Analysis of Financial Speculators’ Herd Behaviour Impact on the Instability of Commodity Prices. Evidence from Weekly WTI Crude Oil Market Data Using Uncertainty Theory (2018–2024)

A Financialization Model of Crude Oil Markets

A novel approach to Predict WTI crude spot oil price: LSTM-based feature extraction with Xgboost Regressor

The impact of the COVID-19 pandemic on the energy market – A comparative relationship between oil and coal

Time-frequency analysis between Bloomberg Commodity Index (BCOM) and WTI crude oil prices

We propose a new supply and demand-based correlation model of clean energy indices and energy prices with the influence of energy on clean energy business including renewables. Empirical studies estimate the model parameters using the stock indices and energy prices including S&P Global Clean Energy Index (GCE), Wilderhill Clean Energy Index (ECO), S&P/TSX Renewable Energy and Clean Technology Index (TXCT), S&P 500, WTI crude oil prices, and Henry Hub (HH) natural gas prices. Results show the correlations between GCE or ECO and WTI crude oil or HH natural gas prices are positive and an increasing function of the corresponding energy prices. It seems reasonable because the values of renewable energy businesses, which sell electricity in the spot market, are enhanced by the increase in energy prices, considering that electricity spot prices increase in line with energy prices. In contrast, results show the correlations between S&P 500 and WTI or HH prices are still positive but a decreasing function of the energy prices. This sharp contrast may come from the fact that the S&P 500 listed companies’ businesses can be damaged by high energy prices while not applicable to GCE and ECO companies. Regarding TXCT, the correlations with WTI are positive and are a decreasing function of WTI while those with HH tend to be positive and are an increasing function of HH. It may suggest that TXCT is not fully functioning but still developing as a clean energy index, taking into account the results of GCE and ECO.

The drop in crude oil price during late 2014 has had a significant impact on all nations. While some countries have reaped the benefits of low oil prices, others have suffered greatly. As a result, it is no surprise that many academics have attempted to develop reliable models to forecast crude oil price. In the age of information and social media, the role of Twitter and Facebook has become increasingly more relevant in understanding our environment. Many academics have exploited this wealth of data to extract features including sentiment and word frequency to build reliable forecasting models for financial instruments such as stocks. These methodologies, however, remain unexplored for the prediction of crude oil prices. The purpose of this investigation to develop a novel model that uses sentiment of United States foreign policy and oil companies' to forecast the direction of weekly WTI crude oil prices. The investigation is divided into three parts: 1) a methodology of collecting tweets relevant to US foreign policy and oil companies'; 2) a statistical analysis of the novel features using Granger Causality Test; 3) the development and evaluation of three machine learning classifiers including Naive Bayes, ANNs, and SVM to predict the direction of weekly WTI crude oil. The findings of the statistical analysis showed strong correlation between the novel inputs and WTI crude oil price. The results of the statistical tests were then used in the development of the predictive model. SVM was found to provide best forecasting performance. Furthermore, using these novel features, the predictive accuracy exceeded that of existing models mentioned in literature

Are green bonds environmentally friendly and good performing assets?

Dependence and spillover among oil market, China's stock market and exchange rate: new evidence from the Vine-Copula-CoVaR and VAR-BEKK-GARCH frameworks

Several papers have documented the cross-correlations across wide range of markets using different methods and the results are being used for the improvisation of market efficiency. In this paper, we have investigated the crosscorrelations between Gold market and WTI Crude oil market using MF-X-DFA. Quantitatively, we have employed the recently developed MF-X-DFA method for the data of Gold and WTI Crude oil markets and we have confirmed the existence of cross-correlation between Gold and Crude oil markets. We have also found that these cross-correlations are strongly multifractal in the short term and weakly multifractal in long the term. Moreover, their behavior for small fluctuations is persistent and those of large fluctuations are anti persistent in the short term. Cross-correlations of very small fluctuations and large fluctuations are persistent but whereas for small fluctuations are anti-persistent in the long term. Furthermore, based on multifractal spectrum, we have produced substantial evidences to determine crosscorrelation behaviors exhibit multifractal features. Our results have significant implications to market efficiency.

Purpose of the study: In this paper, we investigate the impact of the changes in crude oil prices and fluctuation of foreign exchange rate on the operating performances of Taiwanese 3PL industry.Methodology: Vector Autoregression Models. Through the empirical model, we find that all the 3PL companies are more suffered to the volatility of WTI and Dubai crude oil prices, but Dubai is insignificant to the warehousing companies. In the fluctuations of foreign exchange rate, some have positive effect and some are negative.Main Findings: All the Taiwanese 3PL companies are more suffered to the volatility of WTI and Dubai crude oil prices, however Dubai is insignificant to the warehousing companies. Moreover, we find an interesting result, that is, for some companies operating performance, the impact on the volatilities of crude oil have the same sign but in opposite direction. For example, in our empirical results, the stock returns are positively correlated to volatilities of Dubai and Brent crude oil prices, however, WTI’s volatility has negative impact on them.Implications: It implies that the company can make a “natural hedge” strategy to hedge the crude oil volatility risk by forming a portfolio which pools these three commodities together. In this way, we made recommendations to the company’s decision-making reminding that the company should make a portfolio of foreign exchange and crude oil price fluctuations in the hedge strategy to enhance the company’s risk management operations and to reduce the loss caused by these factors.Novelty/Originality of this study: This study contributes in the existing literature for an empirically study of a firm-level evidence from Taiwanese 3PL companies.

