WTI Oil Research Articles

Can the sentiment of the official media predict the return volatility of the Chinese crude oil futures?

This study investigates whether the sentiment of Chinese official media towards crude oil influences price volatility of the Chinese crude oil futures (SC). By leveraging textual analysis through Bidirectional Encoder Representations from Transformers (BERT), we quantify the sentiment of oil-related articles published by the primary official media in China. Our main finding, building on both in-sample and out-of-sample analyses, robustly reveals that this sentiment significantly forecasts the one-day-ahead intraday return volatility of SC. Moreover, we extend the analysis to different time horizons (i.e., one-week and one-month-ahead) and find the prominent forecasting power of the official media sentiment as well. We also find that the official media sentiment fails to forecast the price volatility of WTI oil futures, implying that the official media sentiment contains some unique Chinese information. Overall, our study contributes to the existing literature on predicting the return volatility of the Chinese crude oil futures, and offers fresh insights into an essential yet underexplored sentiment, i.e., official media sentiment.

On the Effects of Physical Climate Risks on the Chinese Energy Sector

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market.

COVID-19 and time-frequency spillovers between oil and sectoral stocks and portfolio implications: Evidence from China and US economies

This paper examines the volatility spillovers and the time-frequency dependence between crude oil and stock sectors in the U.S. and China using both wavelet coherence and asymmetric BEKK GARCH models. This study also investigates the impact of the COVID-19 pandemic on spillover effects and portfolio management. The results reveal strong positive co-movements between WTI oil and US sector stock returns at medium and low frequencies particularly in 2020Q1. We find significant long-term co-movements between oil and Chinese sector stock returns (64–128 days). Furthermore, the findings reveal a significant and asymmetric volatility transmission between oil and sector stocks in both the US and Chinese economies. The evidence of spillovers is more pronounced during the COVID-19 period. It is recommended that equity investors should hold more stocks than oil assets to minimize risk without reducing the expected return. Finally, hedging with oil is expensive for U.S. sectors during the pandemic but inexpensive for Chinese sectors.

Linear Ensembles for WTI Oil Price Forecasting

Linear Ensembles for WTI Oil Price Forecasting

Nexus Between Oil, Gold Price and Dxy Index on Indonesian Stock Market During Geopolitical Events (2022 – 2024)

Objective: The aim of this study is to observe the impact of oil, gold, and the DXY index on the Indonesian stock market during geopolitical events in 2022-2024. Theoretical Framework: Rising political tensions also have a major impact on global currencies, financial market and commodity market. This event lead to uncertainty which increasing the investment risk. Hence, geopolitical events could affect stock return in capital market. Method: The data used consists of daily Jakarta Composite Index (JCI) closing data, WTI daily closing prices, gold daily closing prices, and DXY closing data from 24 February 2022 – 31 December 2022 and 14 April 2024 – 30 April 2024. Data obtained form Bloomberg. The analysis technique used was GARCH (1.1). Results and Discussion: The results showed that WTI oil price changes had significant positive effects on stock return in Indonesia. However, gold price changes had insignificant positive effects on stock returns in Indonesia during geopolitical events, while the DXY Index had insignificant, negative effects. Research Implications: Investors and traders in Indonesia Stock Exchange should consider oil price changes for investing in stocks during geopolitical events. Originality/Value: This study contributes to literatures regarding the stock market turbulence during geopolitical events and its effect toward stock return. The relevance and value of this research are evidenced by the insignificanct effect of gold and DXY toward stock return, while WTI has significant effect toward stock return.

African forex markets: Modeling their predictability and the asymmetric effects of oil and geopolitical risk

This study investigates the African forex (FX), geopolitical risk (GPR) and oil associations, resorting to the quantile-based causality, parametric quantile regression (QR), and non-parametric quantile-on-quantile regression (QQR) methodologies. We explore 16 major African currency markets. The sample contains daily data and spans from July 2000 through August 2023. Our results zoom on the differences in the FX-GPR-oil interactions at different market conditions of the dependent and independent variables, proxied for each case by 19 quantiles from 0.05 to 0.95. We find that the African FX returns are significantly asymmetrically predicted by both WTI oil and GPR. Our tail-based causality analysis uncovers the directional causal flow from GPR and Oil (WTI) to foreign exchange rates. We also report on salient asymmetries in the responsiveness of country-specific African FX returns to oil and geopolitics-driven shocks. In addition, the results of our QR analysis show a mix of positive and negative GPR and Oil effects, characterized by varying degrees of significance for different currencies. Our QQR analysis also reveals that GPR and WTI oil exert asymmetric effects on African FX returns. The returns of several African forex markets demonstrate negative relationships with GPR, especially at the lower tails of GPR and/or WTI oil, as usually the extreme-tailed conditions are driven by diverse kinds of crises, well covered by the analyzed dataset. Our outcomes cater relevant knowledge for scholars, FX investors, traders and regulatory bodies.

