Oil Volatility Research Articles

Bi-layered oil encapsulates formed by polydopamine-supported in situ complex coacervation: Investigation of structure formation and sustained release performance

In polydopamine (PDA)-supported in situ complex coacervation, PDA is pre-coated onto emulsion droplets, and further applied as an interlayer to support the interfacial immobilization of coacervates biopolymers. In the current research, the stepwise immobilization of gelatin (GE) and gum Arabic (GA) onto PDA layer is monitored to in-depth investigate the mechanism of PDA layer as coating material and functional substrates. GE and GA were applied as adsorbates, with different forms of PDA (spherical shell, planar solid, and nanoparticles) as substrates. Real-time observations of the binding steps were performed using quartz crystal monitor with dissipation (QCM-D) and isothermal titration calorimetry (ITC). Meanwhile, using PDA-supported coacervates as encapsulating material, sustained release of essential oil volatiles was also evaluated, either dispersed in detergent solution or freeze dried in open air. The anti-oxidation performance was determined by measuring the absorbance of lipid hydroperoxide using UV spectrophotometer. The results revealed that PDA enabled synergistic immobilization of biopolymers through chemical bonding and adhesive force during the processing of in situ complex coacervation, thus enhanced protection of core compounds with two-stage releasing kinetics and prevented hyperoxidation. Also, PDA-based colloids and substrates also exhibit controllable conformation characteristic, provide a promising prospect as versatile intermediate functional biomaterials for encapsulation.

The role of investors’ fear in crude oil volatility forecasting

We study whether investors’ fear can predict oil price volatility. The proxies used for investors’ fear are the CBOE Crude Oil Volatility Index (OVX), Google searches for “oil price”, and United States Oil Fund (USO) trading volume. In the in-sample analysis, we find that increased OVX, increased Google searches, and increased trading volume predict increased oil price volatility. Additionally, we find bidirectional Granger-causalities between volatility and OVX, volatility and Google searches, and volatility and trading volume. However, results are very different for the out-of-sample forecasts. We incorporate OVX, Google searches for “oil price”, trading volume, and their combinations into commonly used volatility models but find that these variables or their combinations improve none of the models in terms of out-of-sample forecasting.

Hybrid forecasting of crude oil volatility index: The cross‐market effects of stock market jumps

AbstractFrom the cross‐market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data‐driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH‐type models with jumps, and the forecasting effects of five non‐parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross‐market research.

Quantile frequency connectedness between crude oil volatility, geopolitical risk and major agriculture and livestock markets

ABSTRACT This article investigates the quantile frequency risk connectedness between the global geopolitical risk (GPR) index, the CBOE Crude Oil Volatility Index (OVX), and the major agricultural and livestock indexes using a quantile vector autoregression (QVAR)-based frequency connectedness approach. The empirical results suggest that spillover effects are higher in extreme market conditions than in normal market conditions. The GPR has a more significant impact on other markets in the long-term domain of extreme market conditions. Moreover, grain markets are the primary transmitters of spillovers, while OVX predominantly assumes the role of a net receiver of risk spillover effects in most instances. Meanwhile, although the spillover structure between frequencies is homogeneous at the same quantile level, the connection between major agricultural and livestock index markets and two uncertainty indexes is time-varying, and spillovers are heterogeneous at different quantile levels. Our research provides investors and policymakers with new understandings of the development of differentiated crude oil volatility and geopolitical risk aversion strategies under different market conditions, given the heterogeneity of volatility spillover connectedness under different market conditions and frequency domains.

On the effect of short-run and long-run US economic expectations on oil and gold volatilities

This paper investigates the effect of US Economic expectations on oil and gold volatilities, examining their formation across different time horizons. To this end, we compute expectations using a MS-VAR model from one (short run) to sixteen quarters ahead (long run). Then, we estimate the impact of an expectation shock on oil and gold volatility measures through an impulse response function estimating a VAR model from 1987Q1 to 2022Q1. Our results show that oil volatility is significantly affected by expectations in the short run, while gold volatility is affected by expectations in the long run. In particular, gold and oil volatilities exhibit a decrease following a positive expectation shock.

