The price of crude oil in the international energy market: A political analysis

Political processes, claims the author, offer the chief explanation of price trends in the international oil market. Although these processes take place within an economic framwork, the band between the costs of production and those of alternative sources of energy within which prices could move, is so large that is has lost much of its practical effect in determining prices. A number of actors are identified whose interaction principally determines prices. The historical development of this interaction is traced and the author concludes that it may ultimately be in the interests of all actors — producers, consumers, companies and developing nations — to coordinate their efforts in synchronising the introduction of new energy sources to replace oil.     

The role of market fundamentals and speculation in recent price changes for crude oil

I hypothesize that the price spike and collapse of 2007–2008 are driven by both changes in both market fundamentals and speculative pressures. Contrary to arguments for a demand shock, I hypothesize that prices rise sharply in 2007–2008 because ongoing growth in Chinese oil demand runs into a sudden and unexpected halt to a decade long increase in non-OPEC production. This caused a loss of OPEC spare capacity because increased demand for OPEC production runs ahead of increases in OPEC capacity. These changes are reinforced by speculative expectations. Although difficult to measure directly, I argue for the role of speculation based on the following: (1) a significant increase in private US crude oil inventories since 2004; (2) repeated and extended break-downs (starting in 2004) in the cointegrating relationship between spot and far month future prices that are inconsistent with the law of one price and arbitrage opportunities; and (3) statistical and predictive failures by an econometric model of oil prices that is based on market fundamentals. These changes are related to the behavior and impact of noise traders on asset prices to sketch mechanisms by which speculative expectations can affect crude oil prices.     

The price of OPEC crude oil

What will be the price of petroleum in 1985? Dr Cochrane describes a methodology for determining the price of OPEC crude oil in 1985 assuming a zero growth in output. He reviews the energy balance sheets of OECD areas — North America, Western Europe and Japan — and concludes that a price of $34 per tonne could be realised by members of OPEC by 1985, but that this depends on keeping annual output for 1985 below the annual output for 1975.     

Interpreting the oil risk premium: Do oil price shocks matter?

This paper analyzes the link between the economic fundamentals of the global crude oil markets and the oil futures risk premium. The compensation for risk required by speculators in the oil futures market is modelled as part of the endogenous transmission of oil price shocks. The empirical approach is based on a Structural Vector Autoregressive model of the international market for crude oil. The dynamic response functions show a negative relationship between the risk premium and the real price of oil, triggered by shocks to economic fundamentals. Moreover, the expected returns of a long futures investment are largely explained by a specific shock component related to oil speculators and a shift in the global demand for crude oil.     

Global economic activity,crude oil price and production,stock market behaviour and the Nigeria-US exchange rate

This paper investigates the dynamic relationship among oil price, Nigeria-US exchange rate, stock market activity, Kilian's global economic activity index, and global oil production. We develop a robust, stable single-equation error correction model where the exchange rate solely bears the burden of short-run adjustments with causal influences from the rest of the variables included in the model. We find a role for asymmetry in the long run, confirming the presence of equilibrium-path adjustment asymmetry and suggesting that the positive and negative variations must be accounted for in designing the policymaking process to enforce stable exchange rate movement. By comparing the linear and nonlinear models, we find that the two models are complementary and that each is horizon-bound in its forecasting ability.     

Influential factors in crude oil price forecasting

This paper identifies factors that are influential in forecasting crude oil prices. We consider six categories of factors (supply, demand, financial market, commodities market, speculative, and geopolitical) and test their significance in the context of estimating various forecasting models. We find that the Least Absolute Shrinkage and Selection Operator (LASSO) regression method provides significant improvements in the forecasting accuracy of prices compared to alternative benchmarks. Relative to the no-change and futures-based models, LASSO forecasts at the 8-step ahead horizon yield significant reductions in Mean Squared Prediction Error (MSPE), with MSPE ratios of 0.873 and 0.898, respectively. We also document substantial improvements in forecasting performance of the factor-based model that employs only a subset of variables chosen by LASSO. Finally, the time-varying nature of the relationship between factors and oil prices is used to explain recent movements in crude oil prices.     

Multiscale entropy analysis of crude oil price dynamics

Price formation in crude oil markets is the result of the action of many participants (e.g., producers, governments, speculators, etc.) whose effects are perceived at different time scales, from days to years. The diversity of participants as well as the occurrence of extreme socio-political events yields a market with complex price evolution. This paper uses entropy methods to monitor the evolution of crude oil price movements. As the complexity of the price can depend of the time horizon, entropy computations are performed for different time scales via low-pass filtering of the price difference dynamics. The results are interpreted in term of relative market efficiency concepts in the sense that high entropy values should be related to a more complex and, hence, less predictable market evolution. It is shown that the highest market efficiency is found for small time scales up to one or two weeks. The multiscale entropy pattern for high time scales, longer than one quarter, is interesting as it shows alternating periods of high and low entropy levels. Interestingly, this alternating pattern has a dominant spectral component of about 4.3 years, which could be related to macroeconomic (Kitchin) business cycles. It is shown that U.S. recessions in the recent 25 years are coincident with periods of reduced entropy levels, meaning that during economic downturn the long-run market complexity is drastically reduced. The possible effects of extreme events (e.g., Iraq War) are analyzed in terms of the relative market efficiency, suggesting that some events have affected the short-term but not the long-term market complexity. Overall, these results show that methods based on entropy concepts can shed light on the structure of crude oil markets as well as on its link to macroeconomic conditions and socio-political extreme events.     