Nonlinear Effects of Climate Policy Uncertainty on Carbon Allowance and ESG Prices: Evidence From the US

We examine the nonlinear effects of climate policy uncertainty (CPU) on California carbon allowance prices (CCA) and S&P 500 ESG stock prices (SPESG). We used the nonlinear ARDL method on monthly data from December 2013 to August 2022. Using inflation uncertainty and WTI oil prices as control variables, we found that increases in CPU positively affect carbon allowance and ESG stock prices in the short and long term.

Assessing the baseline model of WTI oil and stock returns under financial volatility and spillover effects

Do financial volatility shocks matter for WTI oil and stock markets? They should since world economic activity responds to volatility. To investigate this question, we revisit two empirical frameworks widely used in the literature. Our baseline involves the standard identification of oil shocks in structural vector autoregressions (SVAR) with (world) oil supply, demand, and real oil prices. We then propose a global vector autoregressive (GVAR) model integrating 14 major world economies. We introduce the volatility measure (VIX), various stock markets, and compare two oil demand proxies: real economic activity (REA) based on commodity prices and world industrial production (WIP). The results of the impulse response analysis do not indicate major differences across models. However, when measuring the share of oil price variance explained by shocks to VIX (or to U.S. high-bond corporate yield) SVAR overestimates the influence of VIX. We find (world) demand shocks explain a larger part of the variance of oil and stock markets compared to supply shocks. In SVAR, new information coming from a one standard deviation shock to VIX is incorporated negatively and instantaneously on real oil prices: −1.6 for REA and −1.4 for WIP. In GVAR these are reduced slightly to −1.2 from VIX and to −0.6 if coming from corporate bond yield shocks. Our study highlights the sensitivity of econometric results to distinct approaches: one that identifies shocks and another that captures spillover effects.

Analyzing fossil fuel commodities' return spillovers during the Russia and Ukraine crisis in the energy market

During the Russia and Ukraine crises, the energy market, including fossil fuel commodities, experienced significant global shocks. This research paper investigates the phenomenon of return spillover across the energy market amid the Russia and Ukraine conflict, utilizing the Diebold and Yilmaz Spillover Framework and EGRACH Model. The study examines daily frequency data from 24 February 2022 to 30 June 2023, focusing on the interconnectedness between various energy commodities: Brent Oil, Carbon Emissions, Crude Oil WTI, Gasoline, Heating Oil, and Natural Gas. The result of the study reports a total spillover index of 36%, indicating a significant spillover during the Russia and Ukraine conflict. Furthermore, the highest dynamic gross directional return spillovers have been reported in Brent Oil compared to the others. However, carbon emissions and natural gas have the lowest spillovers. The study significantly contributes to a deeper understanding of energy commodities by confirming the volatility persistence across the energy market based on the E-GARCH model.

WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

How do precious and industrial metals hedge oil in a multi-frequency semiparametric CVaR portfolio?

This paper tries to reduce extreme risk of Brent oil by constructing multivariate portfolios with precious and industrial metals in a multi-frequency framework. Extreme risk is measured by the parametric CVaR and more elaborate semiparametric CVaR measure, while the wavelet technique is used to build portfolios in different time-horizons. The results indicate that gold dominates in the precious metal portfolios, creating the lowest downside risk in most cases. The back-testing results reveal that the raw data CVaR portfolio with precious metals is the best, while the midterm mCVaR portfolio with industrial metals has upper hand. In terms of forecasting, CVaR portfolios with precious and industrial metals are the best in the short- and midterm horizons, respectively. In the long-term horizon, none of the portfolios is good in back-testing and forecasting, which means that CVaR and mCVaR models are not adequate risk functions for identifying realized risk in the long term. In the oil-dominated portfolios, the CVaR portfolios with gold are the best in terms of the lowest risk as well as back-testing and forecasting performances. When Brent is replaced by WTI oil, the share of WTI is somewhat smaller in the portfolios because WTI has higher risk. When the US 10Y bond is added to the portfolios, the portfolio risk is reduced because the bonds are very weakly correlated with the commodities.

