Volatile Reduction Research Articles

Testing the waters meetings, retail trading, and capital market frictions

AbstractPre-IPO firms may “test the waters” by meeting privately with investors in order to allow access to management and more time to make an investment decision. However, these meetings have the potential to undermine the SEC’s objectives of protecting investors and supporting market efficiency by allowing institutional investors, but not retail investors, private access to management. We find lower retail trading after IPOs of firms that held testing-the-waters meetings, consistent with the meetings reducing retail investor participation. Moreover, retail investors that still participate in the market in the presence of testing-the-waters meetings have inferior investment outcomes. Nonetheless, we find no evidence of lower overall market liquidity or slower price discovery following testing-the-waters meetings. In fact, we observe a reduction in stock return volatility. Overall our evidence suggests that, while testing-the-waters meetings may harm retail investors, there does not appear to be a negative impact on overall market function.

AI-ENHANCED PORTFOLIO MANAGEMENT: CRAFTING OPTIMAL INVESTMENT STRATEGIES FOR DIVERSE FINANCIAL MARKETS

This study investigates the transformative potential of artificial intelligence (AI) in portfolio management across diverse financial markets. It aims to develop a comprehensive framework that enhances investment strategies in developed, emerging, and frontier markets through AI techniques. The research will employ a mixed-methods approach, combining quantitative and qualitative methodologies. Initially, extensive secondary data will be collected from market reports, financial databases, and previous studies. This data will encompass market-specific characteristics, AI-based portfolio strategies, and financial indicators. AI methods, including neural networks, machine learning models, and natural language processing applications, will then be utilized to analyze portfolio performance across various market scenarios. The anticipated outcome of this study is a versatile AI-driven framework that accommodates the unique aspects of different financial markets. By optimizing asset allocation, risk management, and predictive analytics, this framework will provide portfolio managers with evidence-based recommendations to enhance the stability and performance of their investments. The study aims to demonstrate how AI can mitigate risks associated with market volatility, liquidity issues, and regulatory constraints, leading to a more resilient and data-driven approach to portfolio management on a global scale KEYWORDS: AI integration in finance, Portfolio performance metrics, Compounded Annual Growth Rate (CAGR), Volatility reduction

Assessing the value and risk of renewable PPAs

Renewable Energy Power Purchase Agreements (RE PPAs) are considered to be a key tool in order to foster RE deployment, as they allow for the reduction of uncertainty for all parties as well as facilitating access to the long term finance required for such projects. Nevertheless, RE PPA adoption is hampered by a number of barriers, including the high level of guarantees demanded from offtakers, a problem which is related to the shortcomings of existing assessment methodologies, in particular, determining the credit risk of the PPA itself. In this work, we propose an RE PPA assessment model focused on the main drivers of value and risk for the offtaker which are cost and volatility reductions, compared to the electricity market. By identifying and valuing the options for the offtaker embedded in the PPA, it is possible to determine the default probability at any given time and the expected loss for the producer, thus allowing for the estimation of the amount of guarantees needed to hedge the credit risk.

New Dynamics in the World Rice Market

In the last four decades of the 20th century, the world rice market was highly unstable, with price volatility greater than that on world wheat and maize markets. In the 21st century, however, new dynamics (a thicker world rice market, more irrigation, milder export restrictions) have contributed to a reduction in price volatility that is now lower than that on world wheat and maize markets. In addition, the El Niño events that are responsible for many shocks to the world rice market are largely predictable several months in advance. The reduced volatility on the world rice market and the predictability of El Niño events make it easier to use international trade as an instrument for food security. The recent experience with rice trade liberalization by Asian rice importers has been largely positive, helping to improve food security and the affordability of healthy diets, with little impact on domestic price volatility.

Biocompatible Nano-Cocrystal Engineering for Targeted Herbicide Delivery: Enhancing Efficacy through Stimuli-Responsive Release and Reduced Environmental Losses.

