WTI Oil Research Articles

Brent and WTI oil prices volatility during major crises and Covid-19

We provide novel evidence of two different types of volatility-patterns of oil spot prices that are generated depending on which is the predominant trigger: a) spikes of volatility (which are highly erratic) are produced during periods of supply/demand crises of oil disruptions (such as the 1990/91 First-Gulf-War, 2001 US-terrorist attack, the oil conflict of Saudi-Arabia with the US in 2014/16 and with Russia in 2020 -together with the Covid-19 impact-); while b) periods where economic/financial/stock market crises are the predominant trigger (such as the 1997/98 Asian and 2008/09 Global-Financial Crises and the 2017/19 oil conflicts including the 2018 stock market crisis) are associated to higher volatility persistence. Our results are very relevant since oil markets in the coming months/years are very likely to have a very high degree of uncertainty, and knowledge of the type of volatility that is generated under each of the different triggers and how it affects oil markets is very relevant for investors, speculators and policy makers.

Regime Switching Mechanism during Energy Futures' Price Bubbles

In the last twenty years, many huge ups and downs have been seen in not only oil prices but also in other spot and derivative' energy prices too. This study has two main purposes. The main purpose of the study is to detect bubbles and their beginning and ending dates in energy derivatives futures prices. Crude oil WTI, natural gas, and heating oil monthly prices are analyzed for the period beginning from 1990 to 2018. Following detecting bubbles, Markov Regime Switching Autoregressive (MSAR) models and Markov Regime Switching Vector Autoregressive (MSVAR) models are used to analyze the movement of the regime-switching mechanism between the bubble dates. The general evidence indicates that the switching mechanism during bubble periods has some mutual similarities as generally their direction is to regime 1 as recession with low/negative returns and high volatility. Following positive return periods in energy prices, mostly after the high return/high volatility periods, the market actors might face bubble collapses.

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

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

Fourier transform based LSTM stock prediction model under oil shocks

<abstract><p>This paper analyses the impact of various oil shocks on the stock volatility prediction by using a Fourier transform-based Long Short-Term Memory (LSTM) model. Oil shocks are decomposed into five components following individual oil price change indicators. By employing a daily dataset involving S & P 500 stock index and WTI oil futures contract, our results show that different oil shocks exert varied impacts on the dynamics of stock price volatility by using gradient descent. Having exploited the role of oil shocks, we further find that the Fourier transform-based LSTM technique improves forecasting accuracy of the stock volatility dynamics from both statistical and economic perspectives. Additional analyses reassure the robustness of our findings. Clear comprehension of the future stock market dynamics possesses important implications for sensible financial risk management.</p></abstract>

An Empirical Analysis of Oil and Stock Markets’ Volatility Based on the DGC-MSV-t Model

We investigate the spillover effect between crude oil future prices, crude oil spot prices, and stock index by using the multivariate stochastic volatility model. These tests between each market show the significant Granger causes of spillover effect. More and more evidences show that the crude oil price has been affected by other financial markets. The oil future played an important role in the energy market. WTI and Brent oil future have more spillover effect than INE oil future. The result shows that S&P stock market is more sensitive to the oil price than Shanghai stock market. The cross-market spillover effect we found can give some advices for the investor of oil and stock market. DIC test shows that DGC-MSV-t is considered effective and more accurate.

Dynamic and frequency spillovers between green bonds, oil and G7 stock markets: Implications for risk management

This paper examines the dynamic and frequency spillovers between global Green Bonds (GBs), WTI oil and G7 stock markets using the time–frequency spillover index by Baruník and Křehlík (2018) and wavelet coherency approach. The results show that the spilllovers is dynamic and crisis-sensitive. Furthermore, adding GBs and oil futures to stock portfolio reduces the spillover size during turmoil periods. The short-term spillovers (up to five trading days) represent the largest proportion of the total spillovers. A significant jump in spillovers is observed in the early of COVID-19 outbreak (March–April 2020). Interestingly, Canada, France, Germany, Italy, and UK are the net transmitters of spillovers, whereas Japan and GBs are the net recipients of the spillovers, irrespective of time horizons. Oil and US stock market shift from net contributors in short term to net receipts in medium and long terms. Wavelet coherence analysis reveals significant co-movements between G7 stock markets and both oil and GBs. The co-movements are more pronounced in both medium and long terms and during COVID-19 spread where both oil and GBs lead stock markets. GBs provide higher diversification benefits to G7 investors than oil in the short-term. The hedging is expensive at the long term for GBs and intermediate term for WTI oil. Finally, the hedge effectiveness of crude oil is higher than GBs, irrespective of time horizons.

