Search Results (44)

Using the OLS model with different quantiles of GPR, we aim to examine the impact of GPR, EPU, and VIX on monthly international crude oil prices, including WTI, BRENT, and DUBAI prices, while differentiating the impact on different levels of risks. Afterwards, we use the GARCH and MGARCH models to assess the impact of these metrics on the volatility of oil prices, and the spillover effects between oil prices with these three metrics as exogenous shocks. Our result indicates (i) global oil price is negatively affected by GPRT at a moderate level of risks in longer time intervals; (ii) GPR, EPU, and VIX affect oil price’s volatility, and (iii) there exists a stronger long-persistent spillover effect between BRENT and DUBAI, with these metrics as exogenous shocks, while WTI is not affected. Full article

In the context of heightened oil price volatility, mastering technical trading strategies is essential for informed investment and sound decision making. This study explores the effectiveness of contrarian technical trading strategies in the Brent crude oil market, aiming to enhance returns in the face of persistent market fluctuations. Utilizing historical price data, this research formulates trading rules based on overbought and oversold signals derived from the Relative Strength Index (RSI) and the Stochastic Oscillator Indicator (SOI). It assesses their performance through a range of Average Holding Period Return (AHPR) metrics, emphasizing the 250-day AHPR as a proxy for one-year returns. The findings show that RSI-based strategies, especially those using a threshold of 25, are most effective in oversold conditions, achieving peak profitability of over 40% in Quarter 2. The conclusions highlight the importance of parameter flexibility, strategic timing, and responsiveness to market dynamics in optimizing the contrarian strategy performance. The implications suggest investors and managers can refine strategies by accounting for behavioral biases, market timing, and flexible parameters, while enhancing big data analytics in technical trading. Full article

This study analyzes the market efficiency of crude oil markets, namely Brent and West Texas Intermediate (WTI), during three different periods: pre-COVID-19, during the COVID-19 pandemic, and during the ongoing Russia–Ukraine military conflict. To evaluate the efficiency of crude oil markets, wavelet entropy is computed from price, return, and volatility series. Our empirical results show that WTI prices are predictable during the Russia–Ukraine military conflict, but Brent prices are difficult to predict during the same period. The prices of Brent and WTI were difficult to predict during the COVID-19 pandemic. Returns in Brent and WTI are more difficult to predict during the military conflict than they were during the pandemic. Finally, volatility in Brent and WTI carried more information during the pandemic compared to the military conflict. Also, volatility series for Brent and WTI are difficult to predict during the military conflict. These findings offer insightful information for investors, traders, and policy makers in relation to crude oil energy under various extreme market conditions. Full article

This paper examines the self-similarity (long memory) in prices of crude oil markets, namely Brent and West Texas Instruments (WTI), by means of fractals. Specifically, price series are decomposed by stationary wavelet transform (SWT) to obtain their short and long oscillations. Then, the Hurst exponent is estimated from each resulting oscillation by rescaled analysis (R/S) to represent hidden fractals in the original price series. The analysis is performed during three periods: the calm period (before the COVID-19 pandemic), the COVID-19 pandemic, and the Russia-Ukraine war. In summary, prices of Brent and WTI exhibited significant increases in persistence in long movements during the COVID-19 pandemic and the Russia-Ukraine war. In addition, they showed a significant increase in anti-persistence in short movements during the pandemic and a significant decrease in anti-persistence during the Russia-Ukraine war. It is concluded that both COVID-19 and the Russia-Ukraine war significantly affected long memory in the short and long movements of Brent and WTI prices. Full article

This study investigated market efficiency across 20 major commodity assets, including crude oil, utilizing fractal analysis. Additionally, a rolling window approach was employed to capture the time-varying nature of efficiency in these markets. A Granger causality test was applied to assess the influence of crude oil on other commodities. Key findings revealed significant inefficiencies in RBOB(Reformulated Blendstock for Oxygenated Blending) Gasoline, Palladium, and Brent Crude Oil, largely driven by geopolitical risks that exacerbated supply–demand imbalances. By contrast, Copper, Kansas Wheat, and Soybeans exhibited greater efficiency because of their stable market dynamics. The COVID-19 pandemic underscored the time-varying nature of efficiency, with short-term volatility causing price fluctuations. Geopolitical events such as the Russia–Ukraine War exposed some commodities to shocks, while others remained resilient. Brent Crude Oil was a key driver of market inefficiency. Our findings align with Fractal Fractional (FF) concepts. The MF-DFA method revealed self-similarity in market prices, while inefficient markets exhibited long-memory effects, challenging the Efficient Market Hypothesis. Additionally, rolling window analysis captured evolving market efficiency, influenced by external shocks, reinforcing the relevance of fractal fractional models in financial analysis. Furthermore, these findings can help traders, policymakers, and researchers, by highlighting Brent Crude Oil as a key market indicator and emphasizing the need for risk management and regulatory measures. Full article

