WTI Oil Prices Research Articles

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.

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.

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.

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

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.

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.

Economic Virus in Our Body Cells: To What Extent Are Gold Prices Immune Against Sectoral Stock Indexes?

The novel coronavirus (COVID-19) has revealed profound and disproportionate effects not only on global financial markets but also on commodity markets. Considering the potential safe-haven role of gold as a vital commodity against economic turmoils and uncertainties; in this study, it has been aimed to investigate the long run impacts and short-run dynamics of the COVID-19 related variables (daily new confirmed cases in the world and U.S., COVID-19 Containment & Health Index) and different macro-financial factors on daily COMEX gold futures contracts prices, especially presenting a comparative analysis with respect to various sectoral stock indexes associated with S&P 500 and Dow Jones -including Health Care, Technology, Medical Equipment, Financials, Industrials, Information Technology and Energy sectors- for the sample period 10.03.2020-22.03.2021 by utilizing from Autoregressive Distributed Lag (ARDL) approach. Analysis results are of great importance in forming crucial policy implications about by which factors and sectoral stocks gold prices are being influenced the most. Consequently; Dow-Jones Financials, S&P 500 Industrials and S&P 500 Energy stock indexes can be said to have a significant impact on gold prices in the long run at 10% significance level. In the models containing these indexes, CBOE volatility index affects gold prices inversely while WTI oil prices have no impact on gold prices. In the long term, the weakest findings in terms of coefficient significancies have been obtained for the model which includes S&P 500 Energy stock index returns. Furthermore, daily new cases in the world -contrary to the cases in U.S.- generally affect gold prices.

THE IMPACT OF RUSSIA-UKRAINE WAR ON ENERGY POLICY MANEUVERS TO STRENGTHEN ENERGY SECURITY AND CYBERSECURITY IN INDONESIA

The war between Russia and Ukraine is highly discussed in global geopolitics and geostrategy. The strategy of attacking Russia directly at the point of a vital national object, namely the power plant, which was preceded by a cyber attack at the Nuclear Power Plant in Chornobyl, followed by a conventional physical attack from the air, resulted in an energy crisis in Ukraine. The resulting war affects many sectors, such as the energy sector, food, and supply chains, especially (at the end of 2021) before the war. WTI oil price was only around 75$/barrel, but when the war broke out, the price increased to 130$ (in August: 93$). It is estimated that oil prices will not fall significantly as long as the war is not over. The direct impact of war disrupts global energy security. This research uses a qualitative study based on the theory of incremental policy from several experts who support new policies quickly and national defense and energy security. Based on the incremental policy, policies that support the strengthening of cybersecurity will be produced, ranging from cryptographic policies to the potential for highly advanced energy sector blockchains.

Volatility spillover between oil and stock prices: Structural connectedness based on a multi-sector DSGE model approach with Bayesian estimation

This paper examine the connectedness between oil and stock markets using the WTI oil price and 10 S&P 500 price subindices data from 1989 to 2021. We present a novel approach for constructing a connectedness index based on a dynamic stochastic general equilibrium (DSGE) model with Bayesian estimation technique. In contrast to existing approaches, our DSGE-based approach has the advantage of (i) incorporating a large number of shocks and effectively distinguish the demand and supply shocks to both oil and stock markets; (ii) allowing the conduction of policy and welfare analyses. By estimating the DSGE model, we find that demand shocks to oil and stock markets produce significant spillover effects compared to the supply shocks. We also show that the role of demand and supply shocks differ largely across sectors. We propose an oil-tax policy rule that can reduce the interconnectedness between oil and stock markets. The optimal oil tax rate that balances the tradeoff between systematic risk and social welfare is computed.