The Asymmetric Relationship between Conventional/Shale Rig Counts and WTI Oil Prices

This work analyses the asymmetric response of conventional and shale oil rig counts to WTI oil price returns. Our analysis shows that the rig count time series exhibited a structural change after the oil glut of 2014. All series are non-stationary in each sub-period but not cointegrated. Therefore, after controlling for possible confounding factors, a vector auto regressive (VAR) model is set up. Our specification accounts for the possible role of oil production and distinguishes between positive and negative oil price changes. It is shown that shale and conventional rig counts reacted differently in each subperiod to signed changes in oil price. Subsequently, by evaluating the response of rig counts to oil price shocks, their intensity and duration over time, we observe that the shale oil rig count reacts more intensively to positive than to negative oil price changes. On the contrary, the conventional rig count exhibits a modest reaction only to positive price changes. Finally, we robustify our findings by focusing on the data of the Permian basin, on the one hand, and the Anadarko, Bakken, Eagle Ford and Niobrara, on the other hand, which are characterized by different patterns in the number of Drilled but not Completed wells.

Forecasting the Nexus and Impact of News Sentiment on NYSE, Gold Prices, and WTI Oil Using The Neural Network Approach

The study aims to determine the impact of news sentiment during COVID-19 and the nexus between the NYSE, Gold prices, and WTI oil prices. For this purpose, we use hourly data of news sentiment and commodity prices hourly data from February 10, 2020, hours 1000 to March 06, 2020 hours 1600 hours, with the help of a multilayer artificial neural network. Further, We use the connection weight approach to observe the relative importance of input variables that help predict output variables. The results suggest that all the indexes are interconnected, where the increase/decrease of any index impacts the other commodity index. Further, the news index remains the second highest predictor for gold price and WTI oil price indexes. In addition, the news index also seems to be an essential predictor of these commodities.
 Keywords: NYSE, WTI Oil, Gold, Covid-19, Neural Network, Connection Weight

Interplay of multifractal dynamics between shadow policy rates and energy markets

This study examines the interconnections between short shadow rates (SSR) and energy markets. We conduct multifractal detrended cross-correlation analysis (MF-DXA) on the daily SSR from four major economies (USA, Eurozone, Japan, and UK) and the daily prices of four energy markets (WTI, Brent, Natural Gas, and Heating Oil) spanning January 1995 to March 2022. Our analysis shows that all energy market-short shadow rate pairs display significant multifractal characteristics. The presence of power law cross-correlation suggests that substantial changes in short shadow rates are associated with significant price changes in energy markets. The highest multifractal cross-correlation is observed between energy prices and shadow short rates of Japan and the US. Energy markets demonstrate persistent cross-correlations for small fluctuations compared to large fluctuations. However, the rolling windows analysis reveal a shift from persistent to anti-persistence cross-correlations during global financial crises (GFC), the 2014–2016 energy crises and the COVID-19 pandemic. Lastly, the cross-correlation exhibit positive skewness. We suggest that integrating these long-term dependency features into financial analysis has the potential to support decision-making for market participants.

Forecasting hourly WTI oil front monthly price volatility densities

<p>Financial commodity markets have an impact on company values and cash flows, where price movements within frequent time intervals can be both significant and random. Understanding highly frequent price movements is both important and difficult. In this paper, I measured and forecasted volatility for high-frequency (mostly twelve hours per day) WTI Oil front month price movements from 2012 to 2024 (about 40,500 observations). I created a new stochastic volatility model for extracting latent volatility using the non-linear Kalman filter. Stable and strictly ergodic hourly price series, along with the BIC optimal non-linear (generic) general method of moments model coefficients, enabled this process. The latent volatility seemed to separate into two volatility factors. One factor that was very persistent suggested slow mean reversion, and one choppy strongly mean-reverting factor. The data dependence found in the factor volatility series suggested forecasting ability. I applied classical static forecasts and three machine learning regression techniques to predict one-step-ahead volatility. The two volatility factors and one summarizing exponential factor were reported, along with several fit measures, including the root mean square error and Theil's measure of covariance.</p>

A novel secondary decomposition method for forecasting crude oil price with twitter sentiment

With the ubiquity of the Internet, valuable social media data have been generated, and a promising idea for using sentiment from social media has emerged in oil price forecasting. To address the complexity of oil price, this study proposed a novel secondary decomposition framework with Twitter sentiment, in which Twitter sentiment provides new information and secondary decomposition technique reduces the difficulty of oil price forecasting. This methodology involves three major steps: (1) sentiment extraction, to collect and extract Twitter sentiment via the state-of-the-art sentiment analysis technique—Bidirectional Encoder Representations from Transformers (BERT); (2) secondary decomposition, to extract scale-aligned components from crude oil price and Twitter sentiment using bivariate empirical mode decomposition (BEMD) first and then decomposing the residual terms through GA-VMD; and (3) oil price prediction, including individual prediction at each intrinsic mode function (IMF) and ensemble prediction across different IMFs. With WTI oil price as a sample, the empirical study results indicate that the proposed novel learning paradigms statistically outperform their corresponding original techniques (without Twitter sentiment and secondary decomposition), semi-improved variants (with either Twitter sentiment or secondary decomposition), and similar counterparts (with one-time decomposition analysis) in terms of prediction accuracy.

