Volatility Profiles Research Articles

Resistance-Linked Volatile Profiles of Sugarcane Varieties in Sri Lanka and their Potential for Semiochemical-based Management of Chilo sacchariphagus indicus (Lepidoptera: Crambidae)

The sugarcane internode borer (INB), Chilo sacchariphagus indicus (Lepidoptera: Crambidae), poses a major challenge to sugarcane cultivation in Sri Lanka. Characterizing the volatile profiles of sugarcane varieties provides crucial insights for integrated pest management (IPM) strategies, particularly identification of attractant green leaf volatiles (GLVs). These compounds have the potential to be used for the development of Semiochemical based environmentally friendly pest control products, thereby promoting sustainable agriculture in the region. This study analyzed the volatility profiles of mature leaves of five sugarcane varieties (Co 775, SL 83 06, SL 90 6237, SL 92 5588, SL 96 128) that exhibited different resistances to INB attack. Volatiles were collected using dynamic headspace sampling and analyzed using gas chromatography-mass spectrometry (GC-MS). A total of 74 organic compounds were detected, of which 28 were identified as GLV. Seven GLVs showed significant variations, including 1-hexanol, 4-methyl; (S)-3-Ethyl-4-methylpentanol; and 3-octanone. Positive correlations were observed between certain GLVs, while others showed negative correlations, facilitating the classification of cultivars into two groups based on their volatile profiles. This study represents the first record of the volatile profiles of sugarcane in Sri Lanka. The results lay the foundation for further development of Semiochemical based IPM solutions against Chilosacchariphagus indicus and provide an innovative green pest control approach for the sugarcane industry

ДОВГОСТРОКОВА ДИНАМІКА МІЖ BITCOIN ТА ФОНДОВИМИ РИНКАМИ

This paper investigates the long-run relationship between Bitcoin and the S&P 500 index using daily price data from January 2018 to July 2024. While previous studies have focused primarily on correlation analysis and volatility spillovers, we employ both Engle-Granger and Johansen cointegration methodologies to examine the possibility of a long-run equilibrium relationship between these markets. Our results provide strong evidence of cointegration, with the Engle-Granger test indicating significance at the 5% level (p-value = 0.0288) and the Johansen trace statistics confirming one cointegrating relationship. Interestingly, this long-run relationship coexists with a relatively low daily return correlation (0.2884) and substantially different volatility profiles (Bitcoin: 68.56%, S&P 500: 19.87% annualized). These findings suggest that while Bitcoin and the S&P 500 may deviate substantially in the short term, they share common long-run drivers. Our results have important implications for portfolio diversification, statistical arbitrage strategies, and regulatory frameworks, challenging the conventional view of Bitcoin as a purely alternative asset class. The evidence of market integration supports the need for coordinated regulatory approaches and sophisticated risk management strategies in cryptocurrency investments.

Comparative characterization of flavor precursors and volatiles of Taihe black-boned silky fowl and Hy-line Brown yolks using multiomics and GC-O-MS-based volatilomics

Eggs are nutritious and highly valued by consumers. However, egg flavor varies greatly among different hen breeds. The present study used gas chromatography-olfactometry-mass spectrometry-based volatilomics to identify and compare volatile compounds in Taihe black-boned silky fowl (TS) and Hy-line Brown (HL) egg yolks. In addition, the relationships between the levels of different metabolites and lipids and flavor-associated differences were investigated using multiomics. Twenty-eight odorants in total were identified; among them, the levels of 3-methyl-butanal, 1-octen-3-ol, 2-pentylfuran, and (E, E)-2,4-decadienal differed significantly (P < 0.05) between TS and HL egg yolks. The difference in flavor compounds results in TS egg yolks having a stronger overall odor and flavor and a higher acceptance level than HL egg yolks. Metabolomic analysis revealed that 112 metabolites in the egg yolks were significantly different between the two breeds. Furthermore, these different metabolites in the egg yolks of both breeds were significantly enriched in phenylalanine, tyrosine, and tryptophan biosynthesis pathways and phenylalanine metabolism, alanine, aspartate, and glutamate metabolism pathways (P < 0.05), as identified by both metabolite set enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Lipidomic analysis revealed significant differences in the lipid subclasses, lipid molecules, and fatty acid profiles between the egg yolks from the two breeds. As a result, 48 lipid molecules had variable influence in projection values > 1 based on the partial least squares regression model, which may play a role in the differences in aroma characteristics between the two breeds through oxidative degradation of fatty acids. Our study revealed the metabolite, lipid, and volatility profiles of TS and HL egg yolks and may provide an important basis for improving egg flavor to satisfy various consumer preferences.

