The decision over asset holding is traditionally premised on the double-edge objective of returns maximization and risk minimization. While the class of assets held by a typical investor depends on his attitude towards risks, an optimal investment portfolio requires a strategic combination of alternative assets (commodities, T-bills, stocks etc). To this end, our paper analyzes the role of crude oil prices in predicting stock returns, in addition to the traditional factors, particularly the returns on risk-free assets (such as, T-bills) as enunciated by the Capital Asset Pricing Model (CAPM). We also consider the possibility of nonlinearities in the nexus between crude oil prices and stock returns of nine major oil and gas companies that are currently listed on the Nigerian Stock Exchange over the period of January 2014 to November 2019. Our results show significant in-sample predictability of stock returns using crude oil prices, thereby affirming our argument that oil price matters in the predictability of stock returns for some listed oil and gas firms in Nigeria. We also offer evidence for the role of asymmetries in the predictability of stock returns for the majority of the listed oil and gas companies in Nigeria. By implication, the increasing exposure of the earnings, vis-a-vis, � the share prices of some major oil and gas companies to negative changes in global oil prices suggests the need for diversification of their scope of operations.

Introduction

The Nigerian economy is largely oil dependent, making it highly sensitive to movements in global crude oil prices. Oil price volatility matters for the investment decisions of prospective investors in Nigeria’s oil and gas sector, most especially. This in turn affects the profitability of firms and hence the values of their shares on the domestic stock market (see, for instance, Gupta, 2016 and Soyemi et al., 2017). In the words of Kayalar et al. (2016), changes in crude oil prices are believed to affect stock markets through the channel of expectations. Meanwhile, Basher and Sadorsky (2006) argued that the impact of falling oil prices on stock market differs from country to country depending on whether the country is an oil exporter or an oil importer. In an oil exporting country, an increase in oil prices improves the trade balance, leading to a higher current account surplus and an improving net foreign asset position. At the same time, a rise in oil prices tends to increase private disposable income in oil exporting countries. This in turn enhances corporate profitability, boosts domestic demand and push up stock prices, thereby causing exchange rate to appreciate. In oil importing countries, the process works broadly in reverse: trade deficits are offset by weaker growth and, overtime, real exchange rate depreciates and stock prices decline (Basher and Sadorsky, 2006). The extent to which stock prices are influenced by world oil price changes is explained by the theory of equity valuation, which defines the stock price as the sum of discounted values of expected future cash flows at different investment horizons (Jouini, 2013). Consequently, oil prices affect stock prices directly by impacting future cash flows or indirectly through an impact on the interest rate used to discount the future cash flows. In the absence of complete substitution effects between the factors of production, rising oil prices, for example, increases the cost of doing business, and for non-oil related companies, it reduces profits. Rising oil prices can also be passed on to consumers in form of higher prices, but this will reduce the demand for final goods and services and depress profits.

1. Introduction

2. Literature review

3. Problem description

4. Model

5. Numerical test

6. Conclusion

Acknowledgements

Appendix A. Supplementary data

Research Data

References

Abstract

Financial risks related to crude oil imports are certainly affected by crude oil price uncertainty. Our question is: How important is it to take also physical risks, such as the crude oil exporters’ political risks and transportation risks into account when controlling financial risks in line with the importer’s risk attitude when planning crude oil imports and transportation at a tactical level? In this paper, two-stage stochastic programming models are proposed to illustrate the problem, and a numerical test is conducted to better understand the effects of physical risks. The mechanism for controlling risk will be forward physical contracts. The results show that the real financial risk is much higher than the importer might believe if physical risks are not considered. Unless the importer is risk neutral, more forward crude oil will be imported when physical risks are considered, and the distribution of forward crude oil will depend strongly on correlations among risks.