Forecasting value-at-risk of crude oil futures using a hybrid ARIMA-SVR-POT model

Forecasting the value at risk (VaR) of crude oil futures can be a challenging task for investors due to the high volatility of these prices. It is crucial to describe the return in the tail distribution, as extreme values can trigger larger price fluctuations and market risks. In this study, we proposed a hybrid model, ARIMA-SVR-POT, which uses a combination of the autoregressive integrated moving average (ARIMA), support vector regression (SVR), and peak over threshold (POT) method from the extreme value theory. We compared the performance of our hybrid model with three other models, namely ARIMA-EGARCH, ARIMA-SVR, and ARIMA-EGARCH-POT. We demonstrated the effectiveness of our model using crude oil WTI Futures as a sample from June 23, 2016, to September 30, 2022. Our findings show that the ARIMA-SVR-POT hybrid model provides accurate predictions of the returns and volatility. Furthermore, the model performs exceptionally well in capturing the extreme tail of returns and outperforms the other models. We also conducted back-testing in the proposed model and the results show that the ARIMA-SVR-POT model passed the Kupiec test at confidence levels of 95 %, 99 %, 99.5 %, and 99.9 %. Our proposed model provides a more precise reflection of potential losses when estimating VaR. The predicted loss probability is closer to the actual loss occurrence probability, indicating superior performance compared to traditional statistical models, which enhanced crude oil risk management tools and suggested effective measures to manage market risks.

Changing determinant driver and oil volatility forecasting: A comprehensive analysis

Academic research relies on exogenous drivers to enhance the accuracy of forecasting oil volatility. Following the relevant literature, this study collects 62 exogenous drivers that reflect the movements of oil demand, oil supply, oil inventory, macroeconomic fundamentals, financial indicators, and measures of uncertainty. Our empirical results indicate that dimension reduction regressions, especially principal component analysis regression (PCA), successfully predict both WTI and Brent oil volatility at the one-month ahead forecast horizon. Shrinkage methods, on the other hand, outperform their counterparts for medium- and long-term forecast horizons. Furthermore, the unsupervised learning method (PCA) achieves superior forecasting performance during periods of oil price decrease, whereas supervised learning methods (i.e., shrinkage methods) significantly improve volatility accuracy. Additionally, the empirical results reveal that movements in the Kilian index, World industrial production index, global economic conditions index, U.S. steel production, Chicago Fed national activity index, capacity utilization for manufacturing, U.S. default yield spread, and MSCI emerging market index have a significant impact on oil volatility.

The impact of joint events on oil price volatility: Evidence from a dynamic graphical news analysis model

The analysis of event-oriented oil market risk deserves attention owing to the diversity, complexity and time variant of the oil-related events. From the aspect of oil price volatility, we examine the dynamic impact of joint events of different aspects on the oil market using graph analytical models and the proposed value-at-risk estimation approach. Based on the construction and evaluation of news indices, we confirm that events of six categories have strong short-term causality with oil price returns. Furthermore, we prove the capacity of event-oriented indices on oil market risk assessment tasks. Demand and energy-based events have shown their effect in improving oil market risk estimation, as proved by empirical analysis of assessing value-at-risk of Brent and WTI oil price. This paper confirms the impact of oil-related joint events on oil market risk and provides a new perspective of assessing latent oil market risks associated with exogenous events.

Estimating the impact of the outbreak of wars on financial assets: Evidence from Russia-Ukraine conflict

This study analyzes the performance of the Shanghai Composite Index, S&P 500 index, WTI oil price, and LBMA gold price when wars took place, especially the Russia-Ukraine conflict. We employ empirical methods to explore the stability, instantaneous shock, and short-term shock regarding the abovementioned financial assets. We first adopt the event study method to ascertain whether the cumulative abnormal returns of the selected assets are significant when wars break out. Then, we use the permutation test to examine the significance of price level changes. Results show that only the Shanghai Composite Index is relatively stable. Second, the difference-in-differences model indicates that the 3 unstable assets all suffered positive shocks in their price levels within several days after the Russia-Ukraine conflict broke out. The parallel trend test confirms the validity of establishing the difference-in-differences model. Third, regression discontinuity is designed to measure the impact in a longer event window, suggesting the robustness of conclusions of the difference-in-differences model and revealing an upward trend before the conflict and a downward trend after the conflict of the financial assets. The study suggests that investors consider adjustments to investment strategies and governments take precautions to diminish the risk of the outbreak of wars.