In addressing the critical challenges posed by the misuse and inefficiency of traditional pesticides, we introduce a Nano-Cocrystal material composed of the herbicide clopyralid and coformer phenazine. Developed through synergistic supramolecular self-assembly and mechanochemical nanotechnology, this Nano-Cocrystal significantly enhances pesticide performance. It exhibits a marked improvement in stability, with reductions in hygroscopicity and volatility by approximately 38%. Moreover, it intelligently modulates release according to environmental factors, such as temperature, pH, and soil inorganic salts, demonstrating decreased solubility by up to four times and improved wettability and adhesion on leaf surfaces. Importantly, the herbicidal activity surpasses that of pure clopyralid, increasing suppression rates of Medicago sativa L. and Oxalis corniculata L. by up to 27% at the highest dosage. This Nano-Cocrystal also shows enhanced crop safety and reduced genotoxicity compared to conventional formulations. Offering a blend of simplicity, cost-effectiveness, and robust stability, our findings contribute a sustainable solution to agricultural practices, favoring the safety of nontarget organisms.

Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization

Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects.

Tri-Phase Implementation of an Innovative Fuzzy Logic Approach for Decision-Making

This paper proposes a novel approach to decision-making based on a three-phase application of a new fuzzy logic model that embraces the principles of symmetry by balancing competing objectives in data collection and analysis. Our study, which employs a three-stage stratified random sample strategy with a randomized response technique, addresses the critical challenges of cost management and volatility reduction. Using the alpha-cut method, our model creates an effective allocation strategy that finds a balance between cost constraints and variance reduction objectives. We use numerical examples from real-world scenarios to demonstrate our approach’s durability and practicality. Our revolutionary technique maintains data quality and cost-effectiveness while offering a game-changing answer to sensitive information acquisition concerns. By combining randomized response techniques and fuzzy logic, this study establishes a new standard for decision-making models that prioritizes both data-gathering precision and privacy preservation, encapsulating the essential principle of symmetry in balancing competing aims.

Green manure combined with reduced nitrogen reduce NH3 emissions, improves yield and nitrogen use efficiencies of rice

Background Green manure is an important source of organic fertilizer. Exploring green fertilizer and nitrogen fertilizer reduction is important for agricultural production. However, few studies have been conducted, especially on the effects of different green fertilizers along with reduced nitrogen fertilizer application on soil ammonia volatilization emissions, rice yield, and nitrogen fertilizer uptake and utilization. Methods In this study, the effects of different types of green manure and reduced nitrogen fertilizer application on soil ammonia volatilization emissions, aboveground population characteristics of rice, and nitrogen fertilizer uptake and utilization were explored. This study was based on a field-positioning experiment conducted between 2020 and 2022. Six treatments were established: no nitrogen fertilizer application (CK), conventional fertilization in wheat-rice (WR), villous villosa-rice (VvR), vetch sativa-rice (VsR), rapeseed seed-rice (RR), and milk vetch-rice (GR), with a 20% reduction in nitrogen fertilizer application. The amounts of phosphorus and potassium fertilizers remained unchanged. The characteristics of ammonia volatilization loss in rice fields, agronomic traits of rice, yield traits, and nitrogen uptake and utilization were investigated. Results The results indicated a significant difference (P < 0.05) in the impact of different treatments on ammonia volatilization emissions from rice in the two-year experiment. Compared with WR treatment, VvR, VsR, RR, and GR treatments reduced the total ammonia volatilization loss by 23.58 to 39.21 kg ha−1, respectively. Compared with the conventional WR treatment, other treatments increased rice yield by 0.09 to 0.83 t ha−1. GR treatment was significantly higher than other green fertilizer treatments, except for VsR (P < 0.05). It increased the nitrogen uptake of rice by an average of 4.24%–22.24% and 13.08%–33.21% over the two years, respectively. The impact of different types of green manure on the nitrogen uptake and utilization of rice varied greatly, indicating that the combination of green manure and fertilizer is a sustainable fertilization model for crops to achieve high yields. In particular, the Chinese milk vetch as green manure was more beneficial for ammonia volatilization reduction in paddy field and stable grain production of rice.

