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The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]

Recent examples of energy price downturns did not alter trade surpluses in commodity exporting countries. Economic theory contends that such developments may indicate that the changes observed in energy prices are perceived as permanent by the market. We perform a trend-cycle decomposition of oil prices and find that permanent shocks account for about 90% of oil price variation. The observed variability of oil prices attributable to permanent shocks may be explained by the increased financialization of oil markets. This finding is in line with the empirical evidence that the current account surplus is generally unaffected by permanent terms-of-trade shocks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image‐based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low‐frequency models for high‐frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends. [ABSTRACT FROM AUTHOR]

We examine the nexus between financial inclusion and energy poverty. Analysing data for 27 energy-poor countries in the Sub-Saharan Africa region over 2004–2021, we employ sequential (two-stage), panel-corrected standard error (PCSE) and two-step dynamic system GMM (generalized method of moments) regression models, and control for endogeneity, CSD, slope heterogeneity as well as stationarity and cointegration patterns of the variables. Our empirical results show that financial inclusion significantly reduces energy poverty in the selected energy-poor countries. The study also finds a positive significant association between energy access and GDP per capita, while oil price and energy intensity are inversely associated with energy access. The results are robust to different control variables, estimation methods and subsamples. These findings have strong policy implications for energy-poor countries and point to the need for appropriate policies to promote financial inclusion for reducing energy poverty. [ABSTRACT FROM AUTHOR]

The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization. [ABSTRACT FROM AUTHOR]

This article investigates the effect of international oil price uncertainty on investment efficiency using quarterly data of Chinese A-share listed companies from 2011 to 2020. It is discovered that uncertainty about oil prices increases principal-agent conflicts and information asymmetry in enterprises, leads to more inefficient investment, and ultimately lowers firms' investment efficiency. Furthermore, China's unique property rights foundation, firm size, and life cycle limit the aforementioned consequences. Additional investigation reveals that by raising companies' cash reserves, oil price uncertainty exacerbates overinvestment behavior. The study presented in this article can serve as a guide for businesses looking to make the best decisions in the context of volatile global oil prices and support their long-term, stable development. [ABSTRACT FROM AUTHOR]

Some US-listed country exchange-traded funds (ETFs) suffer from chronic and meaningful mispricing in the form of premiums or discounts relative to their fundamental value despite the presence of the creation/redemption mechanism. This mispricing is mainly attributed to the staggered information flow due to nonoverlapping time zones between the market where the ETF is listed and its underlying home market. This study provides out-of-sample evidence on the price formation of Gulf Cooperation Council (GCC) country ETFs and gauges the impact of mispricing on their underlying home markets. The GCC context is particularly insightful because these markets have nonoverlapping time zones with the US and follow distinct trading schedules. Our sample comprises daily data from three countries' iShares that exclusively track the Qatari, Saudi, and Emirati stock markets from 17 September 2015 to 14 March 2023. The results show that GCC ETFs are driven mainly by their net asset values (NAVs), albeit imperfectly, while the S&P500 exerts a relatively mild influence on these ETFs compared to other country ETFs, as reported by prior studies. Moreover, we find that crude oil prices positively and significantly impact GCC ETFs' pricing. When we control for unsynchronized trading days between the US and the GCC home markets, we find a structural difference between overlapping and nonoverlapping trading days. This structural difference manifests in a sluggish adjustment to correct mispricing in the ETF market on the day the home market is closed; however, other variables, including the S&P500, show no discernible difference, which refutes the overreaction explanation. This recurrent pattern is reflected in a clear day-of-the-week pattern in the price discovery these ETFs offer to their underlying home markets. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pneumatic spring dampers are extensively utilized within hydraulic systems, and the flow characteristics of the internal oil play a crucial role in determining noise and vibration levels. To validate the mechanical structure's reliability, the shock absorber was simplified and the fluid domain was extracted using a CFD method. The velocity field and pressure field under different conditions were then simulated and analyzed. A finite element model of the flow field was established, and its accuracy was verified by comparing simulation results of gas side pressure with experimental results. According to the working principle of the oil-gas spring, dynamic active surfaces in contact with the main piston and dynamic driven surfaces in contact with the floating piston were defined, determining moving conditions for dynamic grids. The results indicate that as the diameter of flow channels increases, there is a decrease in average pressure drop within the oil chamber (i.e., pressure loss within the flow field). Additionally, as bend angle increases, average pressure drop decreases. However, optimization effects become less significant beyond a certain bend angle [ABSTRACT FROM AUTHOR]

