WTI Oil Research Articles

Russian invasion 2022: analysis of persistent volatility and return spillovers among IMOEX, WTI and Russian OT (10Y)

Russia's invasion of Ukraine is creating instability in the financial markets, with European stock markets falling, and the effects reflected in energy and food prices. A war scenario brings with it a humanitarian crisis and it is the most vulnerable who suffer the worst consequences. Based on these events it is intended in this paper to test the persistence of returns on the IMOEX capital market, Russian Sovereign OT (10 YR), and the WTI oil index over the period April 24th, 2017, to April 22nd, 2022. To perform this analysis different approaches were undertaken to analyse, if: (i) do the analysed markets exhibit persistence in their returns? The results suggest that the returns do not follow the i.i.d. hypothesis from dimension 2, reinforcing the idea that time series returns are nonlinear in nature or have a significant nonlinear component, except for the Russian capital market, which was expected considering the results of the Ljung-Box (with squares of the returns) and ARCH-LM tests. These findings allow the creation of efficient portfolio diversification strategies, opening room for market regulators to take steps to ensure better informational information for investors operating in these financial markets.

The Impact of Crude Oil Price Shocks on Bitcoin under the Russian–Ukrainian War

Nowadays, the Russian-Ukrainian war has been a hotly topic, and the war has shaken the global economy, especially in the international crude oil market. Also, as a popular financial instrument, the investors like to see Bitcoin as a hedging tool, but the problem of whether cryptocurrencies can hedge the volatility of commodity markets lacks a unified explanation. Therefore, the paper wants to find the relationship between Bitcoin and crude oil during the Russian-Ukrainian war. This paper uses data from Bitcoin, crude oil WTI futures, and crude oil Brent futures, and constructs the VAR model and ARMA-GARCH model based on these data. Ultimately, the article finds that the volatility of the international crude oil market only has little impact on Bitcoin. Thus, the investors do not need to worry about the high crude oil price caused by the war will affect Bitcoin’s yield and volatility, so Bitcoin seems like a great hedging instrument against the shock of the international crude oil market.

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.

Extreme connectedness between renewable energy tokens and fossil fuel markets

This paper examines the aspect pertaining to the returns connectedness between renewable energy tokens, namely, Powerledger-POWR and WePower-WPR, and the fossil fuel markets, namely, WTI oil, Brent oil, and Natural gas. For this purpose, we employed a quantile-based regression approach, in order to explore the dependence structures that exist under diverse market conditions. The results of the analysis show that the element of connectedness in the renewable energy tokens-fossil fuel market nexus is characterized by asymmetry and heterogeneity in the tails that are compared to the respective mean and the median. Under normal market conditions, the WTI oil market emerges as the main net transmitter of return spillover to the renewable energy tokens. Whereas, Brent oil and natural gas markets are the net receivers of the return spillover from the digital assets. However, under periods of extreme negative returns, the Brent oil market behaves as the main net transmitter of return spillover to the renewable energy digital markets. Whereas, under period of extreme positive returns, the natural gas market appears to be the main net transmitter of return spillover to the renewable energy digital markets. Therefore, it can be fathomed that on aggregate, renewable energy digital tokens are weakly connected with fossil fuel markets, thus suggesting the addition of renewable energy tokens in the portfolio of fossil fuel markets.

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.

Time-Varying Term Structure of Oil Risk Premia

We develop a framework to estimate time-varying commodity risk premia from multi-factor models using futures prices and analysts’ forecasts of future prices. The model is calibrated for oil using a 3-factor stochastic commodity-pricing model with an affine risk premia specification. The WTI oil futures price data is from the New York Mercantile Exchange (NYMEX) and analysts’ forecasts are from Bloomberg and the U.S Energy Information Administration. Weekly estimations for short, medium, and long-term risk premia between 2010 and 2017 are obtained. Results from the model calibration show that the term structure of oil risk premia moves stochastically through time, that short-term risk premia tend to be higher than long-term ones and that risk premia volatility is much higher for short maturities. An empirical analysis is performed to explore the macroeconomic and oil market variables that may explain the stochastic behavior of oil risk premia, showing that inventories, hedging pressure, term premium, default premium and the level of interest rates all play a significant role in explaining the risk premia.