An empirical comparison of stabilization funds and futures hedging for oil exporting developing countries

AbstractOil price volatility can significantly affect the growth and development of oil exporting developing countries. Given the widespread use of stabilization funds as a risk management tool by such countries and the potential of futures hedging to function in a similar vein, no study has effectively compared both mechanisms. This article hence uses the hedging effectiveness measure to examine the potential of each mechanism to smooth the volatile revenues of oil exporting developing countries. The results show that stabilization funds performed best with larger deposit rates and using benchmark prices with larger moving averages. However, the volatility reduction capabilities of optimal futures hedging were found to be far superior and should thus be adopted by oil dependent developing countries as their prime revenue smoothening mechanism.

The role of stable coins in mitigating volatility in cryptocurrency markets

This study aims to analyze the link between Perceived Volatility Reduction (PVR), Risk Perception, Stablecoin Usage Frequency, Market Confidence, and Stablecoin Adoption (SA). The primary goal is to determine if and to what degree these variables impact stablecoin adoption. We created a questionnaire to gather information from 198 Malaysians. To analyze the research model and test the hypotheses, the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method was utilized. According to the findings, there is a strong and positive association between Perceived Volatility Reduction (PVR) and Stablecoin Adoption (SA). Market players are more likely to adopt stablecoins if they perceive them as useful instruments for mitigating the severe price volatility inherent in traditional cryptocurrencies. This finding emphasizes the importance of risk perception and market stability in driving market behavior. Trust in stablecoin systems, transparency, and regulatory, compliance influenced PVR and SA. The study's findings underscore the significance of perceived volatility reduction (PVR) in driving stablecoin adoption (SA), highlighting the importance of risk perception and market stability. Trust in stablecoin systems, transparency, and regulatory compliance emerge as crucial factors influencing PVR and SA. These insights offer valuable guidance for investors navigating the cryptocurrency market, governments managing stablecoin supply, and scholars studying trust dynamics in the cryptocurrency ecosystem.

Amino acids to reduce the escape of organic amines in the CO2 capture process

Carbon neutrality and carbon peaking are important strategic policies for our country. Carbon dioxide capture technology is the bottom of the realization of this strategy, in which amine escape is an important issue for the development of this technology. Herein, the proposal to utilize amino acids for reducing volatilization of organic amines was introduced for the first time. Diethylaminoethanol (DEEA), widely applied and highly volatile, was selected as the primary target for volatility reduction, while hydroxyethyl ethylenediamine (AEEA) and piperazine (PZ) were chosen as the main absorbents for absorption and desorption processes. The effects of DEEA addition on the main absorbent, different types of amino acid additives on volatility, and amino acids on absorption and desorption performance were investigated, respectively. Glutamine (Tyr) demonstrated significant potential in reducing the volatilization of organic amines; when 2% Tyr was added during gas–liquid equilibrium experiments, it resulted in a 74.5% decrease in DEEA volatilization within the absorbent system. Simulated gas–liquid entrainment escape showed that CO2 loading reduced volatilization by 42.31%, with a further reduction of 53.93% achieved when loaded with 0.5 molCO2/mol amine; this loading also had a promotional effect on absorption and desorption processes. Moreover, Tyr exhibited excellent stability without generating any degradation products, thereby prolonging absorber lifespan and minimizing economic cost losses.

Ionic Liquid-Glycol Mixtures for Direct Air Capture of CO2: Decreased Viscosity and Mitigation of Evaporation Via Encapsulation.