The study of Ellwanger and Snudden (J Bank Financ 154:106962, 2023) discovers a new and remarkable finding regarding the ability of the random-walk model using the end-of-month price of crude oil to forecast future monthly average crude oil prices out-of-sample. The magnitude and nature of the relative predictive gains lead the authors to question whether any other model can "beat" the end-of-month price random-walk out-of-sample. I make an attempt to do so by relying on plain end-of-month crude oil price autoregressive fractionally integrated moving average (ARFIMA) models. These models are more nuanced and at the same time comprehensively account for one of the most salient features of the price of crude oil, namely, its persistence. Consequently, a forecaster is inclined to believe that they might "beat" the end-of-month random-walk model. However, out-of-sample results demonstrate that a uniform (definitive) conclusion cannot be drawn. On the contrary, conclusions depend heavily on the definition of "beating", i.e. population-level versus finite-sample relative predictability, the forecast horizon, state of the business cycle and the choice of the crude oil price series itself. The decisions, judgments and dilemmas faced by the forecaster are presented and elaborated. [ABSTRACT FROM AUTHOR]

We contribute to the literature by being the first to examine the direction of causality between the different sources of oil price shocks and financial stress in the global financial markets (OFR), US, other advanced economies (OAE), and emerging markets (EM). Specifically, we aim to empirically answer a key question: Do global oil market shocks drive financial stress, or does financial stress spur oil market shocks? Using a two‐stage methodology based on the structural VAR (SVAR) and entropy‐based analysis over the period January 2000–October 2022, the results show that the links between financial stress and oil shocks are contingent on the type of shock. Within this, oil supply shock is mildly connected to financial stress; oil demand shock is vulnerable to innovation from financial stress; and oil‐specific demand shock has a noticeable time‐variable element wherein the shock prevails at the beginning of the sample and financial stress dominates the transmission at the end of the sample. [ABSTRACT FROM AUTHOR]

This study examines the intertemporal nature of countries' external adjustment by using two oil income shocks with different timings: giant oil discovery news shocks and contemporaneous oil revenue shocks from international oil price changes. Empirical estimates using a large panel of countries support the intertemporal theory. Net foreign assets hike immediately upon oil revenue shocks, but decline for the first 5 years after oil discoveries and rebound subsequently. These adjustments are largely through the current account but partially stabilized by valuation effects for oil revenue shocks. Oil discoveries attract FDI inflows, while oil revenue shocks increase foreign debt assets holdings. [ABSTRACT FROM AUTHOR]

Remittances are regarded as an important source of foreign exchange for the majority of low and middle-income countries, and thus have the potential to impact their aggregate economic activities. The current study investigates the impact of remittance inflows and international oil prices on India's trade balance from 1975 to 2020. We use a non-linear autoregressive and distributed lag model to examine the asymmetric impact of remittance and oil price changes on trade balance. The study discovered that rising remittance inflows have a detrimental long-run impact on the trade balance. It also demonstrates that while a positive oil price shock worsens the trade balance, a negative oil price shock improves it in the long run. As a result, the paper emphasises the need to reduce skilled migration, properly channel remittance revenues, develop financial institutions, and implement more efficient exchange rate policies in order to achieve long-term trade surpluses. [ABSTRACT FROM AUTHOR]

We use cointegration-based techniques to investigate the relationship between oil prices and the euro effective exchange rate taking also into account the influence of interest rates and stock prices. We find that higher oil prices cause a depreciation in the euro exchange rate (either nominal or real) in the short run. This suggests that during episodes of higher energy prices, the euro area consumer is faced with additional price pressures, as a depreciated euro causes import prices to further rise. Our study also indicates that as stock prices rise in the short and long term, the euro appreciates, while higher oil prices will likely result in overall higher policy rates and lower stock prices. [ABSTRACT FROM AUTHOR]

This document explores the concept of Brownian motion and its applications in various fields. It discusses the historical significance of Brownian motion in understanding the atomic nature of matter and highlights the work of German oceanographer Hasselmann, who used Brownian motion as inspiration for his Nobel Prize-winning climate evolution modeling. The document also discusses the use of Brownian motion in pedagogy, particularly in understanding sources of error in the oil drop experiment. It presents a study that focuses on measuring the diffusion coefficient using a Millikan oil drop apparatus, describing the experimental setup, method, and data analysis techniques. The document concludes by stating that this study can be beneficial for high school and first-year undergraduate students in improving their comprehensive skills. [Extracted from the article]