A dynamic ensemble learning with multi-objective optimization for oil prices prediction

Accurately predicting oil prices is a challenging task since its complex fluctuation characteristics. This paper innovatively introduces the “metabolism” mechanism and sliding window technology and proposes a dynamic time-varying weight ensemble prediction model with multi-objective programming to ameliorate the oil price's prediction performance. This paper first adopts the random forest to select and generate the best feature sets. Second, different individual models are selected to build a heterogeneous ensemble prediction framework. Then, a multi-objective weight generation model is established by considering horizontal and directional accuracy. Moreover, the nondominated sorting genetic algorithm-II is utilized to compute the prediction errors of a single model at different stages and achieve model optimization selection and ensemble weight generation. Finally, we take Brent and WTI oil prices as the prediction objects to verify the effectiveness and superiority of the proposed model. The experimental results reveal that the dynamic time-varying weight ensemble forecasting model has excellent prediction capability for oil prices and can become an effective forecasting tool.

In search of time-varying jumps during the turmoil periods: Evidence from crude oil futures markets

Prior literature demonstrates that energy prices are characterized by time-varying jumps. However, earlier studies do not investigate if the intensity of such jumps appears to be higher amid periods of extreme volatility in comparison to normal periods. Employing the GARCH-jump model, this study examines whether jumps occurring in energy prices are an indicator of market crashes. To serve this purpose, we consider several downturns in oil markets spanning over the last few years. Our empirical analyses reveal that the conditional expected number of jumps in WTI and Brent oil futures prices increases significantly amid the depression periods, which is, however, not the case when the market functions normally. We, therefore, conclude that such clusters of jumps may contain predictive information for oil market crashes and thus provide early signals of future downturns. The findings further show that crude oil volatility, the US equity VIX, and economic policy uncertainty play a significant role in explaining the time-dependent jumps during the turmoil periods. The findings of our research could be useful for investors participating in global crude oil markets and for policymakers watching out for the impact of energy prices on the economy.

Exploring Dependence Relationships between Bitcoin and Commodity Returns: An Assessment Using the Gerber Cross-Correlation

We use a robust measure of non-linear dependence, the Gerber cross-correlation statistic, to study the cross-dependence between the returns on Bitcoin and a set of commodities, namely wheat, gold, platinum and crude oil WTI. The Gerber statistic enables us to obtain a more robust co-movement measure since it is neither affected by extremely large nor small movements that characterise financial time series; thus, it strips out noise from the data and allows us to capture effective co-movements between series when the movements are “substantial”. Focusing on the period 2014–2022, we construct the bootstrapped confidence intervals for the Gerber statistic and test the null that all the Gerber cross-correlations up to lag kmax are zero. Our results indicate a low degree of dependence between Bitcoin and commodities prices, both when we consider contemporaneous correlation and when we employ correlations between current Bitcoin and lagged (one day, one week, or one month) commodities returns. Further, the cross-correlation between Bitcoin and commodities’ returns, although scanty, shows an increasing trend during periods of economic, health and financial turbulence. This increased cross-correlation of returns during hectic market periods could be due to the contagion effect of some markets by others, which could also explain the strong dependence across volatilities we detected. Based on our results, Bitcoin cannot be considered the “new digital gold”.

Does geopolitical risk matter in crude oil and stock markets? Evidence from disaggregated data

This paper investigates the nonlinear Granger causality and spillover effects among geopolitical risk, crude oil and Chinese disaggregated sectoral stock markets by using a multivariate nonlinear Granger causality test and connectedness network analysis. The nonlinear Granger causality and spillover effects are shown to be heterogeneous across sectors. The nonlinear Granger causality indicates Brent oil is likely to play a more important role in geopolitical risk and Chinese stock markets, especially for the Energy and Financial sectors. But the connectedness network analysis suggests that geopolitical risk is the net receiver of spillover effects from WTI oil and the net transmitter to Brent oil. Chinese sectoral stock markets are the main provider of spillover effects in crude oil and geopolitical risk, and its spillover effects are stronger for the Consumer Discretionary, Industrials, Materials, Health Care and Information Technology sectors. In addition, crude oil may play an essential function as an intermediary receiver of Chinese sectoral stock market shocks to geopolitical risk.