In vitro cholinesterase, lipoxygenase inhibition evaluation of rosemary and coriander essential oil nanoemulsion and characterisation

IntroductionThe low water solubility, high volatility, and instability of essential oils limit the application of various formulations, which can be managed by essential oils nanoemulsions. In this present study, in vitro anti-inflammatory and anticholinesterase activities were evaluated by designing nanoemulsions of rosemary and coriander essential oils with their combinations. MethodsThe chemistry of Rosmarinus officinalis L. (Rosemary, Lamiaceae) and Coriandrum sativum L. (Coriander, Apiaceae) essential oils was confirmed by GC-FID and GC/MS methods. Oil-in-Water (O/W) emulsions of essential oils and their 1:1 (w:w) combination were prepared by water titration. The essential oil/tween 20 ratio of the emulsions were 1:9, 2:8, and 3:7 (w:w) respectively. Characterisation studies were performed by measuring the droplet size, polydispersity index (PDI), zeta potential, and viscosity of the emulsions. Also, resistance to centrifugation, freezing, and heating was determined as part of the stability tests. Anti-inflammatory effects of the essential oils and emulsions were determined by in vitro 5-Lipoxygenase (LOX) inhibition. In addition, in vitro Acetylcholinesterase and Butyrylcholinesterase (AChE and BuChE) effects were evaluated. ResultsThe rosemary (1,8-cineole; 44.1%), coriander (linalool; 74.9%) essential oils, and their nanoemulsion formulations prepared were observed as stable. The anti-inflammatory activity of coriander essential oil was relatively increased by nanoemulsion preparations. However, there was no significant difference in the cholinesterase inhibition effects, when compared. To the best of our knowledge this is the first combination study of bioactivity evaluation of rosemary and coriander nanoemulsion preparations. ConclusionRosemary and coriander essential oil nano emulsifications is a promising application for increasing stability and biological activity as well.

Volatiles of essential oils and yeast derivatives influence mating behaviour of Anastrepha fraterculus males under field cage conditions

AbstractThe sterile insect technique (SIT) is used for the management of tephritid fruit fly pests. The South American fruit fly, Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae), is one potential pest to be targeted by means of SIT. The success of SIT depends, to a large extent, on the sexual performance of sterile males. Various approaches have been evaluated with the aim of improving their sexual performance. These include the exposure to plant‐derived compounds and/or the provision of protein sources in the adults' diet capable of stimulating male mating success. The present study aimed to determine whether exposure to volatiles of Citrus limon (L.) Burm. F. (Rutaceae) essential oil and limonene confers a mating advantage to A. fraterculus laboratory males fed two distinct dietary regimes when competing with wild males for wild females under field cage conditions. Dietary regimes were, one, with non‐hydrolysed brewer's yeast and sugar (1:3 ratio), and the other with brewer's yeast hydrolysate enzymatic and sugar (1:12 ratio). The effect was evaluated in four variables associated with mating success: number of copulas obtained, latency to mate, copula duration, and copula location. Exposure to volatiles did not affect the number of matings achieved, irrespective of the diet given to the males. When laboratory males were fed with brewer's yeast hydrolysate, the effect of volatile exposure was shown in latency to mate, copula duration, and copula location. When the laboratory males were fed with non‐hydrolysed brewer's yeast, the effect of volatile exposure was shown only in copula duration. Laboratory males fed brewer's yeast hydrolysate achieved the same number of matings as wild males, whereas laboratory males fed non‐hydrolysed brewer's yeast had lower performance.

PRICE FLUCTUATIONS AND VOLATILITY OF NATIONAL STRATEGIC FOOD COMMODITIES IN INDONESIA

Meeting the food needs of the Indonesian population is a top priority, and national strategic food plays a vital role in achieving this goal. However, the unstable nature of national strategic food prices can have significant implications, particularly for those with low incomes. This volatility can hinder access to healthy and nutritious food, ultimately impacting the nation's food security. In recent times, food prices have risen sharply, which is expected to have far-reaching consequences. Therefore, it is imperative to analyze the volatility of national strategic food prices to comprehend price fluctuations and patterns. This study analyzed the volatility of national strategic food prices in Indonesia from 2018 to 2022, focusing on commodities such as shallots, garlic, red chilies, cooking oil, and rice. The ARCH-GARCH method used to conduct the analysis, revealing that all five commodity prices were volatile at the national level. The red chili and shallot exhibits a distinctive pattern, characterized by an increase volatility during the second quarter of each year. This can be attributed to the rise in demand during the Ramadan. Conversely, garlic volatility was high in early 2019 and 2020, while remaining relatively stable during other periods. Cooking oil volatility remained stable until the end of 2020, but started to increase in early 2021. This closely related to cooking oil crisis that occurred in Indonesia. Lastly, rice price volatility was high in early 2018. Subsequently, exhibited relatively stable volatility until first quarter of 2022, although there were some periods where the volatility exceeded its average.

Dynamic linkages among bitcoin, equity, gold and oil: An implied volatility perspective

Bitcoin Implied Volatility, derived from the recently launched options on Bitcoin, provides a new forward-looking measure of uncertainty in financial markets. This paper explores its long-run and short-run relationships with the implied volatilities of equity, gold, and oil. The NARDL model reveals a long-run relationship among Bitcoin volatility and others. Kyrtsou-Labys Nonlinear Causality tests established pair-wise causality between the implied volatilities under study. Our findings highlight the integration of Bitcoin markets with global financial markets, indicating the coming of age of the Bitcoin market.