We test the impact of globalization on the inflation process (global slack hypothesis) for 15 emerging economies over the period (1996Q1-2019Q4). The novelty of this article lies in testing the impact of globalization on the inflation process. We introduce the smooth transition regression (STR) model of the Phillips curve to capture the gradual impacts of globalization on inflation dynamics. The main results are as follows. (i) The asymmetries in inflation exist that are not captured by the linear Phillips curve. (ii) The domestic output gap remains a main driving force of inflation. (iii) Globalization based on the trade weighted average of the domestic output gap does not affect the inflation process compared to other variables like the trade openness, import prices, real exchange rate and international oil prices. Our results yield important implications that the central banks of the emerging economies can stabilize inflation and output gap in an effective way, exploiting the inflation-output gap relationship. When economic expansion of the trading partners increases, the process of inflation dynamics changes in two economies (Brazil, and China). [ABSTRACT FROM AUTHOR]

This study investigates the relationship between consumer sentiment (CONS), inflation expectations (INEX) and international energy prices, drawing on principles from behavioral. We focus on Brent crude oil price and Henry Hub natural gas prices as key indicators of energy market dynamics. Based on the monthly data from January 2003 to March 2023, three wavelet methods are applied to examine the time-frequency linkage, while the nonlinear distributed lag model (NARDL) is used to verify the asymmetric impact of two factors on energy prices. The results highlight a substantial connection between consumer sentiment, inflation expectations and international energy prices, with the former in the short term and the latter in the medium to long term. Especially, these correlations are particularly pronounced during the financial crisis and global health emergencies, such as the COVID-19 epidemic. Furthermore, we detect short-term asymmetric effects of consumer sentiment and inflation expectations on Brent crude oil price, with the negative shocks dominating. The positive effects of these factors on oil prices contribute to observed long-term asymmetry. In contrast, inflation expectations have short-term and long-run asymmetric effects on natural gas price, and both are dominated by reverse shocks, while the impact of consumer sentiment on natural gas prices appears to be less asymmetric. This study could enrich current theories on the interaction between the international energy market and serve as a supplement to current literature. [ABSTRACT FROM AUTHOR]

Research has identified volatility transmission from the oil market to the tanker freight market through external shocks. However, in the presence of intricate nonlinear structures, the academic literature often encounters difficulties in identifying cycles and their correlations across various timescales. This paper provides a multi-market analysis to comprehend the information from shock effects on different tanker routes and multi-peak fitting. Under different shock regimes, crude oil market and tanker freight rate shocks could transit bidirectly. When events occur, the crude oil market prices the expectations. However, when the actual performance of the future market differs from the traders' predictions of the future market, a price gap exists. Generally, the trade opportunity is tough to catch up on because only partial information can be found. In this study, we investigate the volatility connection of multi-markets and shock effects to clarify previously undisclosed information using multi-peak analysis. The information gathered and double-checked by cargo markets, crude oil supply-demand balance, and tanker freight prices of various tanker types could assist us in identifying prospective trends and investment opportunities. The volatility of each market, as well as the correlation of multi-markets, gives insights to crude oil dealers, tanker market participants, and energy regulators. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors influencing inflation in Turkey between 2006:1-2023:11, taking into account the structural changes that occurred during periods of significant economic, political and geopolitical turmoils. For this purpose, we employ the Hacker and Hatemi-J (2006) bootstrap causality test along with the Fourier Toda-Yamamoto bootstrap causality to capture structural changes. Our findings indicate that when structural changes are taken into account, the drivers of inflation are monetary factors such as the money supply and exchange rate. Furthermore, an exogenous factor such as the oil prices is not a determinant of inflation, suggesting that inflation is a domestic factor. Given these findings, the study emphasizes the importance of strong and stable monetary policies in Turkey to control inflation and achieve price stability. [ABSTRACT FROM AUTHOR]

Purpose: This study examines the value implications of oil price uncertainty for investors in diversified firms using a sample of 922 USA firms from 2001 to 2019. Design/methodology/approach: Our study employs a panel dataset to examine the value implications of oil price uncertainty for diversified firm investors. We consider several alternative specifications to account for unobserved factors and measurement errors that could potentially bias our results. In particular, we use alternative measures of the excess value of diversified firms and oil price uncertainty, additional control variables, fixed-effects models, the Oster test, impact threshold for confounding variable (ITCV) analysis, two-stage least square instrumental variable (2SLS-IV) analysis and the system-GMM model. Findings: We find that the excess value of diversified firms, relative to a benchmark portfolio of single-segment firms, increases with high oil price uncertainty. The impact of oil price uncertainty is asymmetric, as corporate diversification is value-increasing for diversified firm investors only when the volatility is due to positive oil price changes and amidst supply-driven oil price shocks. The excess value increases irrespective of diversified firms' financial constraints and oil usage. Diversified firms become conservative in their internal capital allocations with high oil price uncertainty. Such conservatism is value-increasing for diversified firm investors, as it supports higher performance in response to oil price uncertainty. Originality/value: Our study has three important implications: first, they are relevant to investors in understanding the portfolio value implications of oil price uncertainty. Second, they are helpful for firm managers while comprehending the value-relevant implications of internal capital allocations. Finally, our findings are policy relevant in the context of the future of diversified firms in developed markets. [ABSTRACT FROM AUTHOR]