High volatility of commodity prices is due, among others, to sudden global events. Only recently, COVID-19 pandemic and the war in Ukraine have caused crisis in commodity markets. In particular, the prices of strategic goods such as oil have become very volatile. To hedge against adverse price movements in this commodity, business and investors use commodity options. However, making reasonable buy/sell decisions requires a good mix of market understanding, technical and fundamental analysis, and risk management. In this paper, we use association analysis to discover buy decision rules for the investors in the WTI crude oil options market. The rules are discovered based on moving averages of WTI crude oil prices and the price differences of this commodity between selected days. The effectiveness of the discovered rules is evaluated using indicators related to the level of payout of the buyer of call options and the cost of acquiring these options. The results of experiments on data from 26 August 2008 and 15 November 2022 indicate that the decision rules discovered can effectively support decisions to take a long position in call options and can significantly contribute to effective hedging against unfavorable oil price movements.

The goal of this study is to look into how changes in crude oil prices affect GDP per capita and exchange rate fluctuations.to investigate the influence of crude oil price shocks on GDP per capita and exchange rate movements. This research employed yearly time series data for the price of crude oil, exchange rate (USD/INR), and GDP per capita, from 1990 to 2020. Arithmetical tools such as Descriptive, Unit Root, Granger Causality Test, and OLS Model were applied. The present study discovered a strong bi-directional Granger causality effect of Dubai crude oil prices on exchange rates, as well as a bi-directional Granger influence of exchange rates on WTI crude oil prices. The diagnostic tests were successfully passed by the estimated models. According to the OLS model, the exchange rate was driven only by the price of Dubai crude oil, although the price of WTI crude oil influenced both the GDP per capita and the exchange rate over the research period. The key policy recommendation derived from this analysis is that the Reserve Bank of India (RBI) must depreciate the rupee, first to restore much-needed exchange rate stability, then to stimulate domestic manufacturers, and finally, to attract foreign capital inflows.

The energy commodity options market is characterized by high price volatility, often exceeding that of traditional asset classes such as stocks, bonds, interest rates, or stock indices. This volatility is directly reflected in the pricing of options. As one of the key determinants of the option premium, volatility implies that any significant fluctuations in the underlying asset’s market can lead to higher costs for option buyers. Consequently, sudden and unpredictable events – such as the outbreak of the COVID-19 pandemic – can significantly alter the dynamics and characteristics of the options market. The purpose of this article is to analyze and compare the state of the commodity put options market before and after the outbreak of the COVID-19 pandemic, using WTI crude oil options contracts as a case study. The study examines a four-year period – two years before and two years after the onset of the pandemic –focusing on American put options with maturities of no more than 30 days. The analysis includes options with various strike prices, deviating by 5% from the WTI crude oil price at the position’s opening. The findings indicate a significantly higher level of option premiums across all option categories, along with a notably lower frequency of option exercise. Prior to the pandemic, put options with the highest strike price (5% above the WTI price at the time of position opening) and the shortest maturities provided the most effective hedge against declines in oil prices. Although this category remained the most favorable during the pandemic, the frequency of profitable option exercises was 2.5 times lower compared to the pre-pandemic period.

The study on the computational modelling of Nigeria’s crude oil prices during the Russia – Ukraine war was conducted to determine and select an appropriate model that best describes the functional behaviour of crude oil prices in Nigeria during the Russia – Ukraine war. The research data was the daily price of crude oil prices and the time under review collected from the Central Bank of Nigeria website, and spanned the period from February 24, 2022, to June 13, 2023. The total observations summed up to three hundred and eighteen days (318). Six models were estimated for the study namely; linear, quadratic, cubic, log-linear, linear-log, and log-log models. Nevertheless, the Log-linear Model, which had the values (S= 0.045; R2 = 67.6%; F = 660.29; p=.000), and the smallest accuracy measures (MAE = 0.030; MSE = 0.002; MAPE = 0.034), is adjudged as the best-fit model among the six built regression models. As a result, the Log-linear estimation has the most impact on the crude oil prices of Nigeria during the period of the Russia –Ukraine war under review. The study hereby recommends forecasting crude oil prices using the log-linear model.

How does investor attention affect international crude oil prices?