Forecasting the volatility of crude oil basis: Univariate models versus multivariate models

The studies on investigating and forecasting crude oil volatility primarily focus on oil price volatility. This research aims to forecast the volatility of the oil futures basis using generalized auto regressive conditional heteroskedasticity (GARCH) and its extended models. Specifically, this study compares the forecasting performance of nine econometric models, including two univariate and seven multivariate models. The empirical results show that simple univariate models outperform the multivariate models in forecasting basis volatility, as indicated by the model confidence set (MCS). Moreover, from an economic perspective, the empirical results also demonstrate that the univariate models generate higher Sharpe ratios than the multivariate ones, consistent with the statistical evaluation results on forecasting accuracy.

Impact of Crypto Assets as Risk Diversifiers: A VAR-based Analysis of Portfolio Risk Reduction

This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.

The impact of conventional energy markets on connectedness dynamics among eco-friendly assets

ABSTRACT This study examines the connectedness among eco-friendly assets and how volatility in fossil energy markets affects this connectedness. Using a broad coverage for eco-friendly assets such as a clean energy index, a clean cryptocurrency (Cardano), a green bond index, and a sustainability-leaders index, our results show that the connectedness is higher in the extremities (tail-risk spillovers) and more significant during essential world events such as a pandemic or war. Further, we employ nonparametric causality in quantiles, quantile regression, and kernel-based regularised least squares methods to investigate the influence of fossil energy markets (crude oil, natural gas, and gas oil) volatility on eco-friendly assets’ connectedness. Our results imply that fossil energy market volatility has a positive and causal effect on connectedness dynamics among eco-friendly assets. Our study offers important implications for investors and policymakers in crafting portfolio strategies and policy decisions.

NON-ISOTHERMAL MODELING OF SOIL VAPOR EXTRACTION SYSTEM INCLUDING SOIL TEMPERATURE EFFECT

Soil vapor extraction (SVE) is a proven effective in-situ technology for the removal of volatileorganic compounds (VOCs) from the subsurface. SVE process is highly sensitive to temperature.Studying annual soil temperature variation with depth declares that there is a considerable temperaturevariation in the upper few meters that may affect the overall efficiency of SVE process.A numerical model was developed to aid in investigation of field-scale soil vapor extractionprocess. The model is three-dimensional, time dependent that simulates nonisothermal vapor flow andtransport of multicomponent mixtures in soil and keeps track of the distribution of each compound inthe other three immobile phases (NAPL, aqueous, and sorbed). Rate limited interphase mass transferwith linear driving force expressions were used to model volatilization of oil into gas phase. A localequilibrium partitioning was assumed between gas, water, and solid phase. The model equations werediscretized using a standard Galerkin finite element method and solved using set iterative solutionalgorithm.Simulation of hypothetical field-scale problems was done. The physical domain described a threedimensionalsystem with flow to a single extraction well. A hypothetical soil temperature variationwith depth was incorporated with the model. The result of these simulations showed that thistemperature variation has a considerable effect on system efficiency and may play a role in optimumsystem configuration.

Investigating the Impact of Agricultural, Financial, Economic, and Political Factors on Oil Forward Prices and Volatility: A SHAP Analysis

Accurately forecasting crude oil prices is crucial due to its vital role in the industrial economy. In this study, we explored the multifaceted impact of various financial, economic, and political factors on the forecasting of crude oil forward prices and volatility. We used various machine learning models to forecast oil forward prices and volatility based on their superior predictive power. Furthermore, we employed the SHAP framework to analyze individual features to identify their contributions in terms of the prediction. According to our findings, factors contributing to oil forward prices and volatility can be summarized into four key focal outcomes. First, it was confirmed that soybean forward pricing overwhelmingly contributes to oil forward pricing predictions. Second, the SSEC is the second-largest contributor to oil forward pricing predictions, surpassing the contributions of the S&P 500 or oil volatility. Third, the contribution of oil forward prices is the highest in predicting oil volatility. Lastly, the contribution of the DXY significantly influences both oil forward price and volatility predictions, with a particularly notable impact on oil volatility predictions. In summary, through the SHAP framework, we identified that soybean forward prices, the SSEC, oil volatility, and the DXY are the primary contributors to oil forward price predictions, while oil forward prices, the S&P 500, and the DXY are the main contributors to oil volatility predictions. These research findings provide valuable insights into the most-influential factors for predicting oil forward prices and oil volatility, laying the foundation for informed investment decisions and robust risk-management strategies.

The dynamic volatility nexus of geo-political risks, stocks, bond, bitcoin, gold and oil during COVID-19 and Russian-Ukraine war.