Purpose: The study tests risk-taking and risk-aversion capabilities while distinguishing between business group firms and stand-alone firms and considering oil price volatility. Second, this attempt to study the linkage between risk-taking during market down movements and when the firms have established themselves as product market leaders. Third, this study analyses the "sentiment" state, where it explores the reaction of corporations when the market is in the negative direction, and lastly, it explores the linkage between product market competition and risk-aversion. Design/methodology/approach: This study uses financial information for 1,273 non-financial companies and other required data from various sources. The study employs panel data and utilizes different empirical methodologies, including the generalized method of moments (GMM) estimator, to test the stated hypotheses. Findings: We find that the business group firms have more risk-taking proficiencies compared with the stand-alone firms. Moreover, this study discovers that the corporates avoid taking risks when the market is not performing well. Also, when the market is down and crude prices are high, the management expects high earnings in the future, willingly takes risks and shows that product market leaders do not follow the risk-aversion strategy. Practical implications: The empirical results indicate that oil price movement can restrict management's behaviour when choosing a risky investment project. Management should develop a robust policy that follows the group of firms. In the policy, the management should describe the level of risk that may be taken by the firm and implement it when required. Originality/value: Since we do not find any studies in this context, then there is a major and essential gap in the literature that this study should fill. [ABSTRACT FROM AUTHOR]

Summary: We provide new evidence that the transmission of oil price shocks to the US economy has changed with the shale oil boom. To show this, we develop a time‐varying parameter factor‐augmented vector autoregressive (FAVAR) model with a large data environment of state‐level, industry, and aggregate US data. The model effectively captures potential spillovers between oil and non‐oil industries, as well as variation over time. Specified in this way, we find that investment, income, industrial production, and (non‐oil) employment in most oil‐producing and some manufacturing‐intensive US states increase following an oil‐specific shock—effects that were not present before the shale oil boom. [ABSTRACT FROM AUTHOR]

Fluctuations in oil prices substantially impact both the real economy and international financial markets. Despite extensive studies on oil market dynamics and overnight momentum, a comprehensive understanding of the link between oil price changes and energy market momentum, as well as their broader influence on global financial markets, remains elusive. This study delves into the intricate mechanics of overnight momentum transmission within financial markets, focusing on its origin in oil price fluctuations and its overarching impact on market dynamics. Employing the quantile VAR method, we analyze daily market data from 3 January 2014 to 17 January 2024. This study emphasizes the significance of overnight momentum on the transmission of volatility, particularly in the tails of the distribution, and highlights the necessity for efficient strategies to govern financial stability. The shale oil revolution, COVID-19, the Russia–Ukraine war, and the Israel–Hamas conflict have significantly impacted the interconnectivity of financial markets on a global scale. It is crucial for policymakers to give priority to the monitoring of the energy market to reduce risks and improve the resilience of the system. [ABSTRACT FROM AUTHOR]

In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical results show that the CNN–LSTM model outperforms these models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between the international oil market and the West Texas Intermediate (WTI)—Western Canadian Select (WCS) price differential. Monthly data were collected for January 2005 to September 2023 from the U.S. Energy Information Administration, the Federal Reserve Bank of Dallas, the Canada Energy Regulator, and the Government of Alberta. The paper finds that a negative oil supply shock increases the differential with a delay. On the other hand, a positive oil-specific demand shock increases the WTI-WCS differential with a delay, while a positive aggregate demand shock increases the differential persistently. Overall, the three shocks in the international oil market account for 61% of the variability in the price differential in the long run. The aggregate demand shock is the most substantial factor in explaining the variation in the WTI-WCS differential in the long run. This indicates that the global oil market demand has an asymmetric effect on WTI and WCS prices. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper investigates the intricate relationship between oil and stock markets in the context of different oil price shocks and a country's energy profile. Focusing on five major oil-producing and consuming countries, the analysis reveals a significant, time-varying dimension in the oil-stock relationship. It highlights the importance of distinguishing between various types of oil shocks and a nation's reliance on oil revenues. The paper finds that countries heavily reliant on oil revenues exhibit robust responses to oil supply and demand shocks. In contrast, oil producers show more moderate reactions due to lower oil revenue dependence. This evidences the importance of recognizing the multifaceted impacts of oil shocks on different economies for making well-informed decisions in asset allocation, portfolio management, and policymaking. [ABSTRACT FROM AUTHOR]