Introduction

Crude oil is of strategic importance in all countries, and it is often crucial for a crude oil importer’s national economy. Every import country wants to secure an uninterrupted availability of crude oil at an affordable price. But many crude oil exporters, particularly those located in the Middle East, Africa and Latin America, suffer political instability or have a high risk potential (Vivoda, 2009). The unstable political situations in these regions will create supply disruption risks for importers. There are also correlations between political risks of different exporters in the same region, since they are impacted by some common risk factors (Li et al., 2009). For example, the war in Yemen, with Iran and Saudi Arabia involved, leads to instability in the Middle East. Disruptions in supply will lead to an imbalance between supply and demand in the international crude oil market, and global crude oil prices will fluctuate. Since the global crude oil market is highly integrated, the crude oil prices of other exporters will also be affected. In addition to the political risks and crude oil price fluctuations of exporters, there are also transportation risks. Most crude oil is transported by ship. But the straits and canals in the maritime transportation network are exposed to risks due to political instability in neighboring countries, as well as terrorism, piracy, conflicts and other extreme events (Emmerson and Stevens, 2012). For example, the political instability in the Middle East threatens the safety of the Bab el Mandeb and the Suez Canal. If certain straits or canals are closed, the crude oil transportation will be affected.

Due to the political risks of exporters, transportation risks of straits and canals, and oil price uncertainty, crude oil import and transportation decisions are very challenging for importers, who want to control financial risks. But this problem is underexplored. In previous studies, the focus has mostly been on crude oil price fluctuations, and different hedging strategies are proposed. Chen et al. employed geometric Brownian motion processes to model oil price fluctuations, and determined the optimal oil procurement. Operational and financial hedging strategies are proposed by Ji et al. (2015) to manage risks caused by oil price fluctuation. Chang et al. (2011) used a dynamic multivariate GARCH to obtain a crude oil hedging strategy. Sukcharoen and Leatham (2017) proposed the use of vine copulas to capture characteristics of price changes and estimate multiproduct hedge ratios for a refinery to manage risks. In these studies, there are no considerations of selection of exporters, so the political risks of exporters are not taken into account. In addition, the transportation risks are also not considered.

We evaluate the impact of changes in the price of crude oil on the United Kingdom (U.K.) real gross domestic product (GDP) growth rate by way of an out-of-sample forecasting analysis. We compare the performance of several nonlinear models and determine, which aspects of nonlinearities are most useful for obtaining forecast improvements. Likewise, our approach takes into account the possibility that relative predictive performance can vary over the out-of-sample period. Results based on quarterly data from 1974q1 through 2018q4 illustrate that our conclusions depend on the definition of forecast improvement and whether we rely on pairwise or multiple forecast comparison. For instance, it is very difficult tofind evidence that point forecasts exploiting crude oil price variables are statistically significant more accurate than point forecasts produced under the benchmark. On the other hand, the null hypothesis of no population-level predictability is borderline rejected for certain nonlinear crude oil price variables. We also observe notable differences between using real-time and ex-post revised GDP data with regards to local out-of-sample performance. The predictive power associated with the more successful crude oil price measures appears to concentrate in the early 1990s and around the onset of the Great Recession.

Introduction

The aim of this study is to evaluate the predictive impact of changes in the price of crude oil on the United Kingdom (U.K.) real gross domestic product (GDP) growth rate via an out-of-sample analysis. We are motivated to do so based on several interesting results reported in widely cited studies, such as Bachmeier, Li, and Liu (2008), Carlton (2010), Kilian and Vigfusson (2013), Ravazzolo and Rothman (2013) and Ravazzolo and Rothman (2016) . 1 The central question running through these studies is whether changes in the price of crude oil have a predictive impact on the real U.S. GDP growth rate out-of-sample.2 However, in the context of other countries relatively less is known about whether (a): Changes in the price of crude oil help forecast the country specific real GDP growth rate, (b): If the predictive impact of crude oil price changes on the real GDP growth rate is nonlinear and (c): If so what types of nonlinearities. In fact, in their seminal study, Kilian and Vigfusson (2013) argue that understanding the nonlinear/asymmetric predictive impact of changes in the price of crude oil on the real GDP growth rate deserves further study on non-U.S. data. Arguably, the U.K. can be considered as an interesting case because it is a developed, open economy, which has transitioned from being a net crude oil importer in the 1970s to a net exporter in the 1980s and early 1990s before returning to being a net importer again since the mid 2000s.3 This in turn, provides a very good opportunity to evaluate (a), (b) and (c). Likewise, studies such as Jim
enez-Rodríguez and S
anchez (2006), Harrison, Ryland, and de Weymarn (2011), Millard (2011) and Millard and Shakir (2013) focus on the causes and effects of crude oil price changes on the U.K. economy via examination of impulse response functions. However, they do not account for nonlinearities in the relationship between the price of crude oil and the growth rate. Similar to simulations provided by the National Institute for Economic and Social Research (NIESR) and the Bank of England they conclude that a fall in the price of crude oil leads to an increase in the U.K. GDP growth rate. To date, a comprehensive analysis on the predictive impact of crude oil price changes on the U.K. GDP real growth rate has not been conducted. The purpose of this study is to fill this gap. Finally, the increasing financialization of the crude oil market and the important role of crude oil in the context of a developed, open economy, such as the U.K. makes our analysis interesting to a broad audience.