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

Crude oil volatility index forecasting: New evidence based on positive and negative jumps from Chinese stock market

This article investigates the crude oil volatility index (OVX) forecasting from the perspective of cross-market asymmetric effects of Chinese stock market jumps. We calculate six kinds of positive and negative jumps based on the high-frequency data of stock returns which are used to represent the asymmetric shocks of stock markets. Principal component analysis (PCA) and momentum of predictability (MoP) strategy are employed separately to synthesize the information of asymmetric jumps. Our empirical results find that considering the positive and negative jumps in Chinese stock market helps to improve the forecasting ability of OVX, especially under the MoP strategy. The out-of-sample model confidence set (MCS) tests and Diebold-Mariano (DM) tests, the evaluation of economic significance and the robustness tests further verify our results.

Volatility and dependence in crude oil and agricultural commodity markets

ABSTRACT This paper studies the evolution, volatility dynamics, and nonlinear dynamics between crude oil prices and major agricultural commodity prices. Semiparametric GARCH-in-Mean copula models are applied to monthly crude oil, corn, soybean, palm oil, rice, sugar, and wheat prices from January 1990 to May 2023. We find that the Clayton copula is the best copula to describe the (bivariate) dependence structures between the crude oil and soybean, crude oil and palm oil, crude oil and sugar, and crude oil and wheat markets, suggesting the statistically significant lower tail dependence between crude oil and major agricultural commodity returns. Moreover, the tail dependence is strongest between crude oil and palm oil.

Pollution Characteristics，Source Analysis，and Activity Analysis of Atmospheric VOCs During Winter and Summer Pollution in Zhengzhou

In order to study the pollution characteristics of volatile organic compounds （VOCs）， continuous monitoring of VOCs in two pollution processes was conducted in June and December 2021 in Zhengzhou. Combined with meteorological conditions， the pollution characteristics， source contributions， and reactivity of VOCs in winter and summer were compared and analyzed. The results showed that the volume fraction of atmospheric VOCs in two episodes were （27.92±12.68）×10-9 and （24.30±5.93）×10-9， respectively. The volume fraction of atmospheric VOCs in the haze pollution process in winter was larger than that in the ozone pollution process in summer. The analysis results of winter sources were as follows： industrial source （27.0%）， motor vehicle source （22.5%）， combustion source （20.1%）， solvent use source （16.3%）， and oil and gas volatilization source （14.1%）. The analysis results of summer sources were as follows： motor vehicle source （24.8%）， industrial source （24.1%）， solvent source （17.4%）， oil and gas volatilization source （14.2%）， combustion source （11.2%）， and plant source （8.4%）. The results of the smog production model showed that the proportion of days in the synergistic control zone of VOCs during the two pollution processes in summer （66.7%） was smaller than that in winter （100.0%）. The secondary reaction activity results showed that the average ·OH loss rate （L·OH） values in winter and summer were 4.12 s-1 and 4.75 s-1， respectively. The average ozone formation potential （OFP） values in summer were 108.36 μg·m-3. The olefins were dominant in the top ten species due to L·OH and OFP contributions in summer. The total SOAFP values in winter in Zhengzhou were 54.38 μg·m-3. Among the top ten species contributing to SOAFP in winter， nine were aromatic hydrocarbons.

Spillover in higher‐order moments across carbon and energy markets: A portfolio view

AbstractMotivated by the occurrence of extreme events and nonnormality of returns, we examine the spillovers among the conditional volatility, skewness and (excess) kurtosis of European Union allowances (EUA), Brent oil, natural gas, coal, electricity and clean energy markets. The jointly estimated spillover index in the system of the three higher‐order moments is notably high, exceeding the spillover index estimated for each individual moment separately. This suggests that spillovers across moments in the carbon‐energy system are important for the sake of completeness of the spillover analysis, and should not be ignored. The performance of the portfolio improves after considering higher‐order moments.

Implied roughness in the term structure of oil market volatility

This paper analyses the attributes and the significance of the roughness of oil market volatility. We employ unspanned stochastic volatility models driven by rough Brownian motions that yield semi-analytical prices for future options entailing efficient calibration applications. By performing a Monte Carlo simulation study, we show that the semi-analytical pricing performs well thus establishing its efficiency for calibration applications. Thus we calibrate option prices written on oil futures and provide empirical evidence of the roughness in oil volatility. Introducing just one additional parameter, the Hurst parameter, indicating the roughness of the volatility improves the calibration by almost a factor of 10. The calibrated option-implied Hurst parameter varies over time, but the entire set of parameters becomes more stable than in the non-rough case corresponding to a fixed Hurst parameter 1/2. These results underscore the importance to model the time dependency of the roughness of oil market volatility.