West Texas Intermediate Oil Prices Research Articles

Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis

This study examines the Granger-causal relationships between oil price movements and global stock returns by using time-varying Granger-causality tests in mean and in variance. We use the daily returns from Morgan Stanley Capital International (MSCI) G7 and the MSCI Emerging Stock Market Indexes to distinguish between the effects of daily oil price movements on G7 countries’ and emerging market countries’ stock markets. We further divide the emerging markets into two groups as oil-exporting and oil-importing countries. For the oil market, we use both the West Texas Intermediate (WTI) and Brent oil daily price movements. While the Granger-causality-in-mean tests indicate a causal link from WTI oil prices and G7 countries’ stock returns to MSCI emerging countries’ stock returns, the Granger-causality-in-variance tests suggest no causal link from global oil market prices to stock market returns. Nonetheless, a causal link from the G7 countries’ stock returns to the MSCI emerging countries’ stock returns is detected. In addition, G7 countries’ stock market volatility is found to Granger-cause Brent oil price volatility. The time-varying Granger-causality-in-mean and Granger-causality-in-variance tests present new and further insights. A causal relationship between oil price changes and G7 countries’ stock returns is found for some periods during and after the global financial crisis. Time-varying Granger-causality-in-variance test results indicate evidence of causal linkages among oil prices and global stock market returns that are specific only to certain time periods. We also find that there might be a difference between the movements in Brent and WTI oil prices with respect to their Granger-causal effects on oil-importing emerging markets’ stock returns—especially after the global financial crisis. Our results provide further evidence that the effects of oil price movements on stock returns might be different depending on the volatility in the stock markets.

Oil Market Efficiency under a Machine Learning Perspective

Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

Selected methods of securing the refining sector against crude oil price fluctuations

Abstract The contemporary refining sector has to contend with many types of risks, among which price risk is considered to be the foremost. Moreover, refineries define it as a commodity risk and identify it with both opportunities and threats carried by changes in prices of crude oil and products of refining. In this paper, we present selected methods that may protect enterprises in the refinery sector from the consequences of rapid fluctuations in oil prices. The focus is mostly on the opportunities offered by commodity options. Skillful combination of the above-mentioned derivatives in optional strategies enables hedging of the purchase prices of raw materials within fixed price ranges. In order to examine the effectiveness of using these strategies, the parameters of the commodity options from the New York Mercantile Exchange are utilized. The analysis of the West Texas Intermediate (WTI) crude oil prices covers the period from June 2014 to March 2018. Three different strategies from the vertical spread group have been taken into consideration, namely, short butterfly spread, long strip, and long strap. European call and put options with different strike prices have been used in the construction of these strategies. The comparison of the results achieved in the research indicates that there is an answer to the question of strategies that ought to be used at various levels of oil price changes. Moreover, the empirical results reveal that during rapid fluctuations in crude oil price (<10% month on month [MOM]), the median of most variants (80%) for the three considered strategies was positive. Furthermore, 70% of variants gave positive results, with price changes between 5% and 10%, whereas for price fluctuations of >5%, the strategies turned out to be an ineffective protection. The best results with rapid fluctuations in oil prices were obtained in the long strip strategy. Additionally, increasing the exercise price of options used in this strategy improved the mean for the final results. The higher exercise prices of the options also resulted in greater sensitivity of the effectiveness of the long strip strategy on the level of changes in oil prices. For the strategy variant with the At The Money (ATM)+10% options, the Pearson’s correlation coefficient between the final result and the WTI oil prices in the analyzed period amounted to –0.91. For variants with the ATM+5% and ATM options, the value of this coefficient was –0.85 and –0.71, respectively. It is also worth noting that the consequence of increasing strike price in the long strip strategy was higher standard deviations for the final results. The empirical results might be useful information for oil refineries. It can help refineries to create a more successful price risk management policy, which may thus protect the companies from the negative consequences of unfavorable crude oil price movements.