Herein we address the efficiency of the CO2 sorption of ionic liquids (IL) with hydrogen bond donors (e.g., glycols) added as viscosity modifiers and the impact of encapsulating them to limit sorbent evaporation under conditions for the direct air capture of CO2. Ethylene glycol, propylene glycol, 1,3-propanediol, and diethylene glycol were added to three different ILs: 1-ethyl-3-methylimidazolium 2-cyanopyrrolide ([EMIM][2-CNpyr]), 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]), and 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]). Incorporation of the glycols decreased viscosity by an average of 51% compared to bulk IL. After encapsulation of the liquid mixtures using a soft template approach, thermogravimetric analysis revealed average reductions in volatility of 36 and 40% compared to the unencapsulated liquid mixtures, based on 1 h isothermal experiments at 25 and 55 °C, respectively. The encapsulated mixtures of [EMIM][2-CNpyr]/1,3-propanediol and [EMIM][2-CNpyr]/diethylene glycol exhibited the lowest volatility (0.0019 and 0.0002 mmol/h at 25 °C, respectively) and were further evaluated as CO2 absorption/desorption materials. Based on the capacity determined from breakthrough measurements, [EMIM][2-CNpyr]/1,3-propanediol had a lower transport limited absorption rate for CO2 sorption compared to [EMIM][2-CNpyr]/diethylene glycol with 0.08 and 0.03 mol CO2/kg sorbent, respectively; however, [EMIM][2-CNpyr]/diethylene glycol capsules exhibited higher absorptions capacity at ∼500 ppm of CO2 (0.66 compared to 0.47 mol of CO2/kg sorbent for [EMIM][2-CNpyr]/1,3-propanediol). These results show that glycols can be used to not only reduce IL viscosity while increasing physisorption sites for CO2 sorption, but also that encapsulation can be utilized to mitigate evaporation of volatile viscosity modifiers.

R&D investments and idiosyncratic volatility: evidence from a quasi-natural experiment

ABSTRACT An intriguing phenomenon in the U.S. stock market is that the level of average stock return volatility has increased considerably since the 1960s. We argue that corporate R&D investments provide an explanation for this phenomenon due to the uncertainties of R&D investment payouts and the information asymmetry between firms and investors. We employ a quasi-natural experiment, namely, the changes in state-level R&D tax credits in the U.S. to investigate the effect of R&D investments on idiosyncratic volatility. The results of baseline regressions show that a one-standard-deviation increase in R&D tax credits (user cost of R&D capital) is associated with a 0.013 (0.049) standard-deviation increase (reduction) in idiosyncratic volatility. This finding provides an explanation for the increasing trend in the average stock return volatility over the recent decades.

The influence of well-dispersed mesoporous hydroxystannate ferrate anchored magnesium hydroxide hybrids on enhancing interfacial compatibility and fire safety of EVA/EPDM composites

In this work, an innovative methodology for enhancing the fire safety of EVA/EPDM (EE) composites by employing an in-situ encapsulation technique to integrate magnesium hydroxide (MH) within the mesoporous hydroxystannate ferrate (mHSF) framework via ion-driven self-assembly. Comprehensive characterization involving X-ray diffraction, scanning electron microscopy, and transmission electron microscopy was employed to investigate the structure, elemental composition, and morphology of the resulting hybrids. In comparison to EE/MH composites, the MH@mHSF hybrids exhibited favorable dispersion within the EE matrix, demonstrating minimal aggregation. Upon incorporation of MH@mHSF hybrids, a significant reduction in peak heat release rate and total heat release (73.5 and 50%) compared with pristine EE, and an achievement in limiting oxygen index value of 32.5% from 19% for pristine EE was achieved. Notably, when contrasted with the findings of EE/MH/mHSF, the EE/MH@mHSF composite showcased superior tensile strength and enhanced fire safety properties. This improvement can be ascribed to the catalytic charring behavior and adsorption effects facilitated by well-dispersed MH@mHSF hybrids within the polymer matrix. Thermogravimetric analysis/infrared spectrometry (TG-IR) corroborated a substantial reduction in organic volatiles and the suppression of carbon monoxide emissions upon integration of MH@mHSF hybrids, underscoring the alleviation of fire hazards. The observed synergy of the catalytic and adsorption effects of mesoporous HSF, combined with the flame-retardant characteristics of MH, plays a pivotal role in the suppression of smoke generation.