1. Introduction

2. Method and data

3. Results and discussion

4. Conclusions

Acknowledge

Appendix A. Supplementary data

References

Abstract

The petroleum refining industry is an important contributor to industrial volatile organic compounds (VOCs) emission. As a foundational work for VOCs control, previous VOCs inventories usually treated the petroleum refining industry as an integrated part resulting the unknown of VOCs emission characteristic among different units. Refined management has becoming an inevitability with the increasing strictness of environment standards. This study aims to develop a holistic method for VOCs emission inventory of the petroleum refining industry toward specificity, accuracy and economization, in which a source categorization was proposed from unit angle, and a systematic estimation method was developed from the material flow point of view. This method provides a more specific and accurate quantification method, especially for fugitive emission sources which is a pivotal but difficult problem in VOCs inventory establishment. Through application to a typical medium-scale refinery located in northern China, a unit-specific VOCs emission inventory with 48 emission sources and respective local VOCs emission factors (EFs) was established. Estimation results and economic cost of the developed method were compared with those of other methods. In this study, the integrated EF was 0.77 kg-VOCs/t-crude oil refined, which was in the same order with most previous studies. Inventory results implied that increasing hydrotreating unit, reducing chemical device and solvent-used unit (e.g., polypropylene production, furfural refining unit), upgrading catalytic reforming unit were beneficial to control VOCs emissions in this study. Using floating-roof tanks rather than fixed-roof tanks is an effective way to reduce VOCs emissions from storage tanks, which decrease 72%–86% of diesel storage emissions. Economic cost analysis showed that the advantage of this method lied in lower labor cost, and no subsequent monitoring cost. Suggestions proposed from this study provide feasible measures for local policy makers to control VOCs emission and determine abatement strategies of the petroleum chemical industry. Meanwhile, this study greatly helps enterprises promote the fine management of VOCs-containing materials from the overall processes to identify VOCs control emphasis.

Introduction

Air pollution characterized by high concentration of groundlevel ozone (O3) and fine particulate matter (PM) has caused increasing concerns in China due to its significant adverse impact on human health (Dai et al., 2017; Rohde and Muller, 2015). Volatile organic compounds (VOCs) are key precursors for the formation of ozone and PM through complicated photochemical and physical reactions (Shao et al., 2009a, 2009b; Yuan et al., 2013). Ozone reduction is largely accomplished by the control of VOCs (Tang et al., 2012). Chinese government has been focusing on VOCs pollution problem. The 13th five-year plan for ecological and environmental protection made the reduction target that the total national VOCs should decreased by more than 10% during the 13th five year. Standard for fugitive emission of volatile organic compounds (MEEC, 2019b) and comprehensive VOCs treatment scheme for key industries (MEEC, 2019a) were successively published to promote VOCs prevention work, recently. The petroleum refining industry is an important contributor to VOCs emissions, accounting for approximately 3% of the total anthropogenic VOCs emissions (Wei * Corresponding author. et al., 2016). In 2015, the petrochemical industry become one of the first batch of pilot industries to implement VOCs Pollution Pilot Charge Method published in China. Thus, a specific VOCs emission inventory of the petroleum refining industry is necessary and foundational for VOCs emissions reduction and equitable control policies.

1. Introduction

2. The U.S. Petroleum refining industry

3. U.S. crude oil production and consumption

4. U.S. exports of finished petroleum products

5. Scenario analysis

6. Results & discussion

7. Conclusion

Acknowledgement

References

Abstract

In 2018 the U.S. petroleum refining industry is the largest and most advanced in the world. Continuous consolidations and investments in complex refinery additions have allowed this industry to remain competitive and the shale oil revolution has contributed to the U.S. becoming a net exporter of refined petroleum products in 2008. In light of current and forecasted changes in refined petroleum product demand and worldwide refining capacity additions, the U.S. petroleum refining industry faces new challenges. This paper provides an in-depth study of this industry, presenting past trends, its current state and the effects that a changing U.S. crude oil production has on refiners. Furthermore, a scenario analysis is used to forecast future production levels and the volumes of major refined products available for exports over the years 2017–2032. The competitiveness of current U.S. gasoline and diesel exports is evaluated and forecasted gasoline and diesel demand in current export markets is compared to available export volumes. Major challenges facing refiners by changing market conditions and new regulatory rules are discussed. Finally, a set of recommendations is provided.