The US Shale Gas Revolution and Its Externality on Crude Oil Prices: A Counterfactual Analysis

The expansion of shale gas production since the mid-2000s which is commonly referred to as “shale gas revolution” has had large impacts on global energy outlook. The impact is particularly substantial when it comes to the oil market because natural gas and oil are substitutes in consumption and complements and rivals in production. This paper investigates the price externality of shale gas revolution on crude oil. Applying a structural vector autoregressive model (VAR) model, the effect of natural gas production on real oil price is identified in particular, and then based on the identification, counterfactuals of oil price without shale gas revolution are constructed. We find that after the expansion of shale gas production, the real West Texas Intermediate (WTI) oil price is depressed by 10.22 USD/barrel on average from 2007 to 2017, and the magnitude seems to increase with time. In addition, the period before shale gas revolution is used as a “thought experiment” for placebo study. The results support the hypothesis that real WTI oil price can be reasonably reproduced by our models, and the estimated gap for oil price during 2007–2017 can be attributed to shale gas revolution. The methodology and framework can be applied to evaluate the economic impacts of other programs or policies.

A realistic and integrated model for evaluating oil sands development with Steam Assisted Gravity Drainage technology in Canada

Oil sands resource is the third largest oil reserve, and 70% of the oil sands are in Canada. The emerging of Steam Assisted Gravity Drainage technology has made the commercial development of oil sands economically feasible. However, with the recent oil price crash, the development of oil sands has faced an uphill battle. A realistic and integrated evaluation model is highly needed to overcome difficulties from the low oil price and provide meaningful and valuable information for operators as well as investors to make the right decision. The existing evaluation models have considerable limitations (i.e., the incapability of evaluating integrated oil sands development with Steam Assisted Gravity Drainage technology, the lack of investigation into the input parameters with historical data, and ignoring the effect of the subsurface reservoir, etc.). This study developed an integrated evaluation model through the analyses of a significant amount of actual historical data. This integrated model includes six subcomponent models, ranging from the subsurface reservoir to infield flowline. Data from 35 Canadian oil sands development with 15 internal and external parameters were collected and investigated. The oil price effect was analyzed and quantified through simulations. The breakeven Western Canada Select oil price of $39/bbl. (equal to the West Texas Intermediate oil price of $55/bbl.) and other key price points and distributions were identified. Also, the top seven geological and technical parameters that contribute 86% of the net present value variations were identified and investigated. These quantified effects of external and internal parameters are useful findings for decision making. Considering various price scenarios and uncertainties of the input parameters, this study has concluded that developing oil sands resources with the Steam Assisted Gravity Drainage technology is economically feasible and socially beneficial after we take all stakeholder interests into consideration, which is true even with the sustainable and realistic oil price in the foreseeable future.

Detecting Co-Movements in Noncausal Time Series

This paper introduces the notion of common noncausal features and proposes tools to detect them in multivariate time series models. We argue that the existence of co-movements might not be detected using the conventional stationary vector autoregressive (VAR) model as the common dynamics are present in the noncausal (i.e. forward-looking) component of the series. In particular, we show that the presence of a reduced rank structure allows to identify purely causal and noncausal VAR processes of order two and higher even in the Gaussian likelihood framework. Hence, usual test statistics and canonical correlation analysis can still be applied, where both lags and leads are used as instruments to determine whether the common features are present in either the backward- or forward-looking dynamics of the series. The proposed de finitions of co-movements also valid for the mixed causal-noncausal VAR, with the exception that an approximate non-Gaussian maximum likelihood estimator is necessary for these cases. This means however that one loses the bene fits of the simple tools proposed in this paper. An empirical analysis on European Brent and U.S. West Texas Intermediate oil prices illustrates the main fi ndings. Whereas we fail to fi nd any short run co-movements in a conventional causal VAR, they are detected in the growth rates of the series when considering a purely noncausal VAR.