Public attention, oil and gold markets during the COVID-19: Evidence from time-frequency analysis

This paper uses time-frequency analysis, including wavelet analysis and time-frequency domain causality, to evaluate the relationship between public attention to the COVID-19 pandemic, crude oil, and gold markets in the G7 countries over time and frequency. Empirical findings show that WTI oil lead gold returns during the COVID-19 outbreak, and vice versa when Omicron spread. The relationship between public attention to the COVID-19 and WTI oil/gold markets appears to be heterogeneous for G7 countries. European public attention caused by the COVID-19 outbreak has a strong impact on gold returns at the 32–64 day frequency, while public attention generated by Omicron has a significant effect on WTI oil returns at 4–128 day frequency. The public in the US and Canada is more concerned about the global stock and WTI oil markets slump than the COVID-19 pandemic. The Italian public seems to be the most sensitive to the EU's economic support plan. The heterogeneity of the public attention-oil/gold nexus in the G7 implies that portfolio diversification across markets and investment horizons may be extremely beneficial.

FACTORS IN THE FORMATION OF SUPER-CYCLES IN WORLD COMMODITY MARKETS

Over the past few years, the world’s leading commodity markets have seen an upward trend in prices, which may indicate the beginning of a new super-cycle or may be of a short-term nature, driven by current changes in demand and supply. Commodity super-cycles are important for the global economy, especially for macroeconomic policies in commodity-exporting countries, and are also reflected in the dynamics of international financial markets. The purpose of the article is to determine the essence and features of super-cycles in world commodity markets as well as to identify the factors that led to the increase in commodity prices in 2020-2022. To solve the objectives set in the article, a number of general scientific and special methods of scientific cognition are used, namely the method of theoretical generalization, historical and logical methods, descriptive-analytical method, analysis and synthesis, induction and deduction, abstractlogical method and method of economic-statistical analysis. The article outlines the mechanism of deployment of the conjunctural cycle in commodity markets and its connection with long cycles of business activity, which are conditioned by the implementation of revolutionary technological innovations. It discusses the dynamics of the main composite commodity indices. The factors influencing the growth of prices for commodity assets at the micro and macro levels are identified. The article traces changes in the course of commodity super-cycles during the 20th and early 21st centuries and investigates their causes. It is revealed that global inflationary processes, the dynamics of the US dollar index as well as the disruption of supply chains in international trade due to the COVID-19 pandemic had a significant impact on the global growth of commodity prices in 2020-2022. At the same time, forecasts about the recession of the global economy caused by the large-scale military aggression of russia in Ukraine and the long-term consequences of the pandemic as well as macroeconomic policies of large economies aimed at overcoming excessive inflation may somewhat cool the prices of commodity assets. To assess the sentiments and expectations of economic entities, the dynamics of the basis for WTI oil is analyzed, which gives grounds to conclude that the price fluctuations in global commodity markets in 2020-2022 are short-term in nature as well as to question the beginning of a new super-cycle. Identification and forecasts of conjunctural fluctuations in global commodity markets are important without any exaggeration for all economic entities – both for producers in making strategic as well as tactical management decisions on the development of production and in the formation of structural and macroeconomic policies of the country in order to increase the export potential of the national economy and to ensure its competitiveness.

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.

The Profitability of Technical Analysis during the COVID-19 Market Meltdown

This article explores the profitability of technical trading rules around the COVID-19 pandemic market meltdown for the S&P 500 index, Bitcoin, Comex gold spot, crude oil WTI, and the VIX. Trading rule profits are estimated from January to May 2020, including three sub-periods, on a high-frequency data set. The results reveal that the trading rules can beat the buy-and-hold trading strategy. However, only the Bollinger Bands and trading range break-out rules become profitable after transaction costs during the market crash. Moreover, it is found that composite trading signals effectively improve the profitability of technical analysis around the COVID-19 market crash.

