Texas Intermediate Crude Oil Price Research Articles

Testing the unit root of social, environmental, and governance stock price indexes using a multivariate ARDL model

ABSTRACT The establishment of a green stock market (GSM) has significant implications for the advancement of sustainable development. However, it is susceptible to fluctuations caused by fluctuations in fossil fuel prices, economic policy and geopolitical uncertainties, and monetary policy. This study evaluates the persistence of shocks to five social, environmental, and governance (ESG) stock price indexes using the novel multivariate ARDL unit root test. The Federal Fund Effective Rate (FFER) and West Texas Intermediate Crude Oil Price (WTIOIL) were separately included as covariates in the ARDL model and tested for the null hypothesis of a unit root using a multivariate ARDL unit root test. The results of this study indicate that the ESG stock price indexes exhibit unit root behaviour, suggesting that policy shifts and shocks have a lasting impact on the dynamics of the green stock market and sustainable development.

Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.

Application of the geometric Brownian motion model in West Texas Intermediate crude oil price prediction

Crude oil is one of the primary commodities in the global economy. Crude oil prices are among the most complex and challenging to model because of the irregular, non-linear, non-stationary fluctuations in crude oil prices and their high volatility. It is essential to predict crude oil commodity prices to reduce the negative impact of fluctuations in crude oil commodity prices. Several mathematical models can be used to forecast crude oil commodity prices. One model that can be used is the Geometric Brownian Motion model, also known as the Wiener process. In this research, predictions for WTI (West Texas Intermediate) crude oil in 2022 were carried out using the Geometric Brownian Motion model. The results of this research are predictions of crude oil prices for July 2023 with iterations of 100, 200, and 1000, respectively, producing MAPE values of 6.092415%, 7.364198%, and 7.276606%.

Projection of future carbon benefits by photovoltaic power potential in China using CMIP6 statistical downscaling data

Solar photovoltaic (PV) systems is an affordable solution that significantly contribute to climate adaptation and resilience, energy security and greenhouse gas abatement with respect to fossil fuel electricity generation. Currently, available studies on the benefits of PV power generation only consider the electricity consumption and do not account for the possible future benefits from carbon trading under the combined impacts of pollution emissions and socio-economic. In this study, the downscaling and bias correction were applied to the Coupled Model Inter-comparison Project Phase 6 (CMIP6) multi-model mean data based on the historical data from the China Meteorological Administration (CMA) stations. The corrected measurements of meteorology were used to explore the PV power potential and the environmental and economic benefits offset by solar power generation under SSP126, SSP245 and SSP585 in China during 2023–2100. We found that the annual mean PV power potential across mainland China ranged from 1 to 37 Wm−2 and demonstrated a decreasing trend in the Northwest China and an increasing trend in the Southeast China. Compared to thermal power generation, electricity from solar energy will counteract the total emissions of annual mean 139.54 × 105 t CO2, 1702 t SO2, 2562 t NO X and 3710 t dust in China in SSP126 scenario. The results of variable importance assessment showed that the West Texas Intermediate crude oil price (47.77%), coal price (41.76%), natural gas price (6.65%) and gross domestic product (2.44%) contribute the most to the carbon emissions allowances (CEAs) price. Against a ‘carbon peak’ background in China, the CEA price will reach 80 CNY/t CO2 by 2030 in China, with the carbon trading value potential ranging from 20 billion to 200 billion CNY of each pixel (10 km × 10 km) by 2030. This study would have important implications for the environmental construction and future investment and construction of PV systems in China.

Evaluating the Effect of Oil Price Volatility on Capital Expenditure of Federal Government in Nigeria

This study examined the effect of oil price volatility on federal government capital expenditure in Nigeria for the period 1993 to 2022 using secondary data from relevant government agencies. The study specifically examine the impact of Brent UK crude oil price volatility, OPEC spot rate crude oil volatility and West Texas Intermediate crude oil price volatility controlling the disruptions of oil subsidy, corruption and inflation on capital expenditure. The research employed an ex-post facto research design to produce results via Bounds test and Autoregressive Distributed Lag regression test. The long run estimate of the model report that Brent UK crude oil price volatility, OPEC spot rate crude oil volatility and West Texas Intermediate crude oil price volatility failed to report significant effect on the federal government capital expenditure in Nigeria. This shows that the oil price volatility is a short run phenomenon, that will fade out in short period, hence the reason for the high speed of adjustment of the error correction term. There is need for federal government in Nigeria to continually monitor crude oil international price and negotiate with OPEC on production output and quota. There is also need to monitor federal government’s capital expenditure pattern and revenues in critical revenue generating agencies. Federal Government should remain committed to sustaining fiscal adjustments by creating fiscal space for capital and infrastructural development.