Oil Market Efficiency Under a Machine Learning Perspective

Forecasting commodities and especially oil prices has attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables often used in the relevant literature and through a selection process we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

Oil price changes and stock market returns: cointegration evidence from emerging market

The aim of this paper is to examine whether changes in nominal oil prices (Brent and West Texas Intermediate (WTI)) affect the stock market returns in the context of an emerging market framework. The Autoregressive Distributed Lag bounds testing approach of cointegration is used to test for the long run relation between the two variables, where the daily stock market index return is calculated using the first difference in the natural logarithms of stock market index. Further, we test for the stability of the cointegration relationship by examining the sensitivity analysis where diagnostic tests for serial correlation (namely the Breusch–Godfrey serial correlations LM test) and cumulative sum of recursive residuals (CUSUM) are employed. Using daily data from January 3, 2000 to December 9, 2015, the findings suggest that there is long run integration between oil prices and stock returns series in which the daily oil price shocks have a negative impact on stock returns. The highly significant error correction coefficient indicates high rate of convergence to equilibrium. In addition, the Toda and Yamamoto (J Econom 66(2):225–250, 1995) Granger non‐causality test indicates significant bidirectional causality between stock market returns and Brent nominal oil price, meanwhile there is unidirectional causality running from WTI oil price to stock market returns. These findings are, up to some extent, meaningful for investors, portfolio managers and policy makers.

The Impact of the US Purchasing Manager’ Index on Stock Returns in Korea

The purpose of this paper is to investigate the impact of the US Purchasing Manager’s Index (PMI) on stock returns in Korean industries. A Vector Error Correction Model (VECM) is used to analyze the dynamic inter-relationship which exists among the stock indices of 6 Korean industries, KOSPI index in Korea, S&P 500 index, West Texas Intermediate oil price and PMI in the US during the period from January 1993 to December 2015. The results show that an increase in the US PMI gives a positive impact as an increase in the stock index returns which is consistent with previous literature. In particular, the electric and electronics industry had the largest response in terms of stock returns in the six industries to the impulse of the US PMI. The study contributes to the extant literature by establishing that the US PMI exerts significant influence on stock returns of Korean industries.

Causality patterns for Brent, WTI, and Argus oil prices

ABSTRACTCausality patterns are analysed for daily Brent, West Texas Intermediate (WTI), and Argus Sour Crude Index (Argus) oil prices, Argus is the reference price for exports from Saudi Arabia, Kuwait and Iraq. Nonparametric Granger causality testing uncovers bi-directional causal links between Brent and WTI prices at multiple lags. Unidirectional causality from both Brent to Argus and WTI to Argus is also documented. If the current Saudi Arabia attempt to increase market share is successful, variations in Argus prices may start preceding movements in Brent and WTI, also.

Volatile oil and the U.S. economy

Volatile oil prices have been an interesting topic for both market participants and policy makers. While several studies have attempted to test the interaction between oil price shocks and the economy, there is limited research evaluating the impact of oil price volatility on the U.S. economy. This study attempts to provide a relatively complete picture through testing for the interactions between oil prices, macroeconomic variables and other shock variables commonly used in the literature. In addition, we show that Brent oil prices contain more information in predicting macroeconomic variables than the West Texas Intermediate oil prices. Econometric methods utilized here help evaluate the interactions from the short-run, long-run and volatility transmission perspectives. Our results show that not only oil prices significantly impact the U.S. economy, but the volatility of those prices also spillover to a significant portion of the macroeconomic variables.

The interactions between agricultural commodity and oil prices: an empirical analysis

Th e purpose of the study is to analyse the short and long-term relationships between the world oil prices (Europe Brent Spot Price and West Texas Intermediate Spot Price) and the agricultural commodity prices (Wheat, Corn and Soybeans). Th e analysis is based upon the data set covering the monthly period of 1990.01–2014.05. According to the Johansen co-integration tests results, there are no long-run relationships between each agricultural commodity prices and world oil prices at the 5% signifi cance level. On the other hand, according to the results of the Granger causality tests, there are uni-directional causality relationships from the Europe Brent and West Texas Intermediate oil prices to Wheat at the 1% and 5% signifi cance level respectively, to Corn at the 1% and 1% signifi cance level respectively and to Soybeans at the 1% and 5% signifi cance level respectively. No causality relationship from the agricultural commodity prices to world the oil prices has been observed.

