West Texas Intermediate Crude Oil Research Articles

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.

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.

Impact of Petrobras' price policy change on the brazilian economy:

This study examines the impacts on Brazilian inflation of the Petrobras price policy adopted in 2017, more specifically the transition to the Import Parity Price (IPP) policy in response to abrupt changes in West Texas Intermediate (WTI) crude oil prices. Using the Vector Autoregression Model (VAR), it is observed that the Petrobras price policy and the IPP, played significant roles in transferring oil price fluctuations to inflation. Prior to the IPP, government interventions strove to curb inflation, with positive impacts on inflation after its adoption. The findings highlight the relevance of reevaluating the Petrobras price ‎ policy in 2023 in order to safeguard the company's interests and underpin the nation’s economic stability. This study contributes to discussions on fuel policies in Brazil and how significantly they affect the transport sector.

Hedging performance analysis of energy markets: Evidence from copula quantile regression

AbstractThis study investigates hedging performance with respect to different market structures for energy‐related commodities, including West Texas Intermediate crude oil, Brent crude oil, Chinese crude oil, and Heating oil. Copula quantile regression functions and the generalized autoregressive conditionally heteroscedasticity model are combined to analyze the nonlinear impact of dependence and the heterogeneous impact of market structure changes on hedging performance. Results show that hedging performance presents nonlinearity and market structure changes have surprisingly strong heterogeneous effects on the quantile hedge ratio, where bearish and bullish have lower hedge ratios than normal markets, which is captured better by Clayton copula quantile regression. Additionally, the trend of hedging effectiveness over different market structures also shows an inverted U shape. After changing data frequency or the types of futures contracts, the conclusions remain the same. Our empirical findings imply that hedgers are supposed to adjust the hedging number of futures according to market structure changes to hedge price risk effectively.

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.

Research of the non-linear dynamic relationship between global economic policy uncertainty and crude oil prices

This paper examines the relationship between global economic policy uncertainty (GEPU, Global economic policy uncertainty refers to the fact that market actors cannot accurately predict whether the government will change the current economic policy, if so, when the government will make changes, and what the content of the current policy changes will be. These are difficult for economic actors to accurately predict.) and crude oil prices from a global perspective within a non-linear analytical framework. Firstly, the non-linear dynamic trends between GEPU and WTI (West Texas Intermediate) crude oil prices are examined using the VAR-filtered BDS test, followed by the test of the non-linear Granger causality, which shows significant non-linear causal relationship between GEPU and WTI oil prices. Further, this paper use threshold vector autoregressive model (TVAR) and threshold vector error correction model (TVECM) to analysis the long-term and short-term non-linear dynamic adjustment processes between GEPU and WTI oil prices, the result shows that there is an optimal threshold between GEPU and WTI. In the low global economic policy uncertainty regime, the relationship between GEPU and WTI is mutually beneficial, while in the high global economic policy uncertainty regime, the relationship between GEPU and WTI is mutually inhibited. Meanwhile, the TVECM results show that in the first regime, GEPU adjusts towards equilibrium at a faster rate, while in the second regime, WTI adjusts towards equilibrium at a faster rate.

A Study on Co-movements and Information Spillover Effects Between the International Commodity Futures Markets and the South Korean Stock Markets: Comparison of the COVID-19 and 2008 Financial Crises

Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises.
 Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat.
 Findings - The results of the co-movement analysis were as follows: First, it was shown that the co- movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed.
 Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.

Denoising or distortion: Does decomposition-reconstruction modeling paradigm provide a reliable prediction for crude oil price time series?

Reliable forecasting of the crude oil price has important implications for production planning, investment decisions and policy making. Though the effectiveness of denoising hybrid models in crude oil price forecasting has been widely demonstrated, we argue that the denoised series used for model fitting may be affected by the out-of-sample information due to the one-time denoising operation applied to the entire price series. To verify whether the denoising paradigm does improve the crude oil price forecasting, a rolling window mechanism is introduced into two denoising hybrid models based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD). The novel R-EEMD-RVFL and R-VMD-RVFL models with the random vector functional link (RVFL) neural network are proposed. Then, both monthly and weekly West Texas Intermediate crude oil spot prices are used for multi-step-forward prediction. The experiment reports that: Firstly, the prediction provided by conventional denoising hybrid models is unreliable due to the one-time denoising procedure. Secondly, the proposed R-VMD-RVFL model achieves superior and robust accuracies in both monthly and weekly crude oil price predictions, especially when the forecast horizon is relatively long. Furthermore, this study provides theoretical supports for the findings using the weak-form efficient market hypothesis and behavioral finance theory as well as technical analysis from the perspective of denoising methods.