This study explores the time-varying effects of crude oil prices (OP) and gold prices (GP) on the Turkish stock market using a weekly data series from November 26, 1989 to July 10, 2022. For this purpose, we develop a new hybrid technique, the rolling window-based nonparametric quantile causality test, which allows the investigation of time-varying causality at various quantiles. The results reveal that (i) under all market conditions, there is time-varying causality from crude OP and GP to Turkish stock market returns (SMR) and volatility. (ii) The causal effects of both crude OP and GP on stock market volatility are larger than their causal effects on SMR. (iii) The crude OP have a greater impact on SMR than the GP, while the GP has a greater impact on stock market volatility than the crude OP. (iv) Both crude OP and GP have the strongest (least) causal impact on SMR and volatility under normal (bullish) market conditions. (v) Crude OP and GP have a greater impact on stock market volatility than on stock returns under all market conditions. Overall, our results highlight that OP and GP have a strong impact on the Turkish stock market, and this impact varies by returns and volatility. Therefore, financial investors should consider the volatility of crude OP and GP in the Turkish stock market. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the resource curse hypothesis in an oil-dependent economy, Saudi Arabia, through examining the impact of oil price volatility on foreign ownership among Saudi listed firms. Design/methodology/approach: The study analyzes a unique data set of firm-level data on foreign ownership for the period 2009–2015. A multivariate regression model was applied to analyze the relationships under study. Findings: The analysis reveals a negative association between oil price volatility and foreign ownership in firms with high leverage and low stock volatility. Research limitations/implications: Policymakers are encouraged to develop policies to control shocks in the supply and demand of oil and enforce economic diversification. Investors can better understand the dynamics of an oil-based economy stock market based on the investment behavior of foreign investors and their response to oil price shocks. Originality/value: This study adds to the literature by analyzing the relationship understudy in an oil-rich and oil-dependent emerging economy, where its critical economic parameters are influenced by oil price volatility and it has the largest and the most liquid stock exchange in the MENA region. [ABSTRACT FROM AUTHOR]

In this study, we propose a model to forecast container throughput for the Singapore port, one of the busiest ports globally. Accurate forecasting of container throughput is critical for efficient port operations, strategic planning, and maintaining a competitive advantage. Using monthly container throughput data of the Singapore port from 2010 to 2021, we develop a Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. For the exogenous variables included in the SARIMAX model, we consider the West Texas Intermediate (WTI) crude oil price and China's export volume, alongside the impact of the COVID-19 pandemic measured through global confirmed cases. The predictive performance of the SARIMAX model was evaluated against a diverse set of benchmark methods, including the Holt–Winters method, linear regression, LASSO regression, Ridge regression, ECM (Error Correction Mechanism), Support Vector Regressor (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM) networks, and Prophet. This comparative analysis was conducted by forecasting container throughput for the year 2022. Results indicated that the SARIMAX model, particularly when incorporating WTI prices and China's export volume, outperformed other models in terms of forecasting accuracy, such as Mean Absolute Percentage Error (MAPE). [ABSTRACT FROM AUTHOR]

This study investigates the dynamic, multi-scale relationship between sentiment related to the COVID-19 pandemic and extreme returns in crude oil. The recently developed COVID-19 indices are employed to gauge pandemic sentiment. Utilizing daily data spanning from January 2020 to December 2021, Granger's linear and nonlinear causality tests reveal that indices nonlinearly influence extreme fluctuations in West Texas Intermediate and Brent crude oil prices. Interestingly, a reciprocal causation is also identified: extreme crude oil returns significantly affect the indices. Furthermore, the wavelet transform coherence analysis sheds light on the indices' ability to predict extreme crude oil price volatility across specific time-frequency domains, displaying diverse distributions and lead-lag patterns among the sub-indices. Our study underscores the efficacy of indices in anticipating extreme fluctuations in crude oil values during the COVID-19 pandemic, carrying important implications for investors, scholars, and policymakers. [ABSTRACT FROM AUTHOR]

