Real Oil Research Articles

Oil Price Uncertainty and Consumer Sentiment in Advanced Economies

How does oil price uncertainty affect consumer sentiment in advanced economies? Is the response of consumer sentiment to exogenous positive and negative oil price shocks symmetric or asymmetric? Motivated to answer these questions, this paper provides a comprehensive examination of the effects of real oil price shocks on consumer sentiment in the G7 economies in the context of two classes of empirical models. With the application of a bivariate structural GARCH-in-Mean VAR model we find that (in general) oil price uncertainty has a negative and statistically significant effect on consumer sentiment in the G7 countries. Moreover, using a test of symmetry, we find that the relationship between oil prices and consumer sentiment is in general asymmetric. JEL Classification: C32, D12, Q43, O57

Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data

Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky–Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry.

Do Supply Chain Pressures Affect Consumer Prices in Major Economies? New Evidence from Time-Varying Causality Analysis

This study explores the dynamic causality between the consumer price index and supply chain pressure index in two large economies. Despite extensive research into the role of supply chains at the firm level, literature on their impact on time-varying inflation and macroeconomic stability is scarce. Using data from the United States and the European Union from January 2000 to December 2021, we employ novel time-varying causality techniques to examine the evolving relationships among inflation, supply chains, real gross domestic product, interest rates, and oil prices. Our findings show that supply chains have a significant dynamic relationship with consumer prices, particularly during economic crises like the 2008 global financial crisis and the COVID-19 pandemic. Robustness tests including impulse response functions and the autoregressive distributed lag model confirm this causality. These results are critical for policymakers, emphasizing the importance of supply chains in controlling inflation and achieving global economic resilience and stability.

An investigation into the performance and perikinetics of Brassica nigra meal in the treatment of real vegetable oil refinery condensate effluent.

In this study, the treatment of vegetable oil refinery plant condensate effluent (VORCE) having high total suspended solids (TSS) and chemical oxygen demand (COD) generated from acid oil unit was focused. The utilization of waste Brassica nigra meal (BNM) as protein flocculant in treating VORCE was explored. The B. nigra meal flocculant (BNMF) exhibited a crystalline nature, with the presence of amino and carboxyl functional groups, rendering it highly efficient (89.69% efficiency) in floc formation. Zeta potential and particle size (-5.6mV and 240.68 nm, respectively) indicate BNMF's effectiveness in initiating floc formation. The interactive effects of pH, dosage, settling time on COD, and TSS removal were investigated using the Box-Behnken design. At an optimal pH of 6.9 and BNMF dosage of 0.77 g/L, a maximum removal of 85.38% COD and 72.56% TSS was obtained. The perikinetic theory for the coagulation-flocculation followed a second-order rate reaction with high Kc (0.0001 L/mg min), low settling time (37.04 min), and high collision efficiency (2.703 × 1017), indicating the model's significance in achieving maximum COD and TSS removal. These findings highlight the potential use of BNMF in the treatment of VORCE, leading to circular economy by valorizing waste from mustard oil extraction and zero discharge. PRACTITIONER POINTS: Valorization of waste Brassica nigra meal (BNM) as a potent protein flocculant Optimization for vegetable oil refinery condensate effluent (VORCE) treatment was done. Interactive effects of the process parameters were analyzed using Design expert. Perikinetic theory for VORCE treatment follows second-order reaction rate with high Kc.

Parametric Study on Venturi Pressure Drop for Two Phase Oil (D130)-Water Flow for Different Operating Conditions – An Experimental Investigation

The governing parameters which affect pressure drop of two-phase oil-water flow across the venturi meter are water cut and angle of inclination. The study investigates experimentally the effect of inclination and water cut on pressure drop measurements across different venturi meters with beta ratios (β) = 0.4, and 0.6 for oil–water two-phase flow experiments in a 3-inch pipe for different operating conditions. Two-phase inclinable flow loop has been employed for conducting the experiments for different fluid mixture flow rates and water cuts. The working fluids utilized are Exxsol mineral oil (D130) and potable water. The experiments were conducted for water cuts varying from 0 to 100% in steps of 20%, flow rates ranging from 2000 to 12000 barrels per day (BPD), and for horizontal and vertical flow loop inclinations (0◦ and 90◦). Liquid flow rates considered in the study match flow rates of real oil wells. The results indicate that the venturi pressure drop varies linearly with water cuts for both β ratios (0.4 and 0.6). The study indicates that the effect of inclination on venturi pressure drop is not appreciable for all water cuts and flow rates. Also, from the experimental results it can be concluded that venturi with β = 0.6 is favourable for multiphase flow metering (with flow rates ranging from 8000 to 12000 BPD). The tangible findings of the study will be helpful in combating multi-phase flow challenges in oil and petroleum industries.