Hybrid electric vehicle data-driven insights on hot-stabilized exhaust emissions and driving volatility

Despite the fuel use and emission benefits of hybrid electric vehicles (HEVs), few studies have characterized in detail emission patterns and driving volatility profiles from HEVs in different road types under real-world driving conditions. This article examined the relationship between hot-stabilized emissions, engine variables, internal combustion engine status, roadway characteristics, and vehicular jerk types. Data were collected from a Toyota HEV subcompact on a driving route over rural, urban, and highway roads in the Aveiro region (Portugal). Driving volatility was represented by six driving styles based on combinations of acceleration/deceleration and vehicular jerk. Clustering and Disjoint Principal Component Analysis (CDPCA) was applied to extract principal components and identify specific clusters among variables. Both route type and internal combustion engine (ICE) operating behavior showed to have an impact on the distribution of vehicular jerk types. The proposed CDPCA constrained to the road sector exhibited different shapes in the clusters of the jerk types between ICE operation status. This article can provide insights into emissions monitoring analysis of the new generation of HEVs about the description of volatile driving behaviors.

Characterization of volatiles in flowers from four Rosa chinensis cultivars by HS-SPME-GC × GC-QTOFMS.

Rosa chinensis cultivars with volatile aromas are important resources in the perfume industry. The four rose cultivars introduced to Guizhou province are rich in volatile substances. In this study, volatiles from four Rosa chinensis cultivars were extracted using headspace-solid phase microextraction (HS-SPME), and analyzed with two-dimensional gas chromatography quadrupole time of flight mass spectrometry (GC × GC-QTOFMS). A total of 122 volatiles were identified; the main compounds in these samples were benzyl alcohol, phenylethyl alcohol, citronellol, beta-myrcene and limonene. A total of 68, 78, 71, and 56 volatile compounds were identified in Rosa 'Blue River' (RBR), Rosa 'Crimson Glory' (RCG), Rosa 'Pink Panther' (RPP), and Rosa 'Funkuhr' (RF) samples, respectively. The total volatile contents were in the following order: RBR > RCG > RPP > RF. Four cultivars exhibited similar volatility profiles, with alcohols, alkanes, and esters as the major chemical groups, followed by aldehydes, aromatic hydrocarbons, ketones, benzene, and other compounds. Alcohols and aldehydes were quantitatively the two most abundant chemical groups that included the highest number and highest content of compounds. Different cultivars have different aromas, and RCG had high contents of phenyl acetate, rose oxide, trans-rose oxide, phenylethyl alcohol and 1,3,5-trimethoxybenzene, characterized by floral and rose descriptors. RBR contained a high content of phenylethyl alcohol, and RF contained a high content of 3,5-dimethoxytoluene. Hierarchical cluster analysis (HCA) of all volatiles showed that the three cultivars (RCG, RPP, and RF) had similar volatile characteristics and were significantly different from RBR. Differential metabolites among cultivars were screened based on the OPLS-DA model, and there were six main enriched pathways of differential metabolites: biosynthesis of secondary metabolites, monoterpenoid biosynthesis, metabolic pathways, limonene and pinene degradation, sesquiterpenoid and triterpenoid biosynthesis, and alpha-linolenic acid metabolism. The biosynthesis of secondary metabolites is the most differential metabolic pathway.

Exogenous and endogenous price jumps belong to different dynamical classes

Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On average, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to individual volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed.