Utilizing Topographic Finance to Understand Volatility

Visual representation methods are a common problem in econometrics and finance in order to describe system dynamics. In this paper we address this problem by using the bi-harmonic oscillation process and the Brownian motion components, to generate a three-dimensional volatility surface.The empirical analysis have been carried out on the S&P500 Index, the 10-year US Treasury Rates, and the West Texas Intermediate oil price, by using 85 daily closing observations, at the purpose to show how visualization of volatility can help understand longitudinal movements of price within different asset classes.

Oil prices and MENA stock markets: new evidence from nonlinear and asymmetric causalities during and after the crisis period

This article investigates the potential of nonlinear causal relationships between world oil prices and stock markets in Middle East and North Africa (MENA) countries during a black swan period that is characterized by rarity and devastating impacts. Our study is carried out using the daily data for 11 MENA countries over the period from 2 July 2007 to 27 August 2012. By using the nonlinear and asymmetric causality test of Kyrtsou and Labys (2006), we mainly find that: (i) the oil prices and MENA stock markets interact in a nonlinear manner; (ii) the signs of changes in the causing variables are important for detecting the true causality links between the variables and (iii) the nonlinear causality is more pronounced in the case of the Brent than West Texas Intermediate oil prices.

FORECASTING CRUDE OIL PRICE MOVEMENTS WITH OIL‐SENSITIVE STOCKS

This paper uses monthly data from 1984:M10 to 2012:M8 to show that oil‐sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (1‐month‐ahead predictions), using both in‐ and out‐of‐sample tests. In particular, the forecasts based on oil‐sensitive stock price indices are able to outperform significantly the no‐change forecasts. For example, using the NYSE Arca (AMEX) oil index as a predictor, the 1‐month‐ahead forecasts for nominal crude oil prices reduce the mean squared prediction error by between 22% (for the West Texas Intermediate oil price) and 28% (for the Dubai oil price). Moreover, we find that the directional forecast based on the AMEX oil index is significantly better than a 50:50 coin toss. The novelty of this analysis is that it proposes a new and valuable predictor that both reflects timely market information and is readily available for forecasting the spot oil price.(JEL G17, Q43, Q47, C53)

Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks

This paper uses monthly data from 1984:M10 to 2012:M8 to show that oil-sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (one-month-ahead predictions), using both in- and out-of-sample tests. In particular, the forecasts based on oil-sensitive stock price indices are able to outperform significantly the no-change forecasts. For example, using the NYSE Arca (AMEX) oil index as a predictor, the one-month-ahead forecasts for nominal crude oil prices reduce the mean squared prediction error by between 22% (for the West Texas Intermediate oil price) and 28% (for the Dubai oil price). Moreover, we find that the directional forecast based the AMEX oil index is significantly better than a 50:50 coin toss. The novelty of this analysis is that it proposes a new and valuable predictor that both reflects timely market information and is readily available for forecasting the spot oil price.

The High-Frequency Response of Energy Prices to Monetary Policy: Understanding the Empirical Evidence

This paper examines the impact of conventional and unconventional monetary policy on energy prices, using an event study with intraday data. Three measures for monetary policy surprises are used: 1) the surprise change to the current federal funds target rate, 2) the surprise component to the future path of policy, and 3) the unanticipated announcements of future large-scale asset purchases (LSAPs). Estimation results show that monetary policy news has economically important and highly significant effects on the level and volatility of energy futures prices and their trading volumes. I find that, on average, a hypothetical unanticipated 100 basis point hike in the federal funds target rate is associated with roughly a 3 percent decrease in West Texas Intermediate oil prices. I also document that, in a narrow window around the Federal Open Market Committee meeting, the Federal Reserve’s LSAP1 and LSAP2 programs have a cumulative financial market impact on crude oil equivalent to an unanticipated cut in the federal funds target rate of 155 basis points. Monetary policy affects oil prices mostly by affecting the value of the U.S. dollar exchange rate. Intraday energy prices also respond to news announcements about the U.S. macroeconomy and inventories. The daily responses are never significant, except in the case of inventory news.