Revisiting the Nexus Between Oil Prices and Economic Activity for Asian Economies Using MIDAS

The objective of this paper is to re-examine the causal relationship between oil prices and economic activity for five Asian economies. We apply the unrestricted MIDAS (U-MIDAS) model using monthly data for real oil prices and quarterly data for real GDP for the period from 1998Q1 to 2019Q4. The causal nexus between oil prices and economic activity is also studied by means of wavelet analysis to investigate whether the relationship between the variables changes over different time scales. The key empirical results under the MIDAS approach show that there is a clear significant causal link from oil prices to economic activity, which is not as clearly found under the standard VAR approach. In addition, our results using impulse response functions suggest that the five Asian economies respond, in general, positively in economic activity to an oil price shock at shorter time horizons (less than two years), while the positive responses switch to mostly negative ones at longer time horizons (from two to four years). Our results, in general, support that the (theoretically) expected negative causal nexus between oil price fluctuations and economic activity for oil importers is dominant over longer time horizons for our dataset. An exception to this pattern is China, which has a dissimilar country profile in terms of its oil market and production structure from the profiles of the other Asian economies in the study. JEL Classification: C32, E32, O53, Q43

Financial Interdependencies: Analyzing the Volatility Linkages between Real Estate Investment Trusts, Sukuk, and Oil in GCC Countries

This study investigates the financial interconnections among Real Estate Investment Trusts (REITs), sukuk (Islamic bonds), and oil in Gulf Cooperation Council (GCC) nations. The study sample comprises S&P GCC Composite Equity Real Estate Investment Trusts (REITs) Shariah, the S&P GCC Bond and Sukuk Index, and the OPEC crude oil basket on a daily basis. The duration of coverage spans from 2014 until the beginning of 2024. The TVP-VAR methodology is utilized to examine the interrelationship among the assets. The results indicate that Real Estate Investment Trusts (REITs) and oil are sources of volatility transmission, whereas sukuk is a recipient of volatility within the network. Examining the net pairwise directional linkages of two assets, namely REITs and oil markets, reveals that they transfer their volatility to the sukuk market. Moreover, a reciprocal relationship exists between REITs and oil regarding volatility spillover. It means that REITs act as transmitters to the oil markets during specific periods, while the influence is reversed at other times. This study implies that portfolio managers and investors can discern the volatility patterns of assets in order to enhance their risk-management techniques. For policymakers, comprehending the interdependence of certain asset classes provides valuable knowledge for formulating regulations that might stabilize the financial system and foster economic growth. From a research and academic perspective, this study enhances understanding of the interconnections between different financial asset classes and pricing dynamics in financial markets.

Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling—An Oil and Gas Sector Case Study

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.

Solar-activated tin oxide photocatalysis for efficient naphthenic acids removal and toxicity reduction in oil sands process water

This research studied, for the first time, the effect of activating tin oxide (SnO2) under simulated solar light for treating real oil sands process water (OSPW). The solar/SnO2 system effectively eliminated fluorophore organic contaminants, classical naphthenic acids (O2-NAs), and oxidized NAs (Oxy-NAs) from OSPW. The best experimental conditions to remove over 90 % of O2-NAs were found to be 0.5 g/L SnO2 under 8 h of irradiation. HO• and O2•– species identified by electron paramagnetic resonance (EPR) analysis played an important role in the degradation of NAs and other contaminants in real OSPW. The initial toxic effects of untreated OSPW were noticeably reduced after treatment, with a reduction of approximately 50 % in acute toxicity using Microtox® bioassay and over 80 % in the level of bioavailable hydrocarbons. In addition, the process also demonstrated a significant reduction in immunotoxicity as measured using an immune cell bioassay and reduced the toxic effects on Staphylococcus warneri using an adapted bacterial minimal inhibitory concentration (MIC) viability assay. These results suggest that treated OSPW by SnO2 under solar light has high environmental compatibility, indicating it is safe for reuse in further applications.

Volatility Transmission between Oil Price and Exchange Rate

This article presents a study on the transmission of oil price volatility to the exchange rates of 14 countries (net oil exporters and importers) during the period from 02/01/2000 to 31/11/2022. The aim is to compare the effect of oil price fluctuations on exchange rate volatility based on the country's nature. Using ARCH, GARCH, and GARCH-BEKK models, our results reveal that the real effective exchange rate is significantly linked to fluctuations in the real oil price for both categories of countries: oil importers and exporters. These findings have important implications for monetary, fiscal, inflationary, and trade policies for these countries.