Volatility Managed Indexes: The Importance of Intraday Data

Modern volatility managed indexes employ antiquated close-to-close volatility models to scale exposure. This has material consequences on the option pricing dynamics for structured investments, many of which reference a volatility managed index because of option pricing benefits associated with the constant volatility. The author tests two daily close-to-close models and three intraday “realized volatility” models that leverage the rich information dynamics found in intraday data. The results, across three highly liquid US large-cap indexes, suggest that the latter three models produce more consistent volatility profiles, or alternatively, a lower volatility of volatility, without sacrificing performance (i.e., the Sharpe ratio). All else being equal, this lower volatility of volatility should be preferred by product issuers and investors because of the resulting option pricing benefits.

Constructing spatiotemporal driving volatility profiles for connected and automated vehicles in existing highway networks

Connected and automated vehicles (CAVs) are expected to change the way we travel. Before both the vehicles and infrastructures are fully automated, users of CAVs are required to respond appropriately to any adverse on-road conditions or malfunction that may prevent the autonomous driving system from reliably sustaining the dynamic driving task performance. The objective of this study is to construct spatiotemporal driving volatility profiles to help CAVs or drivers identify the potential hazards in the existing transportation network and make proactive driving decisions. The volatility profiles are constructed based on the historical traffic dynamics, varying spatially and temporally in the network. For demonstration, this study exploited the Basic Safety Messages datasets from Safety Pilot Model Development program in Ann Arbor, Michigan. The driving volatility is a measure to reflect the variability of driving performance, which is often used to show a vehicle or driver’s performance on road. This study extends the concept to capture the driving dynamics as a performance of the transportation network. This study also matched the driving volatility to the spatial and temporal occurrence of historical traffic crashes. Modeling results showed the volatility is significantly related to safety outcomes; therefore, the driving volatility profiles can be compiled into the high definition (HD) maps to inform CAVs and drivers of potential on-road hazards and assisting in making proactive driving decisions. Further, the results offer implications for potential upgrades of the transportation infrastructure for full automation in the future.

Exogenous and Endogenous Price Jumps Belong to Different Dynamical Classes

Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On aver- age, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to individual volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed.

Volatilization of Standalone Dicamba and Dicamba Plus Glyphosate as Function of Volatility Reducer and Different Surfaces

Dicamba is a herbicide with a moderate volatility profile. Such volatility behavior can be significantly diminished with formulation technology and volatilization reducers. The objective of this study was to quantify the volatility potential of dicamba diglycolamine salt (DGA) in a standalone application or in tank mixture with glyphosate (potassium salt) (GK), with and without volatilization reducer (acetic acid—VaporGrip®) from different surfaces. The combination of these products was applied on four different surfaces (glass slides, corn straw, and dry and moist sandy soil) with three replications, and the experiment was duplicated. The application was performed indoors with an automated sprayer. After application, targets were positioned in cartridges containing two filters in series. Cartridges were placed in a vapor collection system that consisted of a chromatographic oven with constant temperature of 40 °C attached to a vacuum pump for 24 h. After this period, liquid samples were obtained from an extraction procedure of filters and surfaces, which corresponded to the volatilized and deposited portions of the herbicides, respectively. The samples were analyzed by liquid chromatography–tandem mass spectrometry (LC-MS/MS). The use of this method provided a rapid and consistent evaluation, in which the treated surface exerts a direct influence on the amount of volatilized dicamba. The mixture of dicamba and glyphosate solutions exhibited different volatility profiles as a function of the treated surfaces. The DGA applied alone had the largest level of volatility when applied on moist soil and the lowest level of volatility in dry soil and straw. The DGA with GK had volatilities similar in dry soil, wet soil and straw. The volatility reducer in the tank mixture was effective in reducing DGA dicamba volatilization, regardless of the sprayed surface and the tank mixture, making the application of dicamba safer from the volatilization standpoint.