Introduction

The U.S. petroleum refining industry is the largest and most sophisticated of its kind in the world in 2019. Since the lifting of the U.S. Crude Oil Export Ban in December 2015 all international trade restrictions in this industry have been lifted and the U.S. market is fully integrated in the world market for refined products. In 2017 global petroleum refining capacity reached 98.7 million barrels per day (MMBD) and the U.S. accounted for nearly 19% of this (OECD/IEA, 2018). The International Energy Agency forecasts U.S. domestic capacity to further increase by 0.2 MMBD by 2022 (OECD/IEA, 2017a, pp. 1–147). However, with changing regional trends in refined petroleum product demand growth, shifts in refining capacity additions, and the increased complexity of refineries around the world (Zavaleta, Walls, & Rusco, 2015), the U.S. petroleum refining industry faces new challenges. Over the years 2000–2017 global refining capacity has grown by 17 MMBD, and Asia accounted for 65% of these additions, while the Middle East accounted for 15% (Gresh, Royall, & Yu, 2017, pp. 1–106). A similar trend is expected through 2023 as world refining capacity is forecasted to increase by 7.7 MMBD from 2017 levels (OECD/IEA, 2018). The Middle East (with big projects in Kuwait, and Saudi Arabia),1 China, and India account for more than half of all projected capacity additions from 2017 to 2023. The latter investments are driven by strong economic growth leading to an increase in the standard of living and an increased demand for transportation fuels in many non-OECD countries. In addition, new fuel specifications that have come into effect recently in Asia (Lofting, Malek, Arun, & Dsouza, 2018), and China’s desire to become a prominent supplier of refined petroleum products to South East Asian nations has also spurred capacity increases.

Abstract

The influence of polyacrylamide (PAM) as drag reducing polymer on flow of Iraqi crude oil in pipe lines was investigated in the present work. The effect additive concentration, pipe diameter, solution flow rate and the presence of radius elbows on the percentage of drag reduction (%Dr) and the amount of flow increases (%FI) were the variables of study. Maximum drag reduction was 40.64% which was obtained with 50 ppm of PAM polymer flowing in straight pipes of 0.0508 m I.D. The dimensional analysis was used for grouping the significant quantities into dimensionless group to reduce the number of variables. The results showed good agreement between the observed drag reduction percent values and the predicted ones with high value of correlation coefficient.

Introduction

A spectacular reduction in energy losses in turbulent flows can be achieved by the addition of small amounts of certain polymers. Polymer drag reduction is due to the large elongational viscosity of the polymer solution; this stabilizes the turbulent boundary layer, leading to less turbulent energy generation and hence less dissipation [1]. Drag-reducing polymer solution flows behave like viscoelastic characteristics. The most notable elastic property of the viscoelastic polymer solution is that stress does not immediately become zero when the fluid motion stops, but rather decays with some characteristic time (the relaxation time), which can reach seconds and even minutes. It is generally believed that the frictional drag reduction caused by polymer and surfactant additives in a wall-bounded flow is the consequence of the interaction between viscoelasticity and turbulence in the flow. In a cationic surfactant solution, rod-like micelles can be formed if the surfactant/counterion chemical structures, molar ratios, concentrations, and temperature are under right conditions. This network microstructure imparts viscoelasticity to the solution flow, which was often stated to be responsible for the occurrence of Dr[2], [3]. Polyacrylamide is a high molecular weight water soluble polymer that used to improve flow. It is used to reduce energy and friction losses and can also be called friction reducers. Water can be injected to simulate oil production. The spontaneous flow of oil and water in crude petroleum production and transportation pipelines is a common occurrence, seen anywhere from the well perforations to the final stages of separation. The goal of the present work was to investigate the validity of the effectiveness of polyacrylamide (PAM) as drag reducing agent with Kirkuk crude oil. Also the effect of additives concentration, pipe diameter, solution flow rate and the presence of radius elbows on the percentage of drag reduction (%Dr) and the amount of flow increases (%FI) were the variables of the study.