This research incorporates crude oil price series with different time scales, China's dirty energy, new energy and green bond index into a system, so as to investigate the spillover effects during some major periods. The results indicate that the spillover effects of short-term oil price fluctuations are mild, and the green bond market is the least affected. While the long-term oil price fluctuations have profound effects on the aforementioned three markets, the spillovers are especially enhanced by extreme events. Our results are verified by appropriate robustness tests. [ABSTRACT FROM AUTHOR]

Analogous to an experienced mariner choosing a safe harbour depending on the wind direction, we hypothesize safe-haven asset(s) are conditional on the cause of the market fall. Using oil markets as a salient case study, we find that traditional safe-haven assets, such as the US dollar and government bonds, act as safe havens only when oil prices fall due to declines in actual or expected demand. On the other hand, stock markets provide safe-haven protection when oil prices fall due to increases in oil supply. Therefore, our results suggest that papers that seek to identify safe-haven assets in response to declines in a given asset's return need to test for the possibility that the identified assets might not be safe-haven assets in all circumstances. [ABSTRACT FROM AUTHOR]

Crude oil is an essential energy source that affects international trade, transportation, and manufacturing, highlighting its importance to the economy. Its future price prediction affects consumer prices and the energy markets, and it shapes the development of sustainable energy. It is essential for financial planning, economic stability, and investment decisions. However, reaching a reliable future prediction is an open issue because of its high volatility. Furthermore, many state-of-the-art methods utilize signal decomposition techniques, which can lead to increased prediction time. In this paper, a model called K-means-dense-sparse-dense long short-term memory (K-means-DSD-LSTM) is proposed, which has three main training phrases for crude oil price forecasting. In the first phase, the DSD-LSTM model is trained. Afterwards, the training part of the data is clustered using the K-means algorithm. Finally, a copy of the trained DSD-LSTM model is fine-tuned for each obtained cluster. It helps the models predict that cluster better while they are generalizing the whole dataset quite well, which diminishes overfitting. The proposed model is evaluated on two famous crude oil benchmarks: West Texas Intermediate (WTI) and Brent. Empirical evaluations demonstrated the superiority of the DSD-LSTM model over the K-means-LSTM model. Furthermore, the K-means-DSD-LSTM model exhibited even stronger performance. Notably, the proposed method yielded promising results across diverse datasets, achieving competitive performance in comparison to existing methods, even without employing signal decomposition techniques. [ABSTRACT FROM AUTHOR]

This study aims to identify the sources of spillovers affecting tourism tokens and classify the type of assets to which they correspond. Using daily data for different asset classes from June 2018 through November 2022, we employ a TVP-VAR methodology to test the connectedness between two tourism tokens, two leading travel equity indices, and the two dominant cryptocurrencies, namely, Bitcoin and Ethereum. The findings show that tourism tokens are relatively independent of fluctuations in the traditional sources affecting the travel and leisure sector, such as the U.S. dollar, the price of oil, or travel equity indices. These results hint that tourism tokens are more closely related to cryptocurrencies rather than pure travel goods. The results may help decision-makers in the travel and hospitality industries considering the use of tourism tokens identify the potential forces impacting them. [ABSTRACT FROM AUTHOR]

New Zealand relies on imported fossil fuels for about 38% of its primary energy. The country's energy demand is expected to grow due to population and economic growth, which will put more pressure on the energy system. Besides, resource scarcity, energy price volatility, and environmental challenges have made energy security a major concern for New Zealand and other countries. Given the lack of significant research on the effects of energy security factors in New Zealand, this study aims to shed light on the primary determinants of energy security using the dynamic autoregressive distributed lag method based on time series data from 1978 to 2021. The study found that a long-run link exists between energy security and energy intensity (energy efficiency), renewable energy use, fossil fuel consumption, and global oil prices. Real GDP, renewable energy consumption, and energy security were found to improve energy security, while fossil fuel consumption and world oil prices had a negative impact. The study also revealed a one-way causality from real GDP, fossil fuel consumption, and renewable energy use to energy security. In contrast, the relationship between energy intensity and energy security is bidirectional. Simulation results showed that global crude oil prices have a lower impact on energy security compared to other variables and are most responsive to a 5% shock in fossil fuel consumption, followed by economic growth. [ABSTRACT FROM AUTHOR]