Dynamic impacts of online investor sentiment on international crude oil prices

In recent years, international crude oil prices have been subject to unusually high fluctuations due to the ravages of the COVID-19 epidemic. Under such extreme market conditions, online investor sentiment can strengthen the correlation between oil price changes and external events. We use a (rolling-window) structural vector autoregression method to investigate the dynamic impact of online investor sentiment on WTI crude oil prices before and after the COVID-19 pandemic across multiple topics of price, supply, demand, and so on, which aims to explore the fluctuation mechanism driven by sentiment and the price changes triggered by public health events. The proposed aspect-level sentiment analysis approach can effectively distinguish and measure sentiment scores of different aspects of the oil market. Our results show that the constructed oil price prosperity index contributes 49.84% to the long-term fluctuations of WTI oil price, ranking first among the influencing factors considered. In addition, the peak value of impulse shocks to WTI oil prices rose from 6.47% to 8.40% during the period of dramatic price volatility caused by the epidemic. The results sketch the mechanisms by which investor sentiment can affect crude oil prices, which help policymakers and investors protect against extreme risks in the oil market.

Time-Varying and Quantile-Based Relationship among Geopolitical Risks, Oil and Gold Prices

This paper probes the relationship between geopolitical risks (GPR), WTI oil, and gold prices utilizing the time-varying causality and quantile regression approaches. The sample period spans from January 1986 to January 2022, comprising 433 monthly observations and representing the longest common period of data availability. The results show that there is no causality between the pairs of GPR–WTI, and GPR–gold prices for the full sample period, while the causality between gold and WTI is unidirectional, running from gold to WTI. Using the rolling causality test, however, the findings show that the dynamic causal relations strengthen over time. The Granger causality from the gold prices to GPR and WTI is stronger than the other way around, suggesting that the gold market dominates the other two variables in terms of strength of the lead-lag structure of causality. Besides, the findings reveal the strongest causation effects between GPR and WTI spot prices. Before 2009, the causal relationship between WTI and GPR is mostly unidirectional while also a bidirectional linkage emerges, coinciding with the crisis periods including the Dot-Com and 2007 US Subprime crises. During the causal periods, these variables respond negatively to changes in others. For the COVID19 period, the direction of causality considerably changes in favor of WTI for the GPR–WTI pair whereas it is unchanged for the WTI–gold pair. The results indicate that WTI has positive and negative predictive powers for GPR and gold while it receives negative and positive causation effects from GPR and gold during the pandemic, respectively. The results, in overall, may offer important insights for investors and regulatory authorities in building portfolio and risk management strategies as well as pricing and trading activities and constructing monetary policies over various market conditions.

Cross-spectral coherence and co-movement between WTI oil price and exchange rate of Thai Baht

This paper investigates the cross-spectral coherence and co-movement between the monthly return series of WTI oil price and exchange rate of Thai Baht against USD from 1986 to 2019. We use a quantile cross spectral (coherency) approach and time frequency wavelets as estimation techniques. Results from the quantile coherency analysis reveal a negative spillover effects of oil price and Thai exchange rates in the short, medium, and long run which connotes that oil market poses systemic risk to the foreign exchange market in Thailand in short, medium and long term. From the wavelets analysis, we first note that there is no co-movements at high frequency (i.e. short term). However, additional results show positive and negative co-movements between oil price and Thailand-US exchange rate in different periods and different frequencies with the effect been stronger during extreme volatility periods. Policy implications are derived at the end of the article.

Energy cryptocurrencies: Assessing connectedness with other asset classes

We investigate connectedness between energy cryptocurrencies and common asset classes, including oil, using TVP-VAR modeling, evidencing that energy cryptocurrencies, as diversifiers, normally have strong connections with bitcoin and nothing else. However, their connectedness to other assets changes rapidly during shocks such as COVID-19 and the start of the Russian-Ukraine war. Connectedness spiked in April 2020, when WTI oil prices fell to negative pricing. Economic policy uncertainty, Twitter-based uncertainty, and infectious disease-related uncertainty all have significant impact on the system's total connectedness. Energy cryptocurrencies, while normally diversifiers, are highly sensitive to shocks and changes in uncertainty.