In this work, Functional Data Analysis (FDA) is used to detect behavioral patterns in the Bolsa de Valores de Colombia (BVC) in reaction to the global crises caused by COVID-19 and the war in Ukraine. The oil price fluctuation curve is considered a covariate. The FDA’s distinctive ability is to represent stock values as smooth curves that evolve over time and provide new insights into the dynamics of the BVC. The methodology makes use of functional multivariate techniques applied to the smoothed curves of the closing prices of the main stocks of the BVC. The results show that the correlations of the oil curve with the average market curve change from almost null or low in the global period to extremely significant in time windows immediately after the beginnings of COVID-19 and the war in Ukraine, respectively. On the other hand, the velocity curves, which are used to evaluate the stock market volatility, show a pattern of synchronization of companies in the crisis periods. Furthermore, in these crisis periods, the companies in BVC showed a high synchronization with the Brent crude oil price. In conclusion, this work shows the usefulness of the FDA as a complement to time series analysis in the study of stock markets. The results of this research could be of interest to academic researchers, financial analysts, or institutions. Full article

Challenges in investment decisions for new fuels remain due to uncertain scenarios regarding profitability. There is also a challenge to improve production efficiency and waste utilization, either for biomass or by-products. This study evaluates the economic potential of biomethane production within sugarcane biorefineries through the principles of the circular economy and economic feasibility. To obtain price data for CBios, Brent crude oil, and natural gas, stochastic models based on GBM and Monte Carlo simulations were applied to project prices and assess revenue potential over a 10-year horizon. Price data were incorporated to assess market correlations and revenue scenarios. Key findings reveal that biomethane’s price stability, driven by its strong correlation with oil markets, supports its viability as a renewable energy source, while CBio presents a weak correlation and limited price predictability with present challenges for long-term planning. Economic modeling indicates high investment returns, with IRR values surpassing 35% in conservative scenarios and payback periods from 2 to 6 years. These results highlight biomethane’s potential for energy efficiency, carbon emission reduction, and the creation of new revenue through waste use. We conclude that targeted investments in biomethane infrastructure, coupled with policy and market support, are essential for achieving global sustainability goals. Full article

Crude oil prices have a key meaning for the economies of most countries. Their levels shape the general production costs in many sectors. Oil prices are also a base for financial derivatives like CFD contracts, which are popular nowadays. Due to these reasons, the possibility of an effective prediction of the direction of future changes in the price of crude oil is especially significant. Most existing works focus on the analysis of daily closing prices. This kind of approach results, on the one hand, in losing important information about the dynamics of changes during the day. On the other hand, it does not allow for the modelling of short-term price changes that are especially important in cases of financial derivatives having crude oil as their base instrument. The goal of the following article is the analysis of possible applications of a binary–temporal representation in the modelling and construction of effective decision support systems on the crude oil market. The analysis encompasses all researched state models, e.g., those applying mean and trend analysis. Also, the selection of parameters was optimized for Brent crude oil rates. The presented research confirms the high effectiveness of our state modelling system in predicting oil prices on a level that allows for the construction of financially effective investment decision support systems. The obtained results were verified based on proper backtests from different quotation periods. The presented results can be used both in scientific analyses and in the construction of investment support tools for the crude oil market. Full article

Wavelet coherence analysis (WCA) examines the dynamic interactions between economic policy uncertainty (EPU) in Brazil, Chile, Colombia, and Mexico and key external variables, using monthly data from 2010 to 2022. The findings reveal the following: (i) medium-term co-movements (4–16 months) between EPU and global financial indicators, including the Chicago Board Options Exchange (CBOE) Market Volatility Index (RVIX), Merrill Lynch Option Volatility Estimate Index (RMOVE), and Global EPU Index (RGEPU), emphasizing the sustained influence of financial volatility on domestic policy environments, particularly during global turbulence; (ii) significant interactions between EPU and the Climate Policy Uncertainty Index (RCPU) in resource-dependent economies like Brazil and Colombia, with pronounced effects in medium- and long-term horizons; (iii) bidirectional relationships between Brent crude oil prices (RBRENT) and EPU in Brazil, Colombia, and Mexico, where oil price fluctuations shape policy uncertainty, especially during global market disruptions; and (iv) notable co-movements between EPU and the Dow Jones Sustainability World Index (RW1SGI) in Brazil, Chile, and Mexico, highlighting sensitivity to shifts in sustainability-driven markets. These results underscore the need for economic diversification, strengthened financial safeguards, and integrated climate risk management to mitigate external shocks. By exploring the time–frequency dynamics of global uncertainties and domestic policy environments, this study provides actionable insights for fostering resilience and stability in Latin America’s interconnected economies while addressing vulnerabilities to global market volatility and sustainability transitions. Full article