Do pandemic, trade policy and world uncertainties affect oil price returns?

We investigate the influence of pandemic and trade policy uncertainty on the dynamics of oil price returns over the two last decades, using a Mixed-Frequency Vector Autoregressive (MF-VAR) model. We find that pandemic uncertainty and, more importantly, trade policy uncertainty significantly explain EU Brent and WTI oil price returns. Additionally, pandemic and trade policy uncertainty shocks are linked with lower (higher) oil price returns in the short-term (medium-term). Finally, while our mixed-frequency approach captures the persistent response of oil price returns to the uncertainty shocks, the single common-frequency (i.e., quarterly) framework only uncovers a muted reaction.

Evidence from the nonlinear autoregressive distributed lag model on the asymmetric influence of the first wave of the COVID-19 pandemic on energy markets

The COVID-19 pandemic remained a global risk factor and integrated into various means in the functioning of companies, economies and financial markets. Therefore, this paper investigates how COVID-19 influences the energy market in the main financial markets (China, France, Germany, Italy, Spain and the United States), using time series from February 28, 2020, to November 3, 2020. The goal of this research is to investigate the asymmetric impact of COVID-19 from leading financial markets on energy commodities. In this regard, the non-linear auto-regressive distributed lag (NARDL) framework is employed to capture the long-run asymmetric reactions. The econometric design allows to explore the long-term asymmetric reactions of dependent variables through positive and negative partial sum decompositions of changes in the explanatory variables. The quantitative results show a significant long-run asymmetric interdependence between the number of new SARS-CoV-2 incidence and mortality and the daily percent change in close price of future contracts pertaining to Brent oil, crude oil WTI, carbon emissions, gasoline RBOB, heating oil, Chukyo kerosene, and natural gas. Furthermore, no asymmetry is found in the case of ethanol and fuel oil futures. The novelty of this article is the study of the impact of COVID-19 on the energy sector during the first two waves of COVID-19 by applying the NARDL model that allows to capture long-term asymmetric reactions. Certainly, further research on this topic is necessary due to the permanent shifts in the pandemic, as well as the availability of longer data periods on COVID-19.

Interdependence between WTI Crude Oil Prices and the US Equity Market

The author checks the cointegration between WTI oil market and the US market of stocks represented by the S&P index. As it turns out not to exist, short-term relations are investigated. The study confirms that crude oil market significantly influences the stock market in the short run, however it does not give an unambigous answer if this impact is made by oil itself or together with the GBP/USD currency rate. Furthermore, the relation does not go in another direction which means that the stock market has no impact on the oil market. The Unrestricted Vector Autoregression model is built. The author uses weekly data and the research period is from April 1990 to May 2021. The study implies that stable crude oil prices are desirable in order not to destabilize stock markets whose instability threatens the real economy. Conclusions are vital for a wide group of entities such as policy makers, authorities, institutional and individual investors, as well as other financial market participants.

Information connectedness of international crude oil futures: Evidence from SC, WTI, and Brent

The first Yuan (RMB) denominated crude oil futures contract, SC, was launched in the Shanghai International Energy Exchange (INE) on 26 March 2018, which is extremely meaningful for China and other Asian countries by offering a new option of oil price risk management. To identify the information connectedness among this emerging contract and those mature WTI and Brent oil futures, we provide return and volatility directional connectedness evidence among them in both the time and frequency domains. The empirical results show that, firstly the three oil futures of SC, WTI, and Brent present high degree of total connectedness in the return and volatility series, implying tight information transfer among them. Secondly, the net directional connectedness results suggest that the SC can provide competitive information on oil returns and is a net and powerful contributor to volatility shocks. In addition, from a frequency-specific perspective, we find that the SC appears to be not only a net transmitter of return shocks on the medium- and long-term frequencies but also a net transmitter of volatility shocks consistently across the whole frequency ranges. The overall evidence suggests that China's new oil futures is an active participant in the international oil futures market, and may become an important product in information transmission across international crude oil futures markets by providing effective hedging instrument for crude oil producers, refiners, consumers, and investors, especially those in Asia.