This paper investigated the use of linear models to forecast crude oil futures prices (WTI) on a monthly basis, emphasizing their importance for financial markets and the global economy. The main objective was to develop predictive models using time series analysis techniques, such as autoregressive (AR), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), as well as ARMA variants adjusted by genetic algorithms (ARMA-GA) and particle swarm optimization (ARMA-PSO). Exponential smoothing techniques, including SES, Holt, and Holt-Winters, in additive and multiplicative forms, were also covered. The models were integrated using ensemble techniques, by the mean, median, Moore-Penrose pseudo-inverse, and weighted averages with GA and PSO. The methodology adopted included pre-processing that applied techniques to ensure the stationarity of the data, which is essential for reliable modeling. The results indicated that for one-step-ahead forecasts, the weighted average ensemble with PSO outperformed traditional models in terms of error metrics. For multi-step forecasts (3, 6, 9 and 12), the ensemble with the Moore-Penrose pseudo-inverse showed better results. This study has shown the effectiveness of combining predictive models to forecast future values in WTI oil prices, offering a useful tool for analysis and applications. However, it is possible to expand the idea of applying linear models to non-linear models. [ABSTRACT FROM AUTHOR]

The impact of climate risk on economic and financial fields has attracted considerable interest from academics and practitioners. Recent literature documents that climate risk can affect oil volatility through the dual channel of economic activities and financial markets. Inspired by this, this paper uses climate policy uncertainty (CPU) to examine the predictability of crude oil price volatility. The empirical results indicate that changes in the CPU, rather than the original CPU, can forecast oil volatility. The revealed volatility predictability is both in-sample and out-of-sample. Asset allocation exercise shows that this finding is also economically significant. Furthermore, the predictive ability is mainly concentrated during economic expansions and the CPU contains climate risk that is not available for other predictors. Finally, CPU change indicators can explain the changes in crude oil consumption, which is the economic source of their predictive power. [ABSTRACT FROM AUTHOR]

Recent theoretical developments in economics distinguish between risk and ambiguity (Knightian uncertainty). Using state-of-the-art methods with intraday stock market data from February 1993 to February 2021, we derive financial ambiguity and empirically examine the effect of shocks to it on the price and volatility of crude oil. We provide evidence that ambiguity carries important information about future oil returns and volatility perceived by investors. We validate these results using Granger causality and in-sample and out-of-sample forecasting tests. Our findings reveal that financial ambiguity is a possible factor that explains future drops in oil prices and their increased variability. Our findings will benefit scholars and investors interested in how financial ambiguity shapes short-term oil prices. [ABSTRACT FROM AUTHOR]

Migration and private remittances have been continuously increasing over the years. For many developing countries, the quantum of remittances is greater than other forms of capital, such as FDI, FII, etc. The remittances may be used to fill the gap in potential funding sources for economic development in emerging countries like India. Migrations from India to the United States (US) and the flow of remittances to India have been continuously increasing over the years. The paper studies the macroeconomic factors influencing remittances to India. The study employs ARDL technique to identify the macroeconomic factors such as GDP, term premium, CPI, etc. of US, in addition to the domestic factors of India and price of Brent and other commodities such as gold, silver, and platinum that influence the flow of remittances to India. Further, the study also employs NARDL techniques to identify the asymmetric impact of macroeconomic variables in US on remittance flows to India. CDM technique is also used to decipher the shock evolution of independent variables with regard to remittance flows to India. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of global geopolitical risks and global supply chain pressures on global inflation for the monthly period of 1999M1–2022M12. The investigation is based on a structural vector autoregression model, where the effects of global oil prices and global monetary policy are controlled for. Four alternative measures of inflation are used, including headline, core, food, and energy inflation. The empirical results show that disruptions in global supply chains are the main drivers of global inflation in the long run as the corresponding shocks explain the lion's share of volatilities in headline inflation (by 32%), core inflation (by 30%), and food inflation (by 22%), followed by oil price shocks and policy rate shocks. In comparison, energy inflation is explained the most by oil price shocks (by 55%) followed by supply chain shocks and policy rate shocks. Positive supply chain pressure and oil price shocks have positive and statistically significant effects on headline inflation even after five years, whereas positive policy rate shocks have negative and statistically significant effects on headline inflation in the long run. In contrast, positive shocks to geopolitical risk result in higher headline inflation only up to one year, with insignificant effects in the long run. Several policy implications follow. [ABSTRACT FROM AUTHOR]

Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning. [ABSTRACT FROM AUTHOR]

We build and estimate a dynamic, structural model of the world oil market to quantify the impact of the shale revolution. We model the shale revolution as a decrease in shale production costs and find that the resultant increase in shale production lowers oil prices by 24% in the short run and 48% once the shale oil transition is complete. Current oil price volatility is lowered by 8% to 23% depending on the horizon. We also find that OPEC core acts to keep its market share constant in the face of the dramatic increase in shale production. [ABSTRACT FROM AUTHOR]