Uncertainty and oil volatility: Evidence from shrinkage method

Prior studies argue economic policy uncertainty index (EPU) can successfully predict oil price volatility. This paper mainly sheds light on the forecasting ability of categorical EPU indexes for both WTI and Brent oil futures. To illustrate this problem, we extend the LASSO model with Markov two-stage regimes (MS-LASSO model) to better fit the nonlinear feature of oil volatility dynamic. Our empirical results provide empirical evidence that EPU index not only contains predictive information content for WTI and Brent oil volatility, but achieves higher realized utility. Notably, we find that the predictive ability of categorical EPU indexes is asymmetric through global financial crisis, business cycle and different market conditions. Additionally, the uncertainty index of sovereign debt and currency crises is the most frequent predictor selected by shrinkage method for both WTI and Brent oil volatility.

Do China's macro-financial factors determine the Shanghai crude oil futures market?

Existing studies have investigated the Chinese Shanghai crude oil futures (INE) from price efficiency, cross-futures transmission, etc., but neglected the potential links with China. This paper is committed to filling this gap by conducting an initial discussion from the perspective of macro-financial factors. Applying the dynamic model averaging (DMA) approach, we examine the time-varying importance of the six potential factors that drive the INE prices. The results based on the overall conditions of all determinants and based on individual predictors both support the crucial roles of some Chinese macro-financial factors. The pricing effects of these factors almost display upward features within 6 months since the INE establishment. Thereafter, it maintains an overall stable trend, though some abnormal turmoil is found after the COVID-19 outbreak. According to the multi-scale analysis, the importance of China's macro-financial factors mainly reveals the INE market at the low-frequency components. To confirm the robustness of the estimation and the uniqueness of such effects on the INE, we utilize an alternative forecast accuracy criterion to confirm stability, accommodate the DMA estimation on the WTI and Brent oil futures prices as comparisons, and discuss the frequency domain through another decomposition procedure. These all mirror our findings.

Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities

This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for the market (differences) and observed data dependence important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for both contracts giving access to a long-simulated realization of the state vector with associated contract movements. The realization establishes a functional form of the conditional distributions, which are evaluated on observed data giving the conditional mean function for the volatility factors at the data points (nonlinear Kalman filter). For both Brent and WTI oil contracts, the first factor is a slow-moving persistent factor while the second factor is a fast-moving immediate mean reverting factor. The negative correlation between the mean and volatility suggests higher volatilities from negative price movements. The results indicate that holding volatility as an asset of its own is insurance against market crashes as well as being an excellent diversification instrument. Furthermore, the volatility data dependence is strong, indicating predictability. Hence, using the Kalman filter from a realization of an optimal multifactor SV model visualizes the latent step ahead volatility paths, and the data dependence gives access to accurate static forecasts. The results extend market transparency and make it easier to implement risk management including derivative trading (including swaps).

Measuring risk spillovers between oil and clean energy stocks: Evidence from a systematic framework

Increasing concern about the challenges of climate change has triggered rapid growth of private investment into clean energy sectors. Currently, the uncertain global economic environment makes the risk spillovers in the “Oil-Clean energy stocks” system a vital topic to explore. Using a systematic framework involving both Spillover index and Multivariate quantile models, this paper aims at revealing the heterogeneous risk transmission mechanisms between WTI oil futures and 12 clean energy stock indexes over the period of October 2010 to August 2020 from a systematic perspective by considering both regular and extreme conditions. We find asymmetric and time-varying features in the connections between oil and clean energy. The main findings include: (1) responses of extreme downside risks are substantially intensified compared to normal and upside risks; (2) the (downside) VaR response of oil to shocks of clean energy stocks is greater when the oil price is low; (3) the (downside) VaR response of clean energy to oil shocks is heterogeneous especially when the oil price is low. We also find that oil market is a net volatility receiver from clean energy sector overall and especially from the Advanced material and Energy efficiency groups. However, oil is a risk trigger for the Renewable energy group. These findings have significant and abundant implications for investors, portfolio managers, and policymakers to inform their decision-making and promote clean energy investment.

An analysis of investor behaviour and information flows surrounding the negative WTI oil price futures event

Intertwined with the persisting effects of the COVID-19 pandemic on the world economy, the price of WTI crude oil futures became negative on April 20, 2020. This anomalous incident has drawn much attention within the literature. This paper attempts to investigate the origins and specific impacts of the negative pricing event on the price discovery of WTI futures by employing a bivariate VECM-DCC-GARCH-SNP model, incorporating Legendre polynomials, where the dynamics of major information share measures at high-frequency time intervals are uncovered. Time-varying patterns of information share are identified across the period surrounding negative WTI prices. In particular, price discovery effects steadily abate after a sharp shock during the eight weeks before the negative pricing event. Peak price discovery differentials then re-occur within the negative-pricing event, before once again abating. Such results verify CFTC concerns surrounding the peculiarity of WTI futures trading conditions, that is, the conditions for the negative pricing event were well-established in the weeks before April 2020. Our results shed light on stylised evidence relating to the information efficiency of the international crude oil market more generally.

Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?

This paper investigates whether investor sentiment has stronger predictive power than VIX and uncertainty indices in predicting the realized volatility (RV) of energy assets. The five representative energy assets we consider are natural gas spot and futures, WTI oil spot and futures, and Brent oil spot. Several significant findings appear. First, the in-sample results suggest that VIX has a significantly positive impact on the five energy assets and investor sentiment has a significantly positive impact only for WTI oil futures and spot. Second, the out-of-sample results show that investor sentiment performs best predictions for the RV of three crude oil-related assets, followed by VIX, however, they have almost no predictive ability on natural gas futures and spot. Third, the use of VIX can increase economic returns for natural gas spot, and the use of investor sentiment can achieve higher economic returns for three crude oil-related assets. Finally, the results are supported by numerous robustness checks.

Does a sanitary crisis drive oil prices and carbon emissions in the USA? Evidence from VECM modeling.

This study explores the interdependence among a sanitary crisis, environmental degradation, oil prices, and economic activity in the USA based on weekly data over the period from January 03, 2020, to October 02, 2020, through VECM and Granger causality methods. The study period is characterized by lockdowns and mobility restrictions due to COVID-19 pandemic that may affect the economic and energy sector in the USA. Thus, a meticulous analysis of the impact of a sanitary crisis on economic and energy sectors seems to be crucial. Findings are very interesting and confirm the existence of a significant impact of a COVID-19 pandemic on WTI oil price. More importantly, bidirectional causal relations between the three couples: COVID-19 infections–carbon emission, COVID-19 infections–economic growth, and COVID-19 infections–oil price are also discovered. Taken together, our empirical findings are effective for the relevant authorities and policymakers in the USA to develop an appropriate financial and fiscal policy such as reducing interest rates, subsidizing, promoting sustainable industrialization, and carbon taxation to boost investment and to recover the economic growth without harming the environment and complicating the sanitary situation.

Energy markets – Who are the influencers?

The energy markets have recently undergone important transformations (e.g. deregulation, technological progress, renewable energy deployment and changing energy consumer behaviour) and witnessed a variety of crisis periods, affecting the relationships among energy commodities and their interactions with clean energy indices. This has implications for price discovery, asset allocation and risk management, which requires in-depth analysis to uncover and identify which energy indices (or forms of energy) lead others or are the most influential, while accounting for asymmetry and non-linearity characteristics. To uncover the complex structure of the relationship across the returns of seven different energy commodities and two clean energy stock indices, we apply Granger causality and transfer entropy in both static and dynamic approaches. The results from the Granger causality analysis identify the influence of the other energy products on natural gas, whereas the transfer entropy analysis reveals the importance of WTI oil and the influence of clean energy indices. Diesel is the most influenced energy commodity. A rolling windows analysis confirms those findings and shows evidence of a time-variation that reflects the impacts of crisis periods, especially the pandemic, on the dynamics of relationships.

The Impacts of the Infectious Disease Epidemic on the Permanent Volatility of Precious Metal and Crude Oil Futures Markets: A Long-Term Perspective

The aim of this paper is to identify the quantitative impacts of the infectious disease pandemic on the permanent volatility of precious metal and crude oil futures from a long-term perspective by using a recently constructed Infectious Disease Equity Market Volatility Tracker (ID-EMV) to capture the epidemic severity and with a novel mixed data sampling GARCH (GARCH-MIDAS) method. Different from the extant literature only focusing on the short-term influences of the COVID-19 epidemic on commodity futures market, this paper shows that the infectious disease pandemic does have significant and positive impacts on the permanent (long-term) volatilities of precious metal and crude oil futures markets lasting for at least up to 12 months. In addition, these specific impacts on crude oil futures are greater than those on precious metal futures. Finally, we find that the infectious disease epidemic has larger impacts on gold (WTI oil) futures than those on silver (Brent oil) futures. All these findings are robust after controlling the negative influences of lagged long-run realized volatility in commodity futures markets.