Semi-volatile components of PM2.5 in an urban environment: Volatility profiles and associated oxidative potential

The volatility profiles of PM2.5 semi-volatile compounds and relationships to the oxidative potential of urban airborne particles were investigated in central Los Angeles, CA. Ambient and thermodenuded fine (PM2.5) particles were collected during both warm and cold seasons by employing the Versatile Aerosol Concentration Enrichment System (VACES) combined with a thermodenuder. When operated at 50 °C and 100 °C, the VACES/thermodenuder system removed about 50% and 75% of the PM2.5 vol concentration, respectively. Most of the quantified PM2.5 semi-volatile species including organic carbon (OC), water soluble organic carbon (WSOC), polycyclic aromatic hydrocarbons (PAHs), organic acids, n-alkanes, and levoglucosan, as well as inorganic ions (i.e., nitrate, sulfate, and ammonium) exhibited concentration losses in the ranges of 40–66% and 67–92%, respectively, as the thermodenuder temperature increased to 50 °C and 100 °C. Species in the PM2.5 such as elemental carbon (EC) and inorganic elements (including trace metals) were minimally impacted by the heating process – thus can be considered refractory. On average, nearly half of the PM2.5 oxidative potential (as measured by the dichlorodihydrofluorescein (DCFH) alveolar macrophage in vitro assay) was associated with the semi-volatile species removed by heating the aerosols to only 50 °C, highlighting the importance of this quite volatile compartment to the ambient PM2.5 toxicity. The fraction of PM2.5 oxidative potential lost upon heating the aerosols to 100 °C further increased to around 75–85%. Furthermore, we document statistically significant correlations between the PM2.5 oxidative potential and different semi-volatile organic compounds originating from primary and secondary sources, including OC (Rwarm, and Rcold) (0.86, and 0.74), WSOC (0.60, and 0.98), PAHs (0.88, and 0.76), organic acids (0.76, and 0.88), and n-alkanes (0.67, and 0.83) in warm and cold seasons, respectively, while a strong correlation between oxidative potential and levoglucosan, a tracer of biomass burning, was observed only during the cold season (Rcold = 0.81).

Capturing the power options smile by an additive two-factor model for overlapping futures prices

Abstract In this paper we introduce an additive two-factor model for electricity futures prices based on Normal Inverse Gaussian Levy processes, that fulfills a no-overlapping-arbitrage (NOA) condition. We compute European option prices by Fourier transform methods, introduce a specific calibration procedure that takes into account no-arbitrage constraints and fit the model to power option settlement prices of the European Energy Exchange (EEX). We show that our model is able to reproduce the different levels and shapes of the implied volatility (IV) profiles displayed by options with a variety of delivery periods.

Policy Portfolios and Portfolio Characteristics

In this article, the author provides an alternative to traditional portfolio rebalancing based on changes in asset market values, one informed by equity characteristics. The logic of policy portfolio rebalancing is applied to a framework that uses assets’ 12-month rolling average characteristic values and return volatilities as inputs and rebalances toward those assets that exhibit relatively high characteristic-value-to-volatility ratios. Three important practical constraints are applied to the author’s rebalanced portfolios. The first two constraints relate to asset weights and limit the degree to which a rebalanced portfolio’s individual asset class positions and portfolio-level asset allocation can deviate from a fixed-weight reference policy portfolio. The third constraint is that any rebalanced portfolio is required to have a volatility approximately equal to the representative fixed-weight policy portfolio. The author shows that the longer-term, full-sample performance, both risk and return, of even tightly constrained characteristic-driven policy portfolios is superior to the standard procedure of rebalancing to fixed weights. These results are further validated by measuring the performance of characteristic-driven policy portfolios over rolling sub-samples of the original dataset. In the final section of the article, a meta-optimization technique is introduced that allows investors to select one characteristic-driven policy portfolio among several as the best compromise asset allocation that comes closest to maximally satisfying each of their respective objectives. <b>TOPICS:</b>Portfolio theory, portfolio construction, equity portfolio management <b>Key Findings</b> • Policy portfolios based on rebalancing toward assets that exhibit relatively high characteristic-value-to-volatility ratios are shown to produce better performance relative to standard policy portfolios that rebalance towards fixed policy portfolio weights. • Characteristic-driven policy portfolios exhibit superior performance compared to standard policy portfolios even when the characteristic-driven policy portfolios are highly constrained with respect to their individual asset class positions, portfolio-level asset allocations, and volatility profiles. • By means of a simple meta-optimization technique, it is possible to select one characteristic-driven policy portfolio among several as the one that comes closest to maximally satisfying each of their respective objectives.