Analyzing the volatility spillover and cointegration relationship between daily spot West Texas intermediate crude oil price and US dollar

In the study, it is aimed to analyze the diffusion and cointegration relationship between WTI and US Dollar in the period of 2016-2021. In the study, after a comprehensive literature review of the theoretical review, the econometric analysis section was started. In the first part of the analysis, the short and long-term relationships between the variables were examined with the autoregressive distributed lag methodology and the existence of a cointegration relationship was reached. According to the findings, the effect of WTI on foreign exchange volatility in the long run is statistically significant and negative. In the short-term evaluation, ECT is negative and significant within expectations. In this context, the changes between the variables approach the long-term equilibrium level. According to the results obtained in the causality and variance causality analyzes applied in the last part of the analysis, it is understood that there is a volatility spillover effect from WTI to foreign currency.

Examining contagion effects between global crude oil prices and the Southeast Asian stock markets during the COVID-19 pandemic

Many previous studies identify the contagion effect among various types of assets, defined as the increase in correlation of these assets during a financial or economic crisis. During the COVID-19 outbreak, a historic fall in global fuel demand and oil prices has been witnessed. Because crude oil has a strategic position among the export products of the Southeast Asian economies, even a tiny global oil price change leads to a plunge in these stock markets. This study addresses the spillovers of the volatility between the West Texas Intermediate crude oil prices and stock indices across six ASEAN emerging economies. Besides, the study examines whether a contagion connecting the global energy prices and these stock markets exists during the coronavirus pandemic. The empirical results are acquired by applying the Bayesian test for equality of means on the dynamic conditional correlations computed from DCC-GARCH models. The findings present positive volatility transmission from crude oil prices toward these emerging equity markets. During the health crisis, co-movements intensify, indicating the occurrence of contagion effects. The empirical results provide valid implications for policymakers and international investors because a precise volatility forecast is vital for managing portfolio risk. AcknowledgmentThis research is funded by University of Economics Ho Chi Minh City, Vietnam.

The impact of geopolitical risk on the behavior of oil prices and freight rates

The impact of geopolitical risk on energy markets has drawn attention to the need for better statistical modeling, especially of the crude oil markets and the shipping industry. In this work, the West Texas Intermediate crude oil price and the Baltic Dry Index behavior under the assumption of geopolitical risks are examined by using monthly data from January 1985 until May 2021. Using fractional integration methods, the results indicate that geopolitical risk and the Baltic Dry Index series will return to their original trends in the event of an exogenous shock, in contrast to the West Texas Intermediate behavior. These results are supported by analyzing the long-term relationship of the time series using the Fractional Cointegration Vector AutoRegressive approach. Finally, we use Bai and Perron (2003) and wavelet transform approaches to detect breaks in the prices paid for the maritime transport and for the crude oil prices caused by geopolitical risks.

Oil extraction and crude oil price behavior in the United States: a fractional integration and cointegration analysis

ABSTRACT This study reviews the relationship between the different types of oil extraction such as horizontal drilling or fracking, or directional drilling, which is a hybrid between vertical and horizontal, on the behavior of West Texas Intermediate crude oil prices. In doing so the study adds a new dimension to the literature on the relationship between oil price and extraction techniques. The analysis is based on statistical properties using the VAR model of Fractional Cointegration, reflecting evidence of cointegration between the series, and indicating a long-term equilibrium relationship. In addition, we apply the wavelet transform to analyze the structural changes in the price of West Texas Intermediate brought about by changes in drilling technology. Our results show that all three forms of extraction and West Texas Intermediate prices reach high levels of correlation, particularly around 2014. We conclude that a decrease in production based on any form of crude oil extraction leads to an increase in the price of crude oil.

Oil price explosivity and stock return: Do sector and firm size matter?

The paper examines whether the oil price series contains price explosivity and if price explosivity exists whether it offers excess return for oil-related, oil-substitute, and oil-user companies in US stock markets. Moreover, we investigate whether the size effect moderates the relationship between oil price explosivity and stock returns. We use monthly West Texas Intermediate crude oil prices between January 1986 and December 2019 and employ the Generalized Supremum Augmented Dickey-Fuller test to detect the price explosivity. Feasible Generalized Least Squares estimator is also implemented to capture the impact of oil price bubble on stock returns of US companies. The results indicate multiple episodes of price explosivity which mostly coincides with the 2008 financial crisis. The price explosivity leads to an excess return for oil-related companies; whereas, it negatively impacts oil-substitute and oil-user firms. However, the effect of oil price explosivity on stock returns is heterogeneous across size groups. The results provide key insightful information to policymakers and investors. Policymakers should prevent the occurrence of price explosivity to increase the efficiency of an oil futures market. Given the diverse impact of oil price explosivity on the stock return across sectors and sub-size groups, investors can maximize their profits by rebalancing their portfolio based on oil dependency and the firm's size.

Multifractal Analysis of the Impact of COVID-19 on NASDAQ, CIOPI, and WTI Crude Oil Market

In this paper, we explore the impact of COVID-19 on auto-correlations and cross-correlations among NASDAQ stock index of the USA, China iron ore price index (CIOPI), and West Texas Intermediate Crude Oil price (WTI). To find out the effect of COVID-19 on financial markets, we divide the investigated data series into two sub-periods, i.e., pre-COVID19 period and post-COVID19 period. First, multifractal detrended fluctuation analysis (MF-DFA) of those series shows a general trend of strong multifractality after COVID-19, indicating lower market efficiency after the pandemic shock. Second, multifractal detrended cross-correlation analysis (MF-DCCA) method is employed to examine cross-correlations among NASDAQ, CIOPI, and WTI. The three cross-correlations all increase in post-COVID19. The correlation between NASDAQ and CIOPI increases the most, becoming the strongest correlation among the three cross-correlations in post-COVID19. The surrogate procedure shows that the post-COVID19 cross-correlation multifractalities are mostly due to fat-tail distribution. Third, we use multi-scale multifractal analysis (MMA) to visualize the dynamic behaviors of correlations among the series. The Hurst surfaces of the three cross-correlations have more fluctuation, both at small and large scale in post-COVID19 than that of pre-COVID19. Particularly, the Hurst surface of cross-correlation between NASDAQ and CIOPI exhibits stronger multifractality during the outbreak of COVID-19 than that in both pre-COVID19 and post-COVID19. The above investigations provide helpful insights of relevant market trends.

Multiscale dependence, spillovers, and connectedness between precious metals and currency markets: A hedge and safe-haven analysis

In this study, we examine the frequency volatility spillovers, connectedness, and quantile dependence between precious metals futures (gold, palladium, platinum, and silver) and the main US foreign exchange rates of Australia, Canada, China, Eurozone, Japan, Switzerland, and the UK. We use them as a hedging tool for developed currency markets. We further investigate the key drivers of frequency spillovers, before and during COVID-19, and the property of precious metals as a hedge and safe haven against currency risk exposure. We performed the quantile coherency analysis and found that the dependence is positive among the currencies and negative between currencies and precious material in most cases. Using the time-frequency spillovers analysis, we show that gold and silver are net transmitters of spillover to the other markets; however, palladium is a net receiver of spillover for all three horizons. The EUR is a net contributor of risk regardless of the time horizon. Using the quantile regression model, we demonstrate that the effect of the West Texas Intermediate crude oil price (WTI), Economic Policy Uncertainty (EPU) index, volatility index (VIX), and USD index (USDX) on total spillover varies over quantiles in terms of magnitude and direction. Finally, we show that all precious metals can be used as a strong hedge against these major currencies.

Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?

Due to the expensive infrastructures and the difficulties in storage, the supply and demand for natural gas in China has experienced periodic fluctuations and geographical imbalance. The primary purpose of this study is to provide satisfying probability density forecasts using mixed-frequency dynamic factors to alleviate the imbalance risks between the supply and the demand. To this end, this paper develops a novel hybrid model named EEMD-MIDAS-SVR, based on ensemble empirical mode decomposition (EEMD) procedure, mixed data sampling (MIDAS) framework and support vector machine regression (SVR). The empirical results reveal that the mixed-frequency dynamic factors could provide powerful predictive information for natural gas demand. By the model confidence set (MCS) test, we identify the most predictive dynamic factors: gas industry index, electricity industry index, Daqing crude oil prices, Qinhuangdao steam coal prices, West Texas Intermediate crude oil prices, and Australia steam coal prices. They could improve forecasting accuracy by about 70% than benchmark models, particularly considering the nonlinearity of natural gas demand contributes to 21% roughly. Based on the MCS test, we provide the non-parametric bootstrap probability density forecasts for natural gas demand, which performs well in capturing the uncertainty of natural gas demand. These findings have several policy implications and practical value for natural gas pricing and carbon neutrality achieving in emerging markets.