Another perspective on gasoline price responses to crude oil price changes

The effects of oil price volatility on the responses of gasoline prices to oil price shocks have received little attention in discussions on the relationship between the prices of crude oil and gasoline. In this paper we consider such effects by using a bivariate structural vector autoregression which is modified to accommodate GARCH-in-mean errors. Our measure of oil price volatility is the conditional variance of the oil price–change forecast error. We isolate the effects of volatility in the price of oil on the price of gasoline and employ simulation methods to calculate nonlinear impulse response functions (NIRFs) to trace any asymmetric effects of independent oil price shocks on the conditional means of gasoline prices. We test whether the relationship between the prices of crude oil and gasoline is symmetric using tests of the null hypothesis of symmetric impulse responses. Based on monthly U.S. data over the period from 1978:1 to 2014:11, our empirical results show that gasoline prices respond asymmetrically to positive and negative oil price shocks. We also find that oil price volatility has a positive effect on the price of gasoline and it contributes to the asymmetries in the transmission of oil price shocks.     

Investigating price clustering in the oil futures market

Price clustering can be a source of market inefficiency. It follows that searching for price clustering in markets have gone beyond share prices into real estate, interest rate, and exchange rate markets. In this paper, we extend this line of research to oil futures markets. In particular, we consider five different forms of oil futures contracts and test for evidence of price clustering. Our results reveal strong presence of price clustering in the oil futures market. This finding implies that price clustering can potentially be a source of oil market inefficiency, which can influence trading strategies.     

Option prices and implied volatility in the crude oil market

This paper studies the determinants of WTI crude oil call option prices with a special emphasis on the relationship between implied volatility and moneyness. Our first-stage regression estimates a quadratic approximation of implied volatility as a function of moneyness, while our second-stage regression investigates correlations between the estimated parameters and a list of explanatory variables. The first-stage regressions show a positive coefficient on the quadratic term, suggesting that the market exhibits ‘Implied Volatility Smile’ and hence violates the Black-Scholes predictions. The main results of our paper concern the determinants of these violations. We find that the curvature of implied volatility as a function of moneyness is: (i) positively and significantly correlated with basis and hedging pressure of the underlying crude oil futures contract (ii) positively and significantly correlated with various measures of transaction costs on the options market. We explore various explanations for these results. The paper also contains a variety of robustness checks, mostly related to the assumed functional forms.     

Timing strategy performance in the crude oil futures market

The rewards to speculative trading in the crude oil futures market are assessed. For investors who adopt timing strategies that maximise their (iso-elastic) utility during each trading session, the rewards can be economically significant providing that transaction costs are small. Moreover, we are able to show via a decomposition of performance that the bulk of this benefit is due to their ability to predict realised volatility (that is, the second realised moment). The benefits derived from predicting other realised moments either require unrealistic levels of skill (all odd moments) or an infeasible degree of risk aversion (the fourth moment and higher even moments).     

Forecasting short-run crude oil price using high- and low-inventory variables

Since inventories have a lower bound or a minimum operating level, economic literature suggests a nonlinear relationship between inventory level and commodity prices. This was found to be the case in the short-run crude oil market. In order to explore this inventory–price relationship, two nonlinear inventory variables are defined and derived from the monthly normal level and relative level of OECD crude oil inventories from post 1991 Gulf War to October 2003: one for the low inventory state and another for the high inventory state of the crude oil market. Incorporation of low- and high-inventory variables in a single equation model to forecast short-run WTI crude oil prices enhances the model fit and forecast ability.     

Forecasting crude oil price volatility via a HM-EGARCH model

This paper compares uni-regime GARCH-type models, GARCH-type models with Markov and hidden Markov (HM) switching regimes on their forecasting abilities in WTI and Daqing crude oil markets, respectively. Empirical results indicate a HM-EGARCH model outperforms the competitive models, namely the regular GARCH-type models and Markov regime-switching models as well as the other models with hidden Markov regimes through results of six loss functions and the superior predictive ability (SPA) test. More significantly, we find the HM-EGARCH not only performs well in developed crude oil markets, but also in emerging crude oil markets. Therefore, the HM-EGARCH model can be regard as an effective measure of volatility when accounting for different volatility states in the time-changing process.     