Since the seminal work of Schwert (1989a), economic sources of financial volatility have been investigated extensively (e.g., Asgharian et al. (2013); Choudhry et al. (2016); Christiansen et al. (2012); Diebold and Yilmaz (2008); Engle and Rangel (2008); Engle et al. (2013); Nonejad (2017); Paye (2012)). This interest stems from the fact that financial volatility is a crucial input in risk management, portfolio allocation and asset pricing. Financial volatility is also found to successfully predict business cycles, providing early signals of upcoming recessions (Chauvet et al., 2015). However, a recent paper by Paye (2012) shows that although some variables such as treasury spread and default returns can theoretically affect stock volatility, it is difficult to find an individual variable that can predict stock volatility. In detail, adding any macro variables to the benchmark autoregressive model cannot significantly improve out-of-sample forecasting performance. The failure of individual fundamental variables in forecasting stock volatility is further confirmed by more comprehensive analyses conducted by Christiansen et al. (2012), unless some modeling issues such as parameter instability and model uncertainty are addressed (Nonejad, 2017). In this paper, we show that a new variable, crude oil volatility, can be strongly predictive of stock volatility. We demonstrate that oil volatility improves the short-horizon predictability of stock volatility over the autoregressive benchmark model. This predictability is significant during various sample periods. Our investigation complements studies of modeling and forecasting volatility by providing a new fundamental determinant of stock volatility. Our findings are helpful for understanding the economic sources of changes in stock volatility. Various studies have investigated the relationship between oil and stock volatility (see, e.g., Degiannakis et al. (2014); Arouri et al. (2011, 2012); Creti et al. (2013)). Most papers take an in-sample perspective using multivariate GARCH models. However, it is commonly understood that good in-sample performance does not imply that the predictive model displays superior out-of-sample performance. We contribute to the literature by paying attention to the ability of oil to predict stock volatility from an out-of-sample perspective. We employ a parsimonious predictive regression based on realized volatility to provide forecasts, the superiority of which over the GARCH has been well documented in the literature. Our paper is closely related to Driesprong et al. (2008), who find that changes in oil prices predict stock returns in-sample. We complement their work by showing that oil volatility can also predict stock volatility both in-sample and out-of-sample. This paper is also linked to Chen et al. (2010), who show that commodity prices cannot predict asset prices such as exchange rate. We provide a novel example whereby the volatility of a special commodity, crude oil, is shown to have predictive power over stock return volatility. We extend the idea of Chen et al. (2010) to a volatility case and obtain different results.

Oil prices experienced extraordinary plummets from 2008 to 2009 and 2014 to 2016. During these short periods, world oil prices crashed by more than one half. Many economic analysts ascribed the decline during the period 2008–2009 to the global financial crisis. They believed that the 2008 financial crisis resulted in a worldwide economic slump, which, in turn, undercut international oil prices. Later on, numerous industry experts and academics asserted that amassed supplies of oil resulted in tumbling prices during the period 2014–2016. In an attempt to designate the specific supply factors that are responsible for the decline, both U.S. shale oil development and increasing crude oil production of OPEC countries have been suggested. Moreover, there have been various other arguments used to explain the causes of unusual oil price declines. However, to the best of our knowledge, no study has conducted a thorough empirical investigation regarding these causes. Certainly, establishing the cause(s) of the recent collapses by rigorous evidence will provide meaningful information for both economic policymakers and scholars. Our research findings via a novel and rigorous approach suggested in this article may contribute to the work of professionals in both spheres. This article proposes a new approach to verify the above views by examining the relation between demand and supply factors, and oil price dynamics, via some predictive models. In particular, the supply and demand factors of price determinants are examined to explain the causes of the plummets. We propose applying novel autoregressive models with exogenous variables reflecting real demand, speculative demand, and supply factors for forecasting monthly global crude oil prices during the periods of decline. Specifically, we build four different types of predictive models: (1) with only real demand factors, (2) with real and speculative demand factors, (3) with real demand and supply factors, and (4) with real and speculative demand and supply factors. We evaluate these models and suggest which factors are the most important in explaining the oil price drops during the two aforementioned periods. First, we analyze the period of falling global oil prices (June 2008–February 2009). We then check whether the findings during the 2008 oil price fall were still observable during June 2014–February 2016, when prices collapsed again. Due to the backdrops of in-sample forecasting evaluation, such as sensitivity to outliers and model misspecifications (Inoue and Kilian, 2004; Panopoulou and Pantelidis, 2015), we utilize the out-ofsample forecasting approach in the evaluation of candidate models. The empirical forecasting results are retrospectively evaluated to identify the factors enabling improved forecasting performance.