Dynamics of Individual Income Rank Volatility: Evidence from West Germany and the US

Abstract This paper presents a methodology for comparing income rank volatility profiles over time and across distributions. While most of the existing measures are affected by changes in marginal distributions, this paper proposes a framework based on individuals’ relative positions in the distribution that is neutral to structural changes that occur in that distribution. Applying this approach to West Germany and the US over three decades, we show that while poorer individuals in both countries are the most volatile in all periods investigated, the long-term trends of volatility at the different points of the distribution in each of these countries differ.

Dynamics of Income Rank Volatility: Evidence from Germany and the US

This paper presents a methodology for comparing income rank volatility profiles over time and across distributions. While most of the existing measures are affected by changes in marginal distributions, this paper proposes a framework that is based on individuals’ relative positions in the distribution, and is neutral in relation to structural changes that occur in the economy. Applying this approach to investigate rank volatility in Germany and the US over three decades, we show that while poorer individuals within both countries are the most volatile, the volatility trend for the middle class in each of these countries differs.

Characterization of Four Diesel Fuel Surrogates by the Advanced Distillation Curve Method.

The development of surrogate fuels with measured and predicted thermophysical properties similar to their authentic refinery stream counterparts is critical for the development of alternative fuels and the optimization of engines to increase efficiency and decrease emissions. In this work, four diesel fuel surrogates, formulated according to a reliable and proven procedure, were characterized by the advanced distillation curve (ADC) method to determine the composition and enthalpy of combustion in various distillate volume fractions. Tracking the composition and enthalpy of distillate fractions provides valuable information for determining structure-property relationships and also provides the basis for the development of equations of state that can describe the thermodynamic properties of these complex or simplified mixtures. This comparison showed that the volatility characteristic of the four surrogates is quite similar not only to the target diesel fuel but also to a number of other prototype alternative diesel fuels. The number of components in the surrogates affected how closely their volatility profiles resembled diesel fuel, as might be expected. The surrogate labeled V0a, consisting of just four components, was the most dissimilar to the target diesel fuel with respect to the initial boiling point and volatility curve shape. This suggests that, although minimizing the number of components greatly eases modeling and formulation efforts, caution should be used to avoid oversimplifying the surrogate mixtures.

Temperature dependence of source specific volatility basis sets for motor vehicle exhaust

Recent work on emissions testing has focused on developing source specific volatility distributions which could be used to improve emissions inventories. One problem about these volatility profiles is that they are evaluated only at one temperature which is usually 298 K. This study uses a simple statistical model to evaluate the temperature dependence of the source-resolved volatility basis set, considering gasoline and diesel vehicle exhaust. The steps involved (a) fitting a distribution to the emissions data (b) evaluating the goodness of fit using a statistical test (c) updating the volatility bins using the Clausius–Clayperon equation; calculating the heats of vaporization of each volatility class using a regression model (d) assessing how the volatility of different VOC classes-Extremely Low Volatile, Low Volatile, Semi-Volatile, Intermediate Volatile and Volatile Organic Compounds – are affected by temperature. The results indicated that there could be significant changes in gas-particle partitioning of these emissions. For diesel exhaust at 298 K, the fractions are 5.4 × 10−4 (ELVOC), 0.074 (LVOC), 0.76 (SVOC), 0.17 (IVOC) and 10−5 (VOC) respectively. Looking at a window of ∓20 K, the partitioning for 278 K is 3 × 10−3 (ELVOC), 0.26 (LVOC), 0.67 (SVOC), 0.07 (IVOC) with no VOC fraction; while at 318 K it is 1.5 × 10−7 (ELVOC), 9 × 10−3 (LVOC), 0.64 (SVOC), 0.35 (IVOC) and 2 × 10−5 (VOC); demonstrating a significant change with temperature. The parameterizations developed in this work could be used to improve motor vehicle emissions inventory models such as MOVES.