Investigation of demetallization of Ni and V in crude oil using spherical polyelectrolyte brushes with adjuvant

Nickel and vanadium in crude oil adversely affects the quality of petroleum coke and refining process. Polymer brushes bearing high-density ligands have shown promising nickel removal ability. In this work, submicron polybutadiene (PB) grafted by aminodithio- carbamate acrylamide (PAMDTC@PB) was prepared and characterized by the infrared spectroscopy, dynamic light scattering, and elemental analysis. Based on results of molecular simulation, PAMDTC was selected as ligand molecule at first. Then the nickel and vanadium removal rates by PAMDTC@PB and traditional polyacrylic acid brushes (PAA@PB) were evaluated through the electric desalination process, both in the model and real oil samples. The result shows that S atom in AMDTC is most likely to chelate with metal porphyrin compounds and AMDTC molecule presents highest chelating activity. Compared with PAA@PB, the removal rate of Ni and V porphyrin compounds with the aid of PAMDTC@PB increases significantly, with an optimized removal rate of 46.91 % and 56.81 %. To achieve better metal removal efficiency and higher utilization rate of prepared polymer brushes, sodium dimethyl dithiocarbamate (SDD) is introduced into the electric desalination process as a adjuvant. The results indicate that the addition of SDD leads to a increment of 10–30 % to the removal rate of Ni and V and a reduction of brush dosage by 200–400 ppm in the electric desalination process, exhibiting great potential of industrial applications of novel desalination process with adjuvant.

The economic and fiscal transition costs of global climate mitigation in fossil fuel export dependent economies

Global decarbonization has large implications for trade and financial flows, and with few economies likely to be more affected than those relying heavily on exporting fossil fuels. This study first identifies 40 highly fossil fuel dependent economies and then, through several empirical strategies, examines the potential implications for oil rents, public finances and economic growth from global decarbonization. First, it is estimated that as a group, countries would lose more than 60 percent of oil rents alone, or between (present value adjusted) US$12–14 trillion, for the period 2023–2040 under a global ‘net-zero 2050’ versus a ‘business as usual’ scenario, - a loss equivalent in size to between 120 and 142 percent of GDP. Second, local projections model estimates suggest that there are large and persistent adverse growth and fiscal impacts associated with global decarbonization in emerging market and developing economy oil exporters. Here, a 10 percentage point (pp) decrease in (real) oil price inflation is associated with a decline of 1.58 percent in (real) GDP four periods after the shock; a decrease in annual government revenue (as a percentage of GDP) by between 0.6 and 0.8 pp and; an addition of about 0.5 pp to government debt per year. Finally, the study discusses relative vulnerabilities, and the domestic and international policy options needed to mitigate transition cost. Aside from a handful of primarily high-income countries, most countries have little or no savings to help them cushion an inevitable steep fall in demand for their main export and source of revenue, and more than one third of countries are characterized by either low or medium levels of human development. Whereas several countries are relatively well positioned to successfully mitigate the transition cost provided urgently prioritized domestic reforms, a substantial subgroup of countries will need international assistance to avoid potentially large negative socio-economic consequences.

Impact of 4D Seismic Data Assimilation in the Production Forecasting of a Heavy Oil Turbidite Sandstone Field Offshore Brazil

The incorporation of 4D seismic data can be done at model characterization and data assimilation steps, the latter applying the observed data to update the model properties. In this work, we evaluate the impact of 4DS data in the production forecasting results investigating the influence on these distinct steps. We employ a prior model set generated without 4DS data and another prior set with 4DS in the characterisation. We perform, then, the DA in three different ways: (1) using only well history data with a prior set without 4DS; (2) using well history and 4DS data with this same prior set; and (3) using well history and 4DS data with the prior set generated with 4DS data. We employ the Ensemble Smoother with Multiple Data Assimilation method for data assimilation and implement a procedure to adjust the well productivities and injectivities after DA. This work assesses the differences among the three DA cases in terms of field oil production forecasting, model attributes and model misfits, the latter evaluated in terms of the assimilated data set and of a different data set for validation purposes only. We apply the work to a real heavy oil turbiditic field located offshore Brazil. The case application demonstrates a successful data assimilation study on a real field integrating well history and 4DS data. The results show that the utilisation of 4DS in both model characterisation and DA steps brought value to the final uncertainty reduction results. The posterior model ensemble with 4DS data in model characterization and DA provided the lowest model misfits overall to the full data set, being the best choice for application in field production forecasting studies. The case without 4DS data provided significantly more pessimistic field oil production than the cases with 4DS, making very evident the importance of employing 4DS at uncertainty reduction workflows.