Contingent Claims and Hedging of Credit Risk with Equity Options

Abstract Using contingent-claims valuation, we introduce novel hedge ratios for credit exposures using put options. Option hedge ratios are generally in line with the empirical sensitivities of credit spread changes to put option returns and, relative to stock hedge ratios, produce further reductions in volatility for a portfolio of North American firms. We show that option hedge ratios capture option-specific credit exposure related to the VIX index and the default spread, which is unaccounted for by Merton’s (1974) equity hedge ratios alone. Combining stocks and put options for credit risk hedging can be done effectively using the volatility smirk. (JEL E43, E44, G10)

Pyrolysis temperature and time of rice husk biochar potentially control ammonia emissions and Chinese cabbage yield from urea-fertilized soils

Current agricultural practices are increasingly favoring the biochar application to sequester carbon, enhance crop growth, and mitigate various environmental pollutants resulting from nitrogen (N) loss. However, since biochar’s characteristics can vary depending on pyrolysis conditions, it is essential to determine the optimal standard, as they can have different effects on soil health. In this study, we categorized rice husk biochars basis on their pH levels and investigated the role of each rice husk biochar in reducing ammonia (NH3) emissions and promoting the growth of Chinese cabbage in urea-fertilized fields. The findings of this study revealed that the variation in pyrolysis conditions of rice husk biochars and N rates affected both the NH3 emissions and crop growth. The neutral (pH 7.10) biochar exhibited effective NH3 volatilization reduction, attributed to its high surface area (6.49 m2 g−1), outperforming the acidic (pH 6.10) and basic (pH 11.01) biochars, particularly under high N rates (640 kg N ha−1). Chinese cabbage yield was highest, reaching 4.00 kg plant−1, with the basic biochar application with high N rates. Therefore, the neutral rice husk biochar effectively mitigate the NH3 emissions from urea-treated fields, while the agronomic performance of Chinese cabbage enhanced in all biochar amendments.

Characterization of the key aroma compounds of wild bitter melon lujiu based on LLE coupled with GC–O–MS and the aroma influence of polysaccharide addition using VASE–GC–MS

Wild bitter melon (Momordica charantia L. Var. Abbreviata Ser; WBM) is widely cultivated in East and Southeast Asia. It is made into lujiu with high-quality strong-flavor baijiu and presents a beautiful golden color and harmonious aroma. Lujiu is a type of alcoholic beverage similar to gin. In this study, the aroma profile of wild bitter melon lujiu (WBML) was determined through sensory evaluation, and important aroma-active compounds were identified through liquid–liquid extraction (LLE) coupled with gas chromatography–olfactometry–mass spectrometry (GC–O–MS) and aroma extraction dilution analysis (AEDA). A total of 42 aroma-active compounds were identified, and 28 of them with FD factor ≥ 3 were quantified. Sixteen compounds were pivotal aroma contributors, among which ethyl hexanoate, ethyl pentanoate, ethyl butanoate, ethyl octanoate, and acetic acid had the highest OAVs. Moreover, WBM-sourced aroma compounds were found in WBML, including dihydroactinidiolide, (E,E)-2,4-nonadienal, β-ionone, etc. The extraction parameters of vacuum-assisted sorbent extraction (VASE) were optimized for the first time to analyze lujiu aroma. The effect of homologous polysaccharide addition to the aroma of WBML was explored with VASE–GC–MS. The greasy off-odor was decreased due to the reduction of ethyl octanoate volatility by polysaccharide addition.

Herbicide Applications Reduce Gaseous N Losses: A Field Study of Three Consecutive Wheat–Maize Rotation Cycles in the North China Plain

Herbicide residues in farmland soils have attracted a great deal of attention in recent decