West Texas Intermediate Crude Oil Research Articles

Extreme downside risk connectedness between green energy and stock markets

This study addresses the need to understand the transmission of tail risk across financial markets, especially in the context of green energy and stock markets. Utilizing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology combined with the Conditional Autoregressive Value-at-Risk (CAViaR) framework, the model analyzes data from September 30, 2013, to October 11, 2023. This study focuses on five major stock indices (S&P500, CSI300, Nikkei225, STOXX50, and FTSE100), West Texas Intermediate (WTI) crude oil, and three sectors within green energy markets (Green bond, Global Clean Energy, and Renewable Energy and Clean Technology), highlighting the significant role of these sectors in risk propagation. The model can capture dynamic changes and asymmetries in financial returns, thus providing a precise estimation of extreme downside risks. Key findings show that three stock indices (S&P500, STOXX50, and FTSE100) and one green energy sector (Renewable Energy and Clean Technology sectors) are the predominant sources of risk, with significant connectedness around events, such as the COVID-19 pandemic. These insights are crucial for developing effective risk-management strategies and supporting the transition to a sustainable energy sector. This study concludes that understanding these risk dynamics is essential for strategic planning and market stability.

Crude oil price behavior and the adoption of sustainable measures in companies before and after COVID-19

The objectives achieved in the Paris Agreement to reduce greenhouse gas emissions and reduce dependence on fossil fuels have caused, in recent years, a growing importance on sustainability in companies in order to reduce Environmental, social and economic impacts. This study is focused on understanding how the variation in West Texas Intermediate crude oil prices affects the Dow Jones Sustainability Index, and therefore the companies included in it, and vice versa. The research aims to examine the statistical properties of both indices, using fractional integration methods, the fractional cointegration vector autoregressive (FCVAR) approach and the continuous wavelet transform (CWT) technique. The results warn of a change in trend, with the application of extraordinary measures being necessary to return to the original trend, while the analysis of cointegration and wavelet analysis measures reflect that an increase in those adopted based on sustainability by the different companies that make up the index imply a drop in the price of crude oil.

Potential formation mechanism and prediction of crude oil price based on underdetermined independent component analysis

In most crude oil price prediction, a few of them explored the potential formation mechanism of oil price. This study proposes a novel multi-scale integrated (MSI) learning paradigm to mine the potential formation mechanism of the crude oil price and predict the crude oil price. Firstly, we propose a novel underdetermined independent component analysis (UICA) model based on extreme-point symmetric mode decomposition (ESMD) and independent component analysis (ICA) to study the potential formation mechanism of crude oil price. Based on the representative independent components (ICs), we found and analyzed some factors affecting the crude oil price from high frequency to low frequency, as well as the potential formation mechanism of crude oil price, such as geopolitical implications, natural disasters, historical pricing data and copper futures price. Moreover, the ICs are predicted and generated the final forecasting result of crude oil price via radial basis function neural network (RBFNN). The experimental results based on the West Texas Intermediate (WTI) crude oil price, confirm that the proposed MSI model is superior to the existing prediction models in terms of statistical and error performance criteria and Diebold-Mariano (DM) test with the optimal D stat = 99.0220 , MAE = 0.0050 , RMSE = 0.0537 , MAPE = 0.0116 , ESD = 0.0007 , R 2 = 0.9960 .

Energy price and returns: a threshold heteroscedasticity model approach

ABSTRACT The energy market is characterized by its intricate dynamics, influenced by various factors that result in erratic behavior, making it challenging to model energy price returns and volatility. In energy market, the changes in Brent and West Texas Intermediate (WTI) crude oil price yields and volatility are the focus of great interest and attention. Due to the influence of different periods and some economic factors, the fluctuation of crude oil price shows asymmetric characteristics. For exploring the asymmetric characteristics of energy market, we introduce the application of the threshold double autoregressive (TDAR) model to handle the complexities of energy price returns and volatility. The TDAR model is able to capture nonlinearities and regime shifts in the oil market, making it particularly suitable for understanding the inherent complexities in crude oil price movements. The empirical results implied that TDAR model demonstrates superior performance in capturing the intricate dynamics of crude oil prices compared to traditional asymmetric GARCH-type models, making it a valuable tool for market practitioners and policymakers. Our findings reveal the subtle movements in Brent and WTI prices and offer valuable insights for risk management and investment strategies, which provide an alternative for market practitioners and policymakers.