In situ-floating organic dispersive solid-phase extraction based on putrescine as a new adsorbent for the extraction of ions from crude oil prior to ICP-MS

ABSTRACT Quantification of ions in complex matrices like crude oil is an outstanding issue because of the high complexity of the sample. Herein, a facile, reliable, and precise floating dispersive solid-phase extraction procedure was introduced for the extraction of several ions (B, Ca, Cd, Cu, Cr, Ag, Fe, Mn, Ni, Na, Ba, Mg, Al, Si, Sn, Ti, Mo, V, Pb, Zn, P, and K) in which the need for adding complexing agent for the extraction of ions, centrifugation for separation of adsorbent, and shaker to enhance the contact area of the sample solution with absorbent was eliminated. These advantages were provided by using putrescine as the absorbent for the extraction of the analytes. To reach this goal, a proper amount of putrescine was dissolved in ethanol and injected into a diluted crude oil solution. During this step, putrescine formed stable complexes with the ions and extracted them from the solution and collected on top of the solution easily. The floated phase was collected by a spatula, and the extracted ions were determined by inductively coupled plasma mass spectrometry. Validation parameters of this method showed that low limits of detection (0.003–3.62 ng g−1) and quantification (0.009–12.0 ng g−1), broad linear range (12.0–80,000 ng g−1), acceptable relative standard deviation (≤5.3%) for intra- and inter-day precisions and high extraction recovery (72–90%) were the features of the method. The method was successful in the determination of the analytes in real crude oil samples.

Highly sensitive analysis of fatty aldehydes in vegetable oils using a novel coumarin-based fluorescent probe by HPLC for quality control

A novel fluorescent probe, 3-(4-((hydroxylamino)methyl)-1H-pyrazol-1-yl)-7-methoxyl coumarin (HAPMC), has been designed and synthesized. The compound exhibits exceptional fluorescence properties due to a coumarin-pyrazolyl fluorophore. Moreover, it incorporates an N-hydroxylamine functional group capable of reacting with aldehydes to yield nitrone derivatives under mild conditions. This unique combination renders it an ideal derivatization reagent for aldehyde analysis. A standard method was established for the quantitative analysis of eight fatty aldehydes by utilizing high-performance liquid chromatography with fluorescence detection (HPLC-FLD) and a pre-column derivatization process, which has low detection limits (0.3–1.2 nM), satisfactory reproducibility (intraday and interday relative standard deviations in the range of 1.29 %-2.92 % and 1.73 %-3.45 %), and an extensive linear range (25–5000 nM) with excellent linearity (R2 ≥ 0.9989). The spiked recoveries of the fatty aldehydes range from 89.6 % to 106.0 %. The greenness and sustainability of our approach are reinforced by evaluations conducted with the AGREE and RGB 12 algorithms. The established method was successfully applied to the qualitative and quantitative detection of fatty aldehydes in nine real vegetable oil samples, including peanut, canola, rice, soybean, olive, rapeseed, corn, sunflower, and blended oils. The fatty aldehydes are known to have adverse effects on human health due to their carcinogenic and mutagenic properties. The findings revealed that hexanal and nonanal were the most prevalent saturated fatty aldehydes, with a notable increase in the total aldehyde concentration observed as the temperature and duration of heating increased. Compared to alternative analytical techniques, this pre-column derivatization method utilizing HAPMC displayed simplicity, reliability, and sensitivity. The results demonstrate the potential for broad application of this approach in various important research domains, including food quality control, environmental monitoring, and medical research.

Production optimization in a fractured carbonate reservoir with high producing GOR

The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR.

Increasing the accuracy of oil recovery factor predictions by integrating lithology data