This study delved into various aspects of noodle production, emphasizing the impact of waste fermented black carrot powder (WFBCP) concentration and drying techniques. Higher concentrations of WFBCP increased cooking times, particularly in freeze-dried (FD) samples, resulting in varying water absorption and notable cooking losses. Color variations, influenced by both WFBCP concentration and drying methods, led to generally darker hues in the FD samples. Significant differences in the water content were observed across treatments. Ash content increased significantly with higher WFBCP concentrations, particularly at 40%, indicating the impact of WFBCP on ash formation. Noodle samples exhibited a significant rise in oil content with increasing WFBCP concentration, reaching a peak at 20%. Total Phenolic Content notably increased at higher WFBCP concentrations, peaking at 40%, suggesting potential nutritional benefits. Similarly, antioxidant activity increased significantly with higher WFBCP concentrations, indicating enhanced protection against food oxidation. Total monomeric anthocyanin content surged with increased WFBCP concentration, establishing WFBCP as a noteworthy source of anthocyanins. HPLC analysis revealed elevated levels of phenolic compounds when WFBCP was added to noodles, with FD-WFBCP showing the highest content. Freeze-drying effectively preserved the phenolic compounds, while the protein content remained stable. Sensory analysis, conducted using a 0–10 scale, emphasized the impact of processing methods and WFBCP concentrations on attributes such as color, stickiness, chewing, flavor, and overall rating. This study underscores the importance of precise control over WFBCP concentrations and processing for enhancing noodle attributes. Principal component analysis, explaining 90.8% of the total variation, highlights distinct differences between control and treated samples. [ABSTRACT FROM AUTHOR]

This study investigates the extent and efficiency of global energy transition by examining the impact of renewable electricity generation on non-renewable electricity generation in 73 economies from 1990 to 2020. This is done to empirically scrutinise the assumption of perfect displacement as adopted by Intergovernmental Panel on Climate Change and other environmental and economic analysts. The estimated models also control for the effects of globalisation, economic growth, and international crude oil prices. The PMG-ARDL estimation demonstrates that renewable energy is able to partly displace the non-renewable energy and that the global energy transition is far from perfect displacement. More specifically, the coefficient of renewable energy generation is − 0.157, indicating that more than 6 units of alternative energy are necessary for displacing one unit of fossil fuel energy at the global level. However, the findings reveal that globalisation, economic growth, and crude oil prices negatively influence the energy transition by pushing up the non-renewable energy generation. To delve into finer details, the ARDL findings uncover that 1% increase in globalisation, economic growth, and crude oil prices increases the non-renewable energy generation by 0.88%, 0.75%, and 0.03%, respectively. The Driscoll–Kraay and feasible generalised least squares estimation techniques also confirm these baseline findings. Potential policy implications for enhancing the efficiency of global energy transition are also discussed. [ABSTRACT FROM AUTHOR]

Many studies have examined the asymmetric effect of US dollar-denominated crude oil prices on petroleum product prices. The 'rockets and feathers' argument suggests that a crude price increase raises petroleum product prices more than a corresponding decrease in crude prices lowers product prices. However, for the countries that do not use the US dollar as a medium of exchange, petroleum product prices are also affected by the exchange rates. This paper analysed the asymmetric effects of both US dollar-denominated crude oil prices and exchange rates on local currency-denominated diesel prices for 27 European countries in the short run as well as long run. The overall empirical evidence suggests that, in the short run, diesel prices react more to crude oil price increases than to a decrease, parallel to the 'rockets and feathers' argument. However, contrary to that argument, the long-run adjustment is the opposite. As for exchange rate shocks, again the 'rockets and feathers' argument holds and diesel prices respond more to exchange rate depreciation than appreciation in the short and long run. [ABSTRACT FROM AUTHOR]

The past decade witnessed a surge in anti-corruption activities in post-Soviet oil-dependent countries. Despite the scale of the anti-corruption measures, and their political implications, the extant literature paid limited attention to the comparison of these campaigns, including differences in their motivations, functions, objectives, and targets. Aiming to fill this research gap, this study employs a mixed-methods research design that combines fixed-effects panel data analysis with a paired-case qualitative analysis of Russia and Kazakhstan. I argue that anti-corruption policies are the result of either economic necessity or the desire to secure power through buttressing regime legitimacy. Consequently, a significant drop in oil price due to the COVID-19 crisis is likely to result in a major anti-corruption campaign targeting high-level officials in oil-rich countries. [ABSTRACT FROM AUTHOR]