Comments: Conventional Economics

Robust forecasts for natural gas growth from conventional and unconventional sources continue to see a smooth shift to a world in which gas is the dominant fuel of choice by industry and consumers. But market forces may have a say on how quickly the world gets there. A new report examining the top 10 growth markets in energy, air, and water puts shale gas development at the head of the list. The McIlvaine consultancy sees a continuing increase in investment in US shale gas projects, as well as in other promising areas such as China, Argentina, and Europe. Shale is followed on the list of growth industries by vessel air and water treatment, water reuse, NOx control, aquaculture, power plant efficiency improvements, soil and groundwater remediation, fine particulate reduction technology, solid waste management, and renewable energy. But questions are being raised about the rapid rise of gas in Europe, where gas prices are tied closely tied to oil prices. With the price of Brent comfortably above USD 100/bbl, at least for now, and a good USD 20/bbl higher than the US West Texas intermediate oil price benchmark, power generators and industries are becoming increasingly cautious about the finances of increased gas usage. The US International Energy Agency speaks of a “golden age of gas,” in which European electricity generation alone will increase 40% by 2015. And many market forecasts have predicted huge capital outlays in gas infrastructure across Europe in the near future. The Netherlands, for instance, has plans for a network of pipelines and liquefied natural gas (LNG) terminals that would allow it to become of the region’s gas hubs. But recent demand has been softer than expected because gas, at current price levels, is less economic than coal. That, in turn, has raised questions about the need for huge outlays for infrastructure if there is uncertainty about the long-term attractiveness of gas, including new pipelines or terminals to import LNG from producers in Qatar and Nigeria. European government initiatives to promote energy efficiency also could have an effect. In the US, it is low gas prices that are taking a toll. Producers with strong ties to US gas production are “losing their shirts,” ExxonMobil Chief Executive Officer Rex Tillerson said in releasing his company’s second-quarter earnings. ExxonMobil has yet to see the benefit of buying US gas producer XTO 2 years ago. Chesapeake, Noble, Encana, Anadarko, and others all have suffered because of sustained low gas prices in the US caused, in part, by the success of shale plays. Shale gas has quickly become a place not to sink additional capital, and many producers have shifted their attention toward liquids production instead. What would help is a sooner-rather-than-later ramping up of new LNG export facilities in the US, which would allow producers to take advantage of more attractive markets such as Asia and which would eventually firm prices in the US. Until that hap-pens, US gas prices at Henry Hub will remain under pressure because US gas is not tradable on the open market. Whether the public and politicians can agree to exporting US energy resources after seeing the country finally lessening its dependence on foreign sources is another question.

An empirical model of daily highs and lows of West Texas Intermediate crude oil prices

There is a large collection of literature on energy price forecasting, but most studies typically use monthly average or close-to-close daily price data. In practice, the daily price range constructed from the daily high and low also contains useful information on price volatility and is used frequently in technical analysis. The interaction between the daily high and low and the associated daily range has been examined in several recent studies on stock price and exchange rate forecasts. The present paper adopts a similar approach to analyze the behaviour of the West Texas Intermediate (WTI) crude oil price over a ten-year period. We find that daily highs and lows of the WTI oil price are cointegrated, with the error correction term being closely approximated by the daily price range. Two forecasting models, one based on a vector error correction mechanism and the other based on a transfer function framework with the range taken as a driver variable, are presented for forecasting the daily highs and lows. The results show that both of these models offer significant advantages over the naïve random walk and univariate ARIMA models in terms of out-of-sample forecast accuracy. A trading strategy that makes use of the daily high and low forecasts is further developed. It is found that this strategy generally yields very reasonable trading returns over an evaluation period of about two years.

Non-Linear Oil Price Dynamics: A Tale of Heterogeneous Speculators?

Abstract While some of the recent surges in oil prices can be attributed to a robust global demand at a time of tight production capacities, commentators occasionally also blame the impact of speculators for part of the price pressure. We propose an empirical oil market model with heterogeneous speculators. Whereas trend-extrapolating chartists may tend to destabilize the market, fundamentalists exercise a stabilizing effect on the price dynamics. Using monthly data for West Texas Intermediate oil prices, our STR-GARCH estimates indicate that oil price cycles may indeed emerge due to the non-linear interplay between different trader types.