The object of research in the paper is the process of oil extraction during flooding. The Buckley-Leverett method, which is widely used to estimate oil production in flooding, has certain limitations that lead to uncertainty in the results. This paper proposes to extend the Buckley-Leverett algorithm by integrating lithological data. This approach allows to take into account the influence of geological characteristics of the formation on the process of displacement of oil by water, which leads to a significant increase in the accuracy of forecasting the oil production coefficient. The effectiveness of the proposed method is confirmed on the basis of data analysis of a real oil field. The methodology for calculating the oil recovery coefficient during flooding using lithological dissection is presented. In this work, the steps of determining the oil recovery coefficient were analytically determined, which achieves a certain degree of accuracy due to the inclusion of the lithological characteristics of the permeable zone of the formation. The basic calculation of the lithological distribution over the layer was performed using the Kriging method. To confirm the accuracy of the Buckley-Leverett method, taking into account lithological dissection, the use of data analysis, including an experimental histogram and a theoretical normal distribution plot, is proposed. For data analysis, one hundred cases of lithological distribution were generated using the Sequential Indicator Simulation method. The comparative analysis of the data of the experimental histogram and the theoretical graph of the normal distribution of the determination of oil recovery coefficients by the Buckley-Leverett method for cases with and without lithological dismemberment allows to quantitatively assess the accuracy of both studied options. On the basis of a real oil field, it is shown that the accuracy of oil recovery coefficients by the Buckley-Leverett method, taking into account lithological fragmentation, exceeds the similar method without taking into account lithological fragmentation by 11 %.

Assessing the effectiveness of local organic materials as sorbents for oil spill response in Arctic waters

The paper presents the results of a comparative assessment of the sorption characteristics of regional natural raw materials of organic origin: sphagnum moss (Sphаgnum palustre L.), fucus algae (Fucus vesiclousus), a composite based on them and peat in comparison with the synthetic sorbent OilSorb used in the practice of emergency rescue units to eliminate accidental oil spills. Experimental data have been obtained on potential pollutants in the Barents Sea (oil ARCO and diesel fuel) and the water capacity of sorbents for standard conditions at temperatures of 21–23 °C according to GOST 33627-2015 and in the temperature range of 10–12 °C typical for the summer period on the Kola Peninsula. The sorption characteristics of the materials under study have been evaluated in the "sea water – petroleum product" system, which simulates a real oil spill in the marine area. Tests in a model system simulating real conditions demonstrate significant changes in the efficiency of using sorption materials shown by them under standard conditions. The study has established the dynamics of the distribution of sorbed oil products and water in the materials under consideration, as well as residual oil products in the water of the "sea water – ARCO oil" and "sea water – diesel fuel" systems at different exposure times. During an exposure time of 30, 60, 90 minutes under the conditions of a system simulating a real oil spill in the water area, the material based on sphagnum moss has the most effective and time-stable indicators of sorption of oil products.

Piezoceramic membrane with built-in ultrasound for reactive oxygen species generation and synergistic vibration anti-fouling

Piezoceramic membranes have emerged as a prominent solution for membrane fouling control. However, the prevalent use of toxic lead and limitations of vibration-based anti-fouling mechanism impede their wider adoption in water treatment. This study introduces a Mn/BaTiO3 piezoceramic membrane, demonstrating a promising in-situ anti-fouling efficacy and mechanism insights. When applied to an Alternating Current at a resonant frequency of 20 V, 265 kHz, the membrane achieves optimal vibration, effectively mitigating various foulants such as high-concentration oil (2500 ppm, including real industrial oil wastewater), bacteria and different charged inorganic colloidal particles, showing advantages over other reported piezoceramic membranes. Importantly, our findings suggest that the built-in ultrasonic vibration of piezoceramic membranes can generate reactive oxygen species. This offers profound insights into the distinct anti-fouling processes for organic and inorganic wastewater, supplementing and unifying the traditional singular vibrational anti-fouling mechanism of piezoceramic membranes, and potentially propelling the development of piezoelectric catalytic membranes.

Assessing the baseline model of WTI oil and stock returns under financial volatility and spillover effects

Do financial volatility shocks matter for WTI oil and stock markets? They should since world economic activity responds to volatility. To investigate this question, we revisit two empirical frameworks widely used in the literature. Our baseline involves the standard identification of oil shocks in structural vector autoregressions (SVAR) with (world) oil supply, demand, and real oil prices. We then propose a global vector autoregressive (GVAR) model integrating 14 major world economies. We introduce the volatility measure (VIX), various stock markets, and compare two oil demand proxies: real economic activity (REA) based on commodity prices and world industrial production (WIP). The results of the impulse response analysis do not indicate major differences across models. However, when measuring the share of oil price variance explained by shocks to VIX (or to U.S. high-bond corporate yield) SVAR overestimates the influence of VIX. We find (world) demand shocks explain a larger part of the variance of oil and stock markets compared to supply shocks. In SVAR, new information coming from a one standard deviation shock to VIX is incorporated negatively and instantaneously on real oil prices: −1.6 for REA and −1.4 for WIP. In GVAR these are reduced slightly to −1.2 from VIX and to −0.6 if coming from corporate bond yield shocks. Our study highlights the sensitivity of econometric results to distinct approaches: one that identifies shocks and another that captures spillover effects.