In today’s life, the uninterrupted supply of electricity to consumers is in close relation to the consistent function of the transformers. Thus, the major components of the transformers should be maintained to achieve its designed service life span [1]. The life of transformer relies on the life of their insulation system that undergoes a number of stresses, such as electrical, thermal, mechanical and environmental. The transformers are unable to operate for the designed service period [2], due to active participation of such stresses. It is therefore necessary for the utility to have continuous monitoring different insulations used in transformer. The transformer insulating system, primarily consisting of two special dielectric media; the insulating mineral oil whose ionic conduction varies and the oil impregnated paper (or pressboard), whose life is limited, even under ideal circumstances. The existence of transformer typically depends upon the health condition of its CIP. The fundamental element of pressboard and paper is cellulose, which is polymer of glucose wherein glucose units link to each other in a chain like structure. Chemically cellulose is represented as [C6H10O5]n, here ‘n’ refers to number of glucose units for each polymeric chain, more specifically Degree of Polymerization (DP); a significant factor of paper degradation. Thus the value of DP specifies the number of monomer unit in the polymer as cellulose is the linear polymer constituent of individual anhydrous glucose units associated with glucosidic bonds. It have been verified that there is direct relationship between mechanical tensile strength of the paper cellulose and the corresponding value of DP. Generally, a fresh cellulose paper has a DP value of 1200 which reduces slightly when subjected to stresses. The cellulose paper life comes to an end when the value of DP approaching to 200. The Kraft/Cellulose paper losses its tensile strength as it is subjected to ageing and, at some point, does not survive on the short circuit stresses. The mechanism involve in paper deterioration is quite difficult and sturdily influenced by the working environment. Degradation of solid insulation occurs due to schism of the 1, 4-glycoside bonds among two consecutive glucose monomers present in it. As a result of these chain scissions, the DP gets reduced that ultimately lowers the mechanical tensile strength of the paper. During ageing, cellulose is decomposed into various substances including furanic compounds, acids, CO2 and CO. These byproducts are partially soluble in oil and are generally characterized as indicators for cellulose ageing and hence show the end of life of power transformers. Several authors contributed their research towards deterioration of cellulose under moisture and thermal stress to enunciate the end of life of power transformers using Degree of Polymerization (DP) as a failure index. A mutual stress failure model was developed to perform a chain of thermal aging experiment on kraft paper of power transformer on a laboratory scale [3].Diagnostic and health monitoring approaches based on the value of the ratio of carbon dioxide and carbon Monoxide (i.e. CO2/CO) gases were also investigated in [4]. Soft computing techniques like Neural Network (NN), Fuzzy Logic (FL), Neural Fuzzy System, Support Vector Machine (SVM) and Wavelet Network [5][6][7]were applied to assess the health condition of CIP. These models used carbon oxide gases to evaluate the degradation of paper cellulose. The main objective of this work is to monitor the condition of CIP using the fuzzy model that correlate the concentrations of CO, CO2 and 2-FAL with the value of Degree of Polymerization (DP). Input to the developed FIS is 2-FAL, CO2 and CO and output is the value of DP which ultimately decides the residual life of transformers.

In the aftermath of the oil crisis of the early 1970s, the relationship between oil prices and economic growth became a focal point of the scientific discourse and public debate. In 1983, James Hamilton published an influential article showing that an oil price increase had preceded all but one recession in the United States since the end of World II [1]. Since then, a large number of empirical studies have looked into the connection between oil prices and real economic growth and frequently found a significant negative correlation [2,3]. The importance of this link between oil price and economic growth was less clear after the second oil shock [4–7]. Recent studies, with more refined statistical tools and price specifications, have restored the link between the oil price and economic growth [8–12]. There is now a general consensus that this connection did not cease but has become more complex in terms of direction (anticyclical and procyclical), typology of shocks (demand or supply) and lag patterns [4,9,11,13,14]. This line of research tried to explain this tight relationship, given that the cost of energy is only a small part of GDP [13] but satisfactory explanations have remained elusive [2,4,13,15]. Interestingly, this research exploring the link between the oil price and economic indicators seems to have entirely ignored the transport sector, which is heavily reliant on refined crude oil products, and its role in shaping the global division of labor. In the post-war period, world trade grew at a faster pace than world GDP [16]. According to recent studies on globalization, the remarkably high rate was propelled by a dramatic decline in international transport costs [16–19].