Differential Tail Dependence between Crude Oil and Forex Markets in Oil-Importing and Oil-Exporting Countries during Recent Crisis Periods

The relationship between foreign exchange rates and crude oil prices holds significant importance in comprehending the dynamics of oil markets and their implications for diverse economies. This study utilizes the time-varying copula to examine the interrelationships between foreign exchange rates (FX) and West Texas Intermediate (WTI) crude oil prices, with a focus on time-varying tail dependence and time-varying linear correlation. We found that the tail dependence between foreign exchange rates (FX) and WTI crude oil prices is higher for oil-exporting countries compared to oil-importing countries. Moreover, the COVID-19 pandemic has further amplified the tail dependence for oil-exporting countries while simultaneously increasing the correlation of FXs–WTI for oil-importing countries. However, the 2022 Russian–Ukrainian conflict has exerted a significant receding effect on both the tail dependence and linear correlation of FXs–WTI, reaching or even surpassing levels comparable to those witnessed during the 2008 financial crisis. These results facilitate policymakers, investors, and market participants in making well-informed decisions and developing effective risk management strategies.

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%.

The dependence and risk spillover between economic uncertainties and the crude oil market: new evidence from a Copula-CoVaR approach incorporating the decomposition technique.

Understanding the risk spillover of the oil market in economic uncertainty is of great importance. However, it is difficult to take on a traditional single perspective in describing the risk spillover law of economic uncertainty in the crude oil market on different timescales. In order to fill the research gap resulting from such difficulty, this paper incorporates empirical mode decomposition into the time-varying Copula-CoVaR model, and for the first time explores the risk spillover path of economic uncertainty on the two international crude oil pricing benchmarks-Brent and West Texas Intermediate crude oil prices-using different timescales. The empirical results not only verify the necessity of research from the perspective of different timescales, but also reveal the heterogeneity of the risk spillover paths of different types of economic uncertainty on crude oil prices. The research in this paper provides a multi-perspective interpretation for understanding the complex risk spillovers between various economic uncertainties and the crude oil market, as well as providing meaningful information to support stakeholders in making rational decisions.

Investing in cryptocurrency before and during the COVID-19 crisis: Hedge, diversifier or safe haven?

This paper examines the safe-haven, diversification, and hedging properties of cryptocurrencies against stock market declines. We use daily data for two sub-periods: pre-Covid-19 and during Covid-19. The sample includes the stock market indices of the 10 countries most affected by the pandemic and the eight most popular cryptocurrencies based on their market capitalization over the study period. We found that Bitcoin, Cardano, Ethereum, and Binance have generated portfolio diversification benefits for investors during the Covid-19 outbreak, while Tether and the USD Coin have acted as safe-haven assets. Additionally, the findings reveal that gold has been an effective diversification tool for investors during the Covid-19 pandemic, while West Texas Intermediate (WTI) crude oil has positively impacted stock index returns. Our results are of great practical interest to academics and policymakers, as well as investors, and provide relevant information on cryptocurrency variations during times of crisis.

TIME SERIES ANALYSIS FOR FORECASTING CRUDE OIL PRICES

Many analysts, policymakers, and researchers have grown increasingly concerned about the fluctuation of international crude oil prices. That is because oil prices reflect many macroeconomic and financial indicators (GDP, unemployment, inflation, S&P 500 Index, Nasdaq Composite Index), and conditions in a variety of financial and goods markets. This paper highlights the most appropriate model for estimating and forecasting West Texas Intermediate (WTI) crude oil monthly prices by comparing three hybrid models – ARMA-GARCH, ARMAEGARCH, and ARMA-FIGARCH. Finally, among these models, the paper considers that the ARMA-EGARCH(1,20) model emerges as the most efficacious model for the prediction of West Texas Intermediate (WTI) crude oil monthly price returns.

Extreme quantile spillovers and connectedness between oil and Chinese sector markets: A portfolio hedging analysis

Oil price instabilities have direct and heterogeneous implications for stock sector markets as a result of portfolio risk management and fund allocations. Previous studies have shown that the oil-stock market nexus is asymmetric and strongly vulnerable to international events. Using daily data of ten Chinese stock sector indices and the West Texas Intermediate crude oil futures over the period from July 2, 2007, to September 3, 2021, we examine the quantile return spillovers and interconnectedness between these markets using the approach of Ando et al. (2022), showing that return spillovers between the markets under investigation are more pronounced under bearish market conditions than during bullish ones. Major financial, political, energy, and COVID-19 pandemic events have magnified spillovers. Irrespective of the state of the market, oil is always a net receiver of return spillovers. Moreover, for all sectors other than materials, the sector that acts as a net receiver during bearish market conditions becomes a net contributor during bullish market conditions, and vice versa. During the COVID-19 period, the hedging technique was the most cost-effective. In the event of a global pandemic, the IT, financial, telecommunication, and energy sectors can benefit from oil's higher hedging effectiveness. Oil was a cheaper hedging asset during the pandemic, and it offered the highest hedging effectiveness to utilities before the outbreak and to the financial sector during the COVID-19 pandemic.