Crude oil is a raw and natural, but nonrenewable, resource. It is one of the world’s most important commodities, and its price can have ripple effects throughout the broader economy. Accurately predicting crude oil prices is vital for investment decisions but it remains challenging. Due to the deficiencies neglecting residual factors when forecasting using conventional combination models, such as the autoregressive moving average and the long short-term memory for prediction, the variational mode decomposition (VMD)-empirical mode decomposition (EMD)-Transformer model is proposed to predict crude oil prices in this study. This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. Ultimately, we apply the Transformer model to predict the decomposed modal components and superimpose the results to produce the final forecasted prices. Further empirical test results demonstrate that the proposed quadratic decomposition composite model can comprehensively identify the characteristics of WTI and Brent crude oil futures daily price series. The test results illustrate that the proposed VMD–EMD–Transformer model outperforms the other three models—long short-term memory (LSTM), Transformer, and VMD–Transformer in forecasting crude oil prices. Details are presented in the empirical study part. Full article

Investigating the stability of and fluctuations in the energy market has long been of interest to researchers and financial market participants. This study aimed to analyze the Chinese energy market, focusing on its volatility and response to financial tensions. For this purpose, data from eight major financial companies, which were selected based on their market share in Shanghai’s and Shenzhen’s financial markets, were collected from January 2014 to December 2023. In this study, stock prices and trading volumes were used as the key variables to build bootstrap-based minimum spanning trees (BMSTs) using ratio mutual information (RMI). Then, using the sliding window procedure, the major network characteristics were derived to create an anomaly-detection tool using the multivariate exponentially weighted moving average (MEWMA), along with the Brent crude oil price index as a benchmark and a global oil price indicator. This framework’s stability was evaluated through stress testing with five scenarios designed for this purpose. The results demonstrate that during periods of high oil price volatility, such as during the turbulence in the stock market in 2015 and the COVID-19 pandemic in 2020, the network topologies became more centralized, which shows that the market’s instability increased. This framework successfully identifies anomalies and proves to be a valuable tool for market players and policymakers in evaluating companies that are active in the energy sector and predicting possible instabilities, which could be useful in monitoring financial markets and improving decision-making processes in the energy sector. In addition, the integration of other macroeconomic factors into this field could strengthen the identification of anomalies and be considered a field for possible research. Full article

Crude oil prices crashed and dropped into negative territory at the onset of the COVID-19 pandemic. This extreme event triggered a series of great-magnitude aftershocks. We seek to investigate the cascading dynamics and the characteristics of the series immediately following the oil market crash. Utilizing a robust method named the Omori law, we quantify the correlations of these events. This research presents empirical regularity concerning the number of times that the absolute value of the percentage change in the oil index exceeds a given threshold value. During the COVID-19 crisis, the West Texas Intermediate (WTI) oil prices exhibit greater volatility compared to the Brent oil prices, with higher relaxation values at all threshold levels. This indicates that larger aftershocks decay more rapidly, and the period of turbulence for the WTI is shorter than that of Brent and the stock market indices. We also demonstrate that the power law’s exponent value increases with the threshold value’s magnitude. By proposing this alternative method of modeling extreme events, we add to the current body of literature, and the findings demonstrate its practical use for decision-making authorities—particularly financial traders who model high-volatility products like derivatives. Full article

This paper examines the predictive ability of volatility in time series and investigates the effect of tradition learning methods blending with the Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Using Brent crude oil returns price volatility and environmental temperature for the city of Sydney in Australia, we have shown that the corresponding forecasts have improved when combined with WGAN-GP models (i.e., ANN-(WGAN-GP), LSTM-ANN-(WGAN-GP) and BLSTM-ANN (WGAN-GP)). As a result, we conclude that incorporating with WGAN-GP will’ significantly improve the capabilities of volatility forecasting in standard econometric models and deep learning techniques. Full article

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data. Full article

Precisely forecasting the price of crude oil is challenging due to its fundamental properties of nonlinearity, volatility, and stochasticity. This paper introduces a novel hybrid model, namely, the KV-MFSCBA-G model, within the decomposition–integration paradigm. It combines the mixed-frequency convolutional neural network–bidirectional long short-term memory network-attention mechanism (MFCBA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. The MFCBA and GARCH models are employed to respectively forecast the low-frequency and high-frequency components decomposed through variational mode decomposition optimized by Kullback–Leibler divergence (KL-VMD). The classification of these components is performed using the fuzzy entropy (FE) algorithm. Therefore, this model can fully exploit the advantages of deep learning networks in fitting nonlinearities and traditional econometric models in capturing volatilities. Furthermore, the intelligent optimization algorithm and the low-frequency economic variable are introduced to improve forecasting performance. Specifically, the sparrow search algorithm (SSA) is employed to determine the optimal parameter combination of the MFCBA model, which is incorporated with monthly global economic conditions (GECON) data. The empirical findings of West Texas Intermediate (WTI) and Brent crude oil indicate that the proposed approach outperforms other models in evaluation indicators and statistical tests and has good robustness. This model can assist investors and market regulators in making decisions. Full article