Energy commodities have become a major part of financial markets as a result of the rapid growth in trading volume and the variety of derivative products, among which the crude oil futures and options have taken a significant proportion. Specifically, the trading volume of crude oil futures and options accounts for over 50% of the total trading volume of energy contracts on the NYMEX in 2015. As for the equity (index) option market, the commodity option market enables the study of variance risk premium, that is, the premium asked by market participants to invest/trade volatility risk. For the extensive literature on variance risk premium we refer, without being exhaustive, to [2], [7], [22], [9] and [19]. Beyond variance risk premium, skew risk premium has recently attracted a strong interest among academics. In [14], see also the important and related work of [16], the authors found that skew risk premium naturally completes variance risk premium for the equity index option market. The fact that financial markets react differently to positive and negative shocks has been widely acknowledged in previous literature. Consequently, semivariance measures, considered in [3] or [18], were found to carry more information than unconditional measures. For the specific case of crude oil market and the relevance of semivariance measures see [8] or [20]. Following that line of research it is therefore natural to assess whether tail risk premium or conditional variance risk premium carries more information than standard (i.e. unconditional) variance risk premium. In [5], [15] and [13], it was confirmed that the conditional variance risk premium has higher forecasting power for equity index excess returns. Based on these works we contribute to the literature by performing a conditional decomposition of variance and skew risk premiums extracted from options written on the USO (an exchange traded fund tracking the daily price changes of the WTI light sweet crude oil) and analyze their relations with the USO excess return. More precisely, our contributions are threefold: (i) we analyze the contemporaneous relationships between these decomposed higher moment risk premiums and the USO excess return, and show that the decomposition is essential to obtain significant results; (ii) we show how these decomposed higher moment risk premiums enable a much better prediction of the USO excess return than the undecomposed variance and/or skew risk premiums, thereby extending significantly the results of [9] that already showed the importance of variance risk premium over other trading activity factors when it comes to prediction; (iii) we provide an extensive comparison with existing results for the equity index option market that highlights the specifics of the crude oil market and further underline the usefulness of the decomposition.

In 2014, global oil supply overtook demand and the oil price started to decline from mid-2014. In its November 2014 meeting, OPEC1 decided not to reduce supply and prices fell further. Many oil-market analysts interpreted this as the formal decision to squeeze higher-cost competitors, including US shale oil extracted using hydraulic fracturing (“fracking”), out of the market. The Saudi Arabian oil minister at the time (and de facto leader of OPEC) expressed intentions consistent with these interpretations: “In a situation like this, it is difficult, if not impossible, for the kingdom or for OPEC to take any action that would reduce its market share and increase the shares of others…”2 This decision stood in contrast with OPEC’s coordinated cut during the Global Financial Crisis. OPEC’s actions occurred against the backdrop of weakening global demand for crude and several years of steadily rising capacity from non-OPEC sources—most notably from unconventional sources in the US. Since mid-2014, the oil price fell from above $100 to an average of $50 during 2015. In its December 2015 meeting, OPEC reiterated its commitment to a “marketshare” strategy. Many have opined on whether OPEC is taking a sensible perspective by driving competitors out of business or whether it is a misguided move tantamount to “hara-kiri”.3 Our goal in this paper is to understand the fundamental market factors that induced the shift in OPEC’s strategy. We present a simple economic model of the oil market: OPEC has a degree of market power and competes against a set of non-OPEC producers who act as a price-taking competitive fringe.4 OPEC has a choice between two strategies. The first strategy, which we call “accommodate”, is to maximize profits via a “high” oil price which allows high-cost non-OPEC producers to remain profitable. The second strategy, “squeeze”, is to drive up production—and hence drive down price—and thereby induce high-cost producers to exit the market.