Trading Momentum in the U.S. Crude Oil Futures Market

This paper investigates if the Rate of Change (ROC), as a popular measure of momentum, can serve as a reliable technical analysis indicator to improve stock price prediction for U.S. West Texas Intermediate (WTI) crude oil futures market. The methodology centers on the application of the ROC and/or Moving Average (MA) price crossover/cross under strategies as a trading system. End of month futures prices of West Texas Intermediate (WTI) crude oil prices are collected for the period 28th May 2004 – 30th April 2024. The performance of the trading system is measured using both the Sharpe and Sortino ratios, thereby adjusting for total and downside risks. The model is also benchmarked against the naïve buy-and-hold strategy. Overall findings suggest a ROC based on 14-month periods outperform other momentum-based indicators, including when combined with price-crossover moving average strategies, and the naïve buy-and-hold strategy. After adjusting for the negative returns, the downside risk or semi-deviation amounted to 8.5%, and a Sortino value of 4.58. The Sortino value is however biased due to the 295% return witnessed in 2009. Findings have some vital policy implications for regulatory bodies and traders in the WTI crude oil energy futures market.

An Entropic Analysis of Efficiency in the West Texas Intermediate Crude Oil Futures Market

For the last 50 years or so, the efficient market hypothesis (EMH) has been the central pillar of economic thought and the building block of portfolio theory. However, the validity of the EMH still remains controversial, and the methods of testing for market efficiency have been criticised. This study utilises entropy analysis combined with generalised autoregressive conditional heteroscedasticity (GARCH) regressions to provide extensive empirical evidence on informational efficiency. This study develops an innovative method for studying market efficiency by investigating the complexity of information saturated in the market. To this end, we have used daily West Texas Intermediate (WTI) crude oil futures contracts from June 1983 to February 2016, giving a total data set of 282,427 observations. The results of the Jensen–Shannon informational criteria indicate that the informational efficiency measure has never reached the maximum value of 1 which indicates weak-form efficiency. Our study has further found that the informational efficiency measure tends to decrease consistently during recessionary periods and to increase in subsequent periods. We have also found that the entropy measure tends to increase over time, supporting the adaptive market hypothesis over the EMH.

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.

Forecasting the market value of power battery industry chain: A novel RRMIDAS-SVR model

The emergence of electric vehicles has contributed to mitigating air pollution and greenhouse effects caused by traditional fuel vehicles. The power battery industry chain, which is a primary component of electric vehicles, requires more attention to monitor its development status. This study proposes a novel method for forecasting the development status of the power battery industry chain by monitoring the market value index of all listed companies in the power battery industry. It proposes a new forecasting model, RRMIDAS-SVR, which outlines reverse-restricted mixed data sampling (RRMIDAS) into support vector regression (SVR) to end the data-driven challenges of mixed-frequency data and nonlinear relationships. We estimate the RRMIDAS-SVR model using a quadratic programming problem and mixed-frequency West Texas Intermediate crude oil futures prices, electric vehicle sales, and the consumer price index as predictors of the market value of all listed companies in the power battery industry chain. The experimental findings reveal that the RRMIDAS-SVR model outperforms the other models, as evidenced by its lower mean absolute error and root-mean-square error. This study contributes to understanding the development status of the power battery industry value chain by proposing and developing a new approach, RRMIDAS-SVR, to monitor the industry's development status that considers a multi-source information set. Moreover, this study provides strategic insights for stakeholders in the power battery industry.

Predicting successful trading in the West Texas Intermediate crude oil cash market with machine learning nature-inspired swarm-based approaches

Predicting successful trading in the West Texas Intermediate crude oil cash market with machine learning nature-inspired swarm-based approaches

Exploring the Relationship and Predictive Accuracy for the Tadawul All Share Index, Oil Prices, and Bitcoin Using Copulas and Machine Learning

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.

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.

Seeing is believing: Forecasting crude oil price trend from the perspective of images

AbstractIn this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image‐based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low‐frequency models for high‐frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends.

A combined model using secondary decomposition for crude oil futures price and volatility forecasting: Analysis based on comparison and ablation experiments

To accurately forecast crude oil futures price and volatility, this article presents a novel deep learning combined model using secondary decomposition with West Texas Intermediate crude oil futures (WTI) and North Sea Brent crude oil futures (Brent) as examples. Firstly, a trend subsequence and several noise subsequences are obtained by decomposing the crude oil futures price or volatility using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the secondary decomposition is performed on the highest frequency noise subsequence using the variational mode decomposition (VMD). Secondly, the remaining subsequences obtained from CEEMDAN and the subsequences obtained from VMD are predicted separately using the BiGRU-Attention-CNN model. Finally, the predicted crude oil futures price or volatility is calculated by linearly integrating the predicted values of each subsequence. The empirical analysis shows that the novel combined model using secondary decomposition proposed in this paper has the best forecasting performance among many models, both in the comparison experiments and in the ablation experiments. The model is also shown to have good robustness by predicting the volatility at different maturities, varying the ratio of the training set and the test set for crude oil futures price prediction, and predicting the price of crude oil futures after extreme events. Overall, the novel combined forecasting model using secondary decomposition proposed in this paper can help countries grasp the direction of the crude oil market and improve national economic and political security.

Interval prediction of crude oil spot price volatility: An improved hybrid model integrating decomposition strategy, IESN and ARIMA

Crude oil price volatility prediction are important for energy policymaking and investment risk avoidance, and have attracted a great deal of global attention. In recent years, although some studies have proposed methods for prediction crude oil price volatility, most of them are point prediction, which are difficult to provide richer reference information for managers. Therefore, an improved hybrid interval prediction model is proposed. The hybrid model combines data feature extraction and quantification techniques, fuzzy information granulation(FIG), improved echo state network (IESN), and autoregressive integrated moving average model (ARIMA). The hybrid model is able to accurately portray the different features present in the volatility of crude oil spot price. The features are quantified in order to merge similar feature components and improve the prediction effect. On this basis, IESN, which possesses strong nonlinear learning ability, is used to predict high-frequency sequences, and ARIMA, which has strong linear carving ability, is used to predict low-frequency sequences and residual terms. By “divide and conquer”, excellent prediction intervals are obtained. West Texas intermediate (WTI) crude oil spot and Brent crude oil spot are selected for analysis, and consider the influence of futures on interval construction. The effect of volatility intervals is validated from the pricing benchmark and time scale dimensions, respectively. The experimental results show that the proposed model has optimal performance in all scenarios. Take the performance of WTI on the daily scale as an example, its PICWC value is 0.8205. Compared with other benchmark models, it improves by 137.4504, 3.1118, 1.0506, and 0.4875, respectively. The proposed model provides a reliable new idea for crude oil spot volatility interval prediction.

Understanding Intraday Oil Price Dynamics during the COVID-19 Pandemic: New Evidence from Oil and Stock Investor Sentiments

This study employed intraday stock market and oil investor sentiment data related to news and social media (i.e., the Thomson Reuters MarketPsych Indices [TRMI] sentiment index) to gauge investors’ interest in the West Texas Intermediate (WTI) crude oil futures market during the recent health crisis. We proposed an original nonlinear empirical framework by considering oil price dynamics’ complexity and its potential interaction with investor sentiment. The analysis revealed three noteworthy findings. First, we observed evidence of nonlinearity in the relationship between excess returns on WTI crude oil futures and investor sentiment data. Second, the causality direction moved only from oil and stock market investor sentiment to oil returns. Third, the impacts of oil and stock market sentiment data on crude oil returns (i.e., volatility) were always negative. Furthermore, sentiment data related to social media showed a more pronounced cross-correlation than that of news.

Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns

This work presents a novel approach to determining the risk and return of crude oil stocks by employing Arbitrage Pricing Theory and Quantile Regression. Arbitrage Pricing Theory identifies the risk factors likely to impact crude oil returns. Subsequently, Quantile Regression estimates the relationship between the selected factors and the returns across different distribution quantiles. The West Texas Intermediate (WTI) crude oil price is used in this study as a benchmark for crude oil prices. WTI’s price fluctuations can significantly impact the performance of global crude oil stocks and, subsequently, the global economy. Various statistical measures are used in this study to determine the proposed model's stability. The results show that changes in WTI returns can have varying effects depending on market conditions and levels of volatility. This study emphasizes the influence of structural discontinuities on returns. These are likely generated by changes in the global economy and the unpredictable demand for crude oil during the pandemic. The inclusion of pandemic, geopolitical, and inflation-related explanatory variables adds uniqueness to the study as it considers current global events that can affect crude oil returns. Findings show that the key factors that pose significant risks to returns are industrial production, inflation, the global price of energy, the shape of the yield curve, and global economic policy uncertainty. This implies that while making investment decisions in WTI futures, investors should pay particular attention to these elements.