Risk perception and oil and gasoline markets under COVID-19

The novel coronavirus (COVID-19) exposed individuals to a great uncertainty about its health and economic ramifications, especially in the early days and weeks of the outbreak. This study documents oil and gasoline market implications of individuals’ behavior upon such uncertainty by analyzing the relationship between Google search queries related to COVID-19—information search that reflects one's level of concern about the subject (risk perception)—and the performance of oil and gasoline markets during the pandemic. The empirical analysis based on daily data and a structural vector autoregressive model reveals that a unit increase in the popularity of COVID-19 related global search queries, after controlling for COVID-19 cases, results in 0.083% and 0.104% of a cumulative decline in Dow Jones US Oil & Gas Total index and New York Harbor Conventional Gasoline Regular spot price, respectively, after one day, 0.189% and 0.234% of a cumulative decline after one week, and 0.191% and 0.237% of a cumulative decline after two weeks. The reaction of Brent and West Texas Intermediate crude oil prices to the spike in COVID-19 related online searches is found to be statistically insignificant, which can be explained by oil price pass-through into gasoline spot price.

Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach

This study aims to analyze two important features of crude oil price data, namely, persistence and parameter instability. We apply an autoregressive fractionally integrated moving average model in which crude oil price persistence measured through the fractional integration parameter, conditional innovation variance and persistence change through Markov-switching dynamics. Model estimation is conducted using Gibbs sampling combined with data augmentation. Applied to monthly West Texas Intermediate crude oil price data from September 1859 to December 2017, we find evidence of four regimes. The main effect of regime switching is in the conditional variance and persistence of the innovations, but there is the possibility that regime switching also affects the fractional integration parameter. Across regimes crude oil price is very persistent with the order of integration estimated close to or slightly higher than one. Finally, discarding parameter instability leads to overestimation of the degree of persistence in the price of crude oil. It also results in inferior density forecasts.

Mean-Reverting Statistical Arbitrage in Crude Oil Markets

In this paper, we introduce the concept of statistical arbitrage through the definition of a trading strategy that captures persistent anomalies in long-run relationships among assets. We devise a methodology to identify and test mean-reverting statistical arbitrage, and to develop trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium, and mispricings arise when West Texas Intermediate crude oil price diverges from the statistical portfolio value. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is measured by three specific profit indicators on out-of-sample data. Lastly, we use a Monte Carlo simulation approach to develop a model for forecasting the expected Value at Risk of the adopted trading strategy over an established holding period.

Are mergers and acquisitions in the petroleum industry affected by oil prices?

ABSTRACTThis paper contributes to the literature on crude oil price behavior and examines how this affects mergers and acquisitions (M&A) in the petroleum industry in the US. The paper analyzes the relationship of these two series by studying its dynamic in the time–frequency domain. The novelty of this study’s approach lies in the application of wavelet tools for its resolution. Monthly data are used in this study, covering the period January 1980–June 2012. It was observed that there was a shift to higher frequencies of the wavelet coherency during the mid-1990s and the late 2000s. The results also indicate that during the mid-1990s and the late-2000s, an increase in M&A took place that was led by the increase in West Texas Intermediate crude oil prices.

Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction

This paper proposes an alternative approach based on a genetic algorithm and neural network (GA–NN) for the prediction of the West Texas Intermediate (WTI) crude oil price. Comparative simulation results suggested that the proposed GA–NN approach is better than the baseline algorithms in terms of prediction accuracy and computational efficiency. Mann–Whitney test results indicated that the WTI crude oil price predicted by the proposed GA–NN and the observed price are statistically equal. Further comparison of the proposed GA–NN with previous studies indicated performance improvement over existing results. The proposed model can be useful in the formulation of policies related to international crude oil price estimations, development plans and industrial production.

The high-frequency response of energy prices to U.S. 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 announcement of future large-scale asset purchases (LSAP). Estimation results show that monetary policy surprises have 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% decrease in West Texas Intermediate crude oil prices. I also document that, in a narrow window around the FOMC 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 156 basis points.

Reexamining the Relationship Between Oil Prices and the US Economy Using a Quantile Regression Approach

The United States (US) industrial product index (IPI) and West Texas Intermediate crude oil prices during 1986 to 2008 are analyzed to reexamine the relationship between oil prices and the US economy. A key feature of this study is an analysis of changing distribution of the US IPI growth rates over time by the quantile regression model and a meaningful comparison analysis with the results of the ordinary least squares estimates is examined. The following conclusions are drawn from our empirical results. First, while oil prices have a significant negative impact on the US economy over expansive conditions, the relationship between them is positive when the US economy is experiencing recession regimes. Second, the asymmetric relationship between oil prices and the US IPI growth rates identified by this study could satisfactorily explain the existing puzzle regarding the link between oil prices and the US economy among prior empirical studies.