On the interdependence between biofuel, fossil fuel and agricultural food prices: Evidence from quantile tests

This study investigates the long- and short-run relationships between fossil fuel, biofuel and agricultural food commodity prices by applying the cointegration and causality analysis. This study employs a quantile approach to consider the non-linearity and asymmetry in the relationship of data. To reduce a possible noise contained in the data, this study uses weekly average data of biofuel (ethanol), fossil fuel (West Texas Intermediate [WTI] oil) and agricultural food commodity (corn) futures prices from April 18, 2005 to October 30, 2020. The main findings are summarised as follows. First, we discover no evidence of cointegration between the quantiles of WTI oil, ethanol and corn prices when all quantiles are considered, although the results of the linear cointegration test are inconclusive. Second, results of the Granger non-causality test in quantiles reveal a significant short-run bidirectional causality between the returns of WTI oil, ethanol and corn prices, for all or most quantiles of the distribution. This implies asymmetric and nonlinear causal linkage between these variables. Moreover, the results of our analysis have several policy implications, including energy price stability, decrease in energy dependence on fossil fuels, promotion of agricultural industry, environmental sustainability, energy conversion and reduction of greenhouse gas emissions.

Shocks and volatility transmission between oil price and Nigeria's exchange rate.

The study examined shock and volatility transmission between oil price and exchange rate markets using daily data covering the period from 23rd October 2009 to 30th November 2020. The contributions of the paper include (i) implementation of VAR-AGARCH model to capture spillover effect of shock and volatility; (ii) examining the nature of shock impact in oil price and exchange rate market; (iii) adopting of two measured of oil price (WTI and Brent); (iv) employing two measures of the exchange rate (USD/Naira and effective/Naira). The study revealed that past own shocks and volatilities significantly contribute to current volatilities in exchange rate and oil price markets. Also, there is bidirectional shock and volatility spillover between the exchange rate (USD/Naira and effective/Naira) and WTI oil price markets. There were bidirectional shock and volatility spillover between USD/Naira and Brent oil price and unidirectional shock and volatility from Brent oil price to effective exchange rate market. We found asymmetric shocks impacting exchange rates and WTI oil price while symmetric shock was observed in Brent oil price. Including innovation in oil price is essential in exchange rate policy formulation and modelling exchange rate shock and volatility.

Determining the COVID-19 effects on spillover between oil market and stock exchange: a global perspective analysis.

This paper investigates volatility spillovers between the global crude oil market and the stock markets of the global oil stock markets (Russian, Canada, China, Kuwait, and the USA) pre and after the COVID-19 pandemic. We use wavelet Granger causality methods to study the volatility spillovers between global oil stock markets, mainly from January 1, 2019, to March 31, 2021. Our Results (1) shows that WTI and Brent oil prices had a negative mean return before COVID-19 but a positive mean return during the pandemic spread. Other Results (2) find the positive, significantly lowest, and highest frequency during the COVID-19 outbreak for all selected countries. The results also show that the link between oil WTI & Brent prices and stock markets return in the lowest (33-66 days) and highest frequency range (4-16) before the Covid-19 epidemic, especially in the first quarter of 2020. Before the COVID-19 period, the Russian oil stock market is seriously prejudiced with oil prices on a modest scale, but not after the pandemic's start. This study also perceives direction opposite between the COVID-19 period. The Canadian and United States America oil and stock markets influence the lowest scale in the previous COVID-19 sample for the U.S. market. Moreover, this paper exposed that oil marketing highest oil futures in their portfolios than stock shares for all times. We found that oil price shocks had a more significant impact on the stock markets of the United States and Canada than on the stock markets of other countries.

Oil price volatility forecasting: Threshold effect from stock market volatility

Stock market volatility, which is usually considered a proxy for the general economy, contains important information for the crude oil market. In this paper, we investigate the incremental benefit of stock market volatility over oil volatility using the S&P 500 index and WTI oil prices for the period from January 1990 to December 2021. The threshold autoregressive regression (TAR) model is used to capture the nonlinear threshold effect of stock market shock on oil market volatility. From empirical analysis, both in-sample and out-of-sample results highlight the prediction superiority and effectiveness of the nonlinear threshold regression model, which indicates the valuable strong threshold effects of stock volatility for oil volatility forecasting. Moreover, the additional effects of stock volatility in terms of bad volatility forecasting further confirm the effectiveness of the nonlinear TAR model and the information content of stock volatility. This study will prove useful for policy-makers to formulate reasonable policies and for investors to avoid risk.