Connectedness between green bonds, conventional bonds, oil, heating oil, natural gas, and petrol: new evidence during bear and bull market scenarios

This study examines the spillovers between US green bonds, non-green bonds (10-year US Treasuries) and four major energy markets - West Texas Intermediate (WTI) crude oil, heating oil, natural gas, and petrol - during bear and bull market conditions. We combine the Diebold and Yilmaz (2012) spillover index and the quantile connectedness approach developed by Ando et al. (2022) in order to capture financial markets exhibiting higher potential risk and to assess their extreme dependence. The main results show a strong time-varying connectedness between focal asset classes during major crisis periods, including the oil price crash, the China-US trade conflict, the coronavirus crisis and the war between Russia and Ukraine. The network analysis reveals that WTI, heating oil and green bonds act as net transmitters of risk, while the other asset classes serve as net receivers of spillovers. In extreme conditions, spillovers between energy commodities, green bonds and non-green bonds are asymmetric, given that they are not equal in the upper and lower quantiles. Green bonds offer greater diversification benefits when coupled with WTI, heating oil, gasoline, and natural gas, though with varying portfolio weights.

STOCK MARKET SENSITIVITY TO CRUDE OIL AND FOREX PRICE MOVEMENTS-A STUDY ON NIFTY 50 INDEX

This study examines the impact of the USD/INR Exchange rate, and the per-barrel prices of WTI Crude oil in rupees on the Nifty 50 index, utilizing monthly data spanning from January 1, 2010, to September 30, 2023. The analysis primarily involves the utilization of the closing returns of the nifty index, the West Texas Intermediate (WTI) oil price per barrel in dollars, and exchange rates. The classical OLS method is sued to analyse the relationship between Nifty, crude oil, and crude oil prices over 166 months. The results reveal that exchange rates have a negative coefficient, suggesting that an increase in exchange rates is associated with a decrease in Nifty50 returns. In addition, the CUSUM test confirms the stability and consistency of our model over time, supporting the absence of structural breaks. This study underscores the dynamic nature of financial markets and highlights the importance of continuous monitoring and risk management strategies. KEY WORDS: Nifty 50, Crude Oil, Exchange Rate

The impact of Russia–Ukraine war on crude oil prices: an EMC framework

As the second-largest oil producer and natural gas exporter, Russia’s war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by $37.14, a 52.33% surge, and the Brent crude oil price to rise by $41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks.

Asymmetry in the prices of crude oil and diesel and gasoline prices in Brazil

PurposeThe aim of this study was to determine if there are asymmetries in the pass-through of West Texas Intermediate (WTI) crude oil prices to its derivatives (diesel and gasoline) in the Brazilian market.Design/methodology/approachInitially, the future WTI oil price series was analyzed using the self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) non-linear models. Subsequently, the threshold autoregressive error-correction model (TAR-ECM) and Markov-switching model were used.FindingsThe findings indicated high prices throughout 2008 due to the subprime crisis. The findings indicated high prices throughout 2008 due to the subprime crisis. The results indicated that there is long-term pass-through of oil prices in both methods, suggesting an equilibrium adjustment in the prices of diesel and gasoline in the analyzed period. Regarding the short term, the variations in contemporary crude oil prices have positive effects on the variations in fuel prices. Lastly, this behavior can partly be explained by the internal price management structure adopted during almost all of the analyzed period.Originality/valueThis paper contributes to the literature at some points. The first contribution is the modeling of the oil price series through non-linear models, further enriching the literature on the recent behavior of this time series. The second is the simultaneous use of the TAR-ECM and Markov-switching model to capture possible short- and long-term asymmetries in the pass-through of prices, as few studies have applied these methods to the future price of oil. The third and main contribution is the investigation of whether there are asymmetries in the transfer of oil prices to the price of derivatives in Brazil. So far, no work has investigated this issue, which is very relevant to the country.