Volatility profiles of monoterpenes loaded onto cellulosic-based materials

Monoterpene compounds were loaded onto pure cellulose and two cellulose-based matrices by impregnation method. The effects of initial ratio, structures of volatile compounds and polymers, i.e. cellulose, acetate cellulose and rice husk, on the release profiles were investigated. Four monoterpenes, namely α-pinene, citronellal, carvone and terpinen-4-ol, were tested as volatile compound models. In the case of carvone loaded onto cellulose, we observed that the release of the volatile molecule increases with increasing initial ratio in the formulation. Using different cellulose-based matrix to study the volatility of monoterpene models, the lowest release rates was obtained with rice husk formulations, with highly retention capacity over 50% for carvone, terpinen-4-ol and citronellal after 20 days at 21°C.It was also concluded that the impregnation of terpinen-4-ol and carvone into cellulose and cellulose acetate respectively, could effectively help in prolonging the retention of these volatiles. α-Pinene, a highly hydrophobic molecule showed no significant retention. These results indicate that cellulose-based matrices could be potentially used as good carriers of active compounds for ecological pesticides formulation for postharvest applications.

Effect of Composition and Temperature on Volatilization of Cs from Borosilicate Glasses

Volatility of Cs from borosilicate glasses incorporated with different additives like ZnO, BaO, and CaO have been investigated as a function of heating temperatures (900°C, 950°C, and 1000°C) and time. According to the lower Cs volatility, the sequence of the incorporated oxides is arrived as ZnO &lt; B2O3 &lt; BaO &lt; CaO. It is confirmed that the volatilization of Cs occurred predominantly through diffusion controlled process. From the temperature‐dependence volatility profiles of Cs, the activation energy and frequency factor was calculated and found to be in the range of 300–500 kJ/mol and 103–1012 cm2/s, lowest for ZnO and highest for CaO containing glasses. It was found that the frequency factor and not the activation energy were controlling the Cs loss. The observed variations in the activation energy and frequency factor have been explained by a unique model structure and possible interactions of Cs with different structural units formed due to incorporation of the different additives.

Physicochemical and oxidative characteristics of semi-volatile components of quasi-ultrafine particles in an urban atmosphere

This study examines the physicochemical and redox profiles of atmospheric semi-volatile compounds to evaluate their contribution to the oxidative potential of ambient particulate matter (PM). Concentrated ambient and thermodenuded quasi-ultrafine particles (<180 nm) were collected using the versatile aerosol concentration enrichment system (VACES) at an urban site near downtown Los Angeles. A thermodenuder (TD) was used to selectively remove the semi-volatile components of these aerosols over the temperature range of 50–200 °C. The oxidative potential of PM was measured by means of the DTT (dithiothreitol) assay. Detailed chemical analyses of PM samples, including organic and elemental carbon, water soluble elements, inorganic ions and PAHs (polycyclic aromatic hydrocarbons), were conducted to quantify the volatility profiles of different PM species, and also to investigate their effect on measured oxidative potential. Refractory constituents, such as metals and elemental carbon, were marginally affected by heating, while labile species such as organic carbon and PAHs showed progressive loss in concentration with increase in TD temperature. The DTT-measured oxidative potential of PM was significantly decreased as the aerosols were heated and their semi-volatile components were progressively removed (42 ± 5%, 47 ± 8% and 66 ± 6% decrease at 50, 100 and 200 °C, respectively). Thus, semi-volatile compounds present in quasi-ultrafine urban aerosols constitute a significant fraction of PM oxidative potential, which is associated with the cellular generation of reactive oxygen species. Regression analysis performed between chemical constituents and DTT activity showed that the oxidative potential was strongly correlated with organic carbon and PAHs (R ≥ 0.80; p ≤ 0.05).