on the response of the exchange rate predicted by Malaysia’s fuel and agriculture commodity impact on the nominal ex- change rate. As such, it examines the upward and downward adjustment of the short- run deviation of oil, palm oil, rubber, and naturalgasprices on the nominal exchange rate in the long-run in Malaysia. The direction and intensity of the relationship are considered through causality. The rest of the paper is organized as follows. Section 2 describes the empirical estimation strategies. Section 3 reports the results and discus- sion, and Section 4 concludes the paper.
Iran is the world's largest combined oil and gas resourc- es, and it seems that the nuclear deal will leave the industry altogether positive effects in the future. However, this effect was not observed in the short term. Iran desperately needs technology and capital for oil and gas sector that would be available from the IOCs.Despite the renewed interest of the super-majors in Iran, the increase in Iranian production will not be immediate as the domestic upstream sector is unlike- ly to be in any state to start producing cast volumes again at the turn of a tap. Even Iranian officials, who are fairly bullish about the sector, estimate that the oil and gas sector re- quires investment of between US $130 billion and US $145 billion in the next five years to keep oil production from fall- ing (with, for example, the enor¬mous South Pars gas field requiring up to US $100 billion). Iran has started a subsidi- ary reform in order to regulate the very low domestic naturalgasprices and thus improve consumption efficiency. The development of the South Pars gas field will significantly increase Iran's export potential in the future. In order for Iran to become a large scale exporter of national gas, however, fundamental structural reform would be required on both the international and domestic levels. Iran would need to create an investment environment more attractive to international companies. Though there are advantages to the scheme from the Iranian point of view, buy-back contracts are signifi- cantly less efficient than PSAs for rapidly raising output lev- els. Officials in Tehran will need to make a decision whether their priority is maximum control over the energy sector or fast increases in production. Beyond this, it would also be necessary to stop factional disputes from intervening in the energy sector and particularly in price negotiations.
Storage valuation literature focus either on best practice power price sim- ulation, gas price simulation or on valuing a general storage volume in the perspective of a commodity arbitrageur. Boogert and de Jong  apply the Least Squares Monte Carlo algorithm, developed by Longstaff and Schwartz , to value a naturalgas storage contract. They show that the size of the effective storage volume as well as injection and withdrawal rates are the most important value-determinants. Lai et al.  value the option to store naturalgas in the form of LNG using a heuristic that incorporates naturalgasprices, LNG shipping models and inventory control. Bjerksund et al.  show that an advanced price process is of greater importance than a an ad- vanced optimisation model, when valuing gas storage. Valuation of storage in connection with CO 2 capture plants is considered by [14, 15]. Finally, 
are integrated of order 1, that is I(1), the return time series obtained from the price time series transformation will be stationary, that is I (0). The plots shown in Figure 2 presents the price returns time series. Table 2 below shows a statistical summary of price return time series of the crude oil and naturalgas benchmarks, as well as the normality and the stationarity hypothesis tests results. The average values of these time series have similar values and as demonstrated by standard deviation the NBP naturalgas return time series show the greatest variability. It is possible to reject the normality hypothesis at a significance level of less than 1%. According to the ADF test, it is possible to reject the unit root hypothesis for the returns time series at a significance level of less than 1%. Thus, as the plots analysis and as occurs with financial series, the crude oil and naturalgasprices are integrated of order 1, or I (1), once their first differences constitute stationary time series. Figure 2 below shows the price return time series, and from the plots it is possible to identify a common behavior in these price return time series, with highly positive or negative observations appearing in clusters, that is, the phenomenon of concentration of higher volatility followed by others periods of relative lower volatility. This shows that the volatility of the current period is related to that of past periods, which presupposes autoregressive heteroskedasticity. Using the ARCH test for price returns time series this proposition can be confirmed.
for trend. Only discrepancy comes from KPSS statistic on original series of naturalgas which does not reject trend stationarity. The p-value is greater than 10 percent. Whereas we apply test on properly detrended series that was purged of seasonality, the results are straightforward. Unit root is rejected in every series and stationarity is not rejected in case of the ADF, DF-GLS and KPSS, respectively. Therefore, cointegration technique can not be used and the OLS or 2SLS, alternatively, is right approach.
country’s foreign exchange reserves, and supplying energy to industry and commerce (Odularu, 2008). It is clear that the UAE is trying to become a leading technology centre based on the innovation strategy of the 4th Industrial Revolution (Alkhateri, Abuelhassan, Khalifa, Nusari, & Ameen, 2018; Ameen, Almari, & Isaac, 2019). Given that the UAE is expected to depend heavily on oil, naturalgas, and associated industries in near term (Independent Statistics & Analysis, 2017), and the oil prices have witnessed sharp decline in the global oil market recently; it is important to know how petroleum industry in the UAE has been using strategic cost management techniques as competitive advantage analysis, continuous improvement, to enhance its performance, and ultimately contribute to the country’s economic activities. With this in mind, the researcher empirically undertakes this research to investigate the influence of competitive advantage analysis, continuous improvement on the petroleum industry firms’ performance in the UAE. By providing adequate answer to the yearning of previous researchers (Afonina, 2015; Iseri-Say, Toker, & Kantur, 2008) to carry out more research in this area, the present study is expected to be first of its kinds that look into the influence of competitive advantage analysis, continuous improvement on the UAE petroleum sector performance.
For too long, Europe’s energy relationship with Russia has been directed by only a few member states. The role of former German Chancellor Gerhard Schroeder in giving President Putin a pass in the areas of democracy and competition are well documented. But the United States has also been more eager to secure energy supplies from Russia than to pressure the Kremlin into reforming its economy. The EU and the United States have for too long ignored the non-competitive and political aspects of Russia’s energy export policies. This is due in part to competition by Western companies for exploration and production rights in Russia. How much thought has been given to the potential power of Gazprom to control the gas markets in Central Europe following the completion of the Baltic pipeline system? Under the German-Russian agreement, Gazprom will be able to buy significant shares in Germany’s gas companies. Will this allow Gazprom to veto shipments of gas from Germany to Poland if the Poles have a dispute with Gazprom over price or availability? Could the increased power of Gazprom be used to stop liquid naturalgas (LNG) receiving plants from being constructed in Poland, Latvia or even in Germany? If the EU decides to implement its long-awaited requirement for member states to have more gas storage, will this be possible now that the EU has blessed the Baltic pipeline system designed to bypass Poland and the Baltic States? What about Russian purchases of gas from Turkmenistan, Uzbekistan and Kazakhstan that are clearly designed to deny the West the ability to buy