Regime-switching stochastic volatility: Evidence from the crude oil market

This paper incorporates regime-switching into the stochastic volatility (SV) framework in an attempt to explain the behavior of crude oil prices in order to forecast their volatility. More specifically, it models the volatility of oil return as a stochastic volatility process whose mean is subject to shifts in regime. The shift is governed by a two-state first-order Markov process. The Bayesian Markov Chain Monte Carlo method is used to estimate the models. The main findings are: first, there is clear evidence of regime-switching in the oil market. Ignoring it will lead to a false impression that the volatility is highly persistent and therefore highly predictable. Second, incorporating regime-switching into the SV framework significantly enhances the forecasting power of the SV model. Third, the regime-switching stochastic volatility model does a good job in capturing major events affecting the oil market.     

A multi-granularity heterogeneous combination approach to crude oil price forecasting

Crude oil price forecasting has attracted much attention due to its significance on commodities market as well as nonlinear complexity in prediction task. Combining forecasts in different granular spaces, we propose a multi-granularity heterogeneous combination approach to enhance forecasting accuracy in the study. Firstly, we introduce various feature selection techniques including filter, wrapper and embedded methods, to identify key factors that affect crude oil price and construct different granular spaces. Secondly, distinct feature subsets distinguished by different feature selection methods are incorporated to generate individual forecasts using three popular forecasting models including Linear regression (LR), Artificial neural network (ANN) and Support vector machine (SVR). Finally, the final forecasts are obtained by combining the forecasts from individual forecasting model in each granular space and the optimal weighting vector is achieved by artificial bee colony (ABC) techniques. The experimental results demonstrate that the proposed multi-granularity heterogeneous combination approach based on ABC can outperform not only individual competitive benchmarks but also single-granularity heterogeneous and multi-granularity homogenous approaches.     

A deep learning ensemble approach for crude oil price forecasting

As crude oil price is influenced by numerous factors, capturing its behavior precisely is quite challenging, and thus leads to the difficulty of forecasting. In this study, a deep learning ensemble approach is proposed to deal with this problem. In our approach, two techniques are utilized. One is an advanced deep neural network model named stacked denoising autoencoders (SDAE) which is used to model the nonlinear and complex relationships of oil price with its factors. The other is a powerful ensemble method named bootstrap aggregation (bagging) which generates multiple data sets for training a set of base models (SDAEs). Our approach combines the merits of these two techniques and is especially suitable for oil price forecasting. In the empirical study, the WTI crude oil price series are investigated and 198 economic series are used as exogenous variables. Our approach is tested against some competing approaches and shows superior forecasting ability that is statistically proved by three tests.     

Monthly crude oil spot price forecasting using variational mode decomposition

Crude oil is one of the most important trade commodities in the world and its price fluctuation has significant effects on global economic activities. In this paper, we proposed hybrid models for monthly crude oil price forecasting using variational mode decomposition and artificial intelligence (AI) techniques (in this paper, support vector machine optimized by genetic algorithm (GASVM) and back propagation neural network optimized by genetic algorithm (GABP) are employed for analyzing). In addition, influencing factors of the long-term crude oil price such as the global crude oil production as well as economic activity (Dow Jones Industrial Index is considered in this paper) are investigated and considered on the crude oil price forecasting. Empirical forecasting results of monthly West Texas Intermediate (WTI) and Brent crude oil spot prices validate that the hybrid VMD-based models are superior to previously popular EEMD-based models and single models in terms of both level and directional prediction accuracies as well as the DM test results. In addition, the VMD-AI based models which consider influencing factors of the long-term crude oil price variation perform better than that without considering influencing factors of the long-term crude oil price variation in terms of MAPE and RMSE. All of which confirm that the newly proposed VMD-AI based models are promising tools for crude oil price analysis and forecasting.     

Forecasting excess stock returns with crude oil market data

In this paper, we forecast excess stock returns of S&P 500 index from January 1997 to December 2012 using both well-known traditional macroeconomic indicators and oil market variables. Based on a dynamic model selection approach, we find that the forecasting accuracy can be improved after adding oil variables to the traditional predictors. The forecasting gains relative to the benchmark of historical average are statistically and economically significant. Moreover, time-varying parameter models generate more accurate forecasts than constant coefficient models.     

The effect of uncertainty and aggregate investments on crude oil price dynamics

This paper is a study of the dynamics of the oil industry and we derive a mean reverting process for the crude oil price. Oil is supplied by a market leader, OPEC, and by an aggregate that represents non-OPEC producers. The non-OPEC producers take the oil price as given. The cost of non-OPEC producers depends on past investments. Shifts in these investments are influenced by costs of structural change in the construction industry. A drop in the oil price to below a given level triggers lower investments, but if the oil price reverts back to a high level investments may not immediately expand. In an uncertain oil demand environment cost of structural change creates a value of waiting to invest. This investment behaviour influences the oil price process.     

The impact of price variation on the production of crude oil and natural gas

This paper is directed at examining the impact of changing prices on the level of production of crude oil and natural gas in the United States. By using a cross correlation test for unidirectional causality, it is clearly demonstrated that for both crude oil and natural gas, domestic production is affected by changing prices. The implications are clear. The decontrol of the price of crude oil and the deregulation of natural gas prices will lead to additional production in the near term.