Risk spillovers and time-varying links between international oil and China’s commodity futures markets: Fresh evidence from the higher-order moments

Existing studies of risk spillovers primarily examine returns and volatility but ignore higher-order moment risks and asymmetrical effects, thereby impeding portfolio optimization and risk management. This study addresses those scholarly deficiencies by investigating the realized higher-order moments (realized skewness and kurtosis) risk spillovers, and asymmetrical spillovers in volatility between WTI and Brent oil and China's commodities spanning 2006–2020. For robustness, we explore the spillovers in signed jump variations, realized hyper-skewness, hyper-kurtosis, and realized jump. Based on the DCC framework and the proposed MVHAR-RS/RK models, we propose a model to quantify the dynamic links in volatility and higher-order moments. Empirical results demonstrate that risk-connectedness varies under different moments. Alongside spillovers in realized volatility, analysis reveals significant spillovers in higher-order moment risks and captures the effects of asymmetries in spillovers of good and bad volatility. WTI and Brent oil act as the net transmitters of risk spillovers in all realized moments considered. The dynamic links in realized kurtosis are relatively more volatile and higher than volatility and skewness links. The dynamic risk spillovers under different realized moments and conditional correlations are time-varying and sensitive to major crises. These findings benefit investors and regulators concerned with cross-commodity risks, portfolio optimization, and policymaking.

Responsibilities of petroleum prospectors: Discussions on dual logic and development trend of hydrocarbon exploration

Some unusual events happened in petroleum industry in 2020, such as the negative WTI oil price, price soaring of melt-blown nonwoven fabric, Exxon Mobil Corp.(NYSE:XOM) removed from Dow Jones Industrial Average, and the oil demand peak theory proposed by BP Energy Outlook 2020 Edition. These events have made profound impact on petroleum exploration. Prospecting is at the forefront of petroleum industry chain, and prospectors have great influence on petroleum industry. The responsibility of petroleum prospectors is to find oil, which calls for the correct way of thinking as well as scientific and technical means, both of which are indispensable. When it comes to the cognition of petroleum exploration, we should draw lessons from predecessors' philosophy of finding oil from a development perspective. It is necessary to define the relationship between subject activity and objective structure, as there is an inherent tension between the two and a dialectical relationship that complements each other. It is also essential to illustrate the logic of initiative and decisiveness, as between the two is the dual logic of active logic that changes the world and deterministic logic based on science and technology. The strategic breakthrough in the Gulong shale oil exploration in Daqing is a typical example. Our knowledge and practice of oil exploration has overthrown the Hubbert Curve. The new curve may have more than one peak, which means hopes are always there for finding oil. Climbing to the top of a mountain must start from the foot. A journey of a thousand miles must begin with a single step. Looking forward to the future, prospectors have the wisdom, ability, and methods to find more, cleaner, and more affordable oil to drive the progress of human civilization. This is the duty of petroleum prospectors.

(A)symmetric time-varying effects of uncertainty fluctuations on oil price volatility: A nonlinear ARDL investigation

Limited number of literature studies looked at the link between uncertainty and oil price volatility. However, the asymmetric relationship regarding this nexus remains relatively unexplored area in the literature. In this paper, we adopt asymmetric analysis for the uncertainty-oil price nexus, via employing the nonlinear autoregressive distributed lag model (NARDL) of Shin et al. (2014). We utilize three uncertainty indices; economic policy uncertainty, global geopolitical risk, and world uncertainty. The paper finds evidence of long-run relationship (cointegration), using monthly data from 1990 to 2020. The NARDL findings show long-run asymmetric effect of economic policy uncertainty and global geopolitical risk on WTI oil prices, however, asymmetric short-run effect was documented for global geopolitical risk only. The empirical inferences highlight that economic policy uncertainty and global geopolitical risk are more suitable and proper indices for analyzing the impact of uncertainty on oil price volatility.

Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions.

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

Forecasting WTI crude oil futures returns: Does the term structure help?

Nelson-Siegel (NS) factors extracted from the term structure of WTI oil futures are shown to predict subsequent WTI holding period returns in-sample. This in-sample predictability is not diminished by augmenting with macroeconomic indicators or oil market specific predictors. Allowing the decay factor in the Nelson-Siegel model to vary over time improves in-sample predictions at medium horizon return forecasts. We conduct out-of-sample forecasting exercises on models that use NS factors, such as a simple two factor model that uses a composite leading indicator along with the NS decay factor, and a LASSO model that combines NS factors with macroeconomic indicators and oil market specific predictors. These models significantly reduce forecast errors relative to a no change benchmark across a range of return horizons and futures contract maturities. We also find consistent evidence that models that use the NS factors result in trading strategies with higher Sharpe ratios and better skewness properties than buy and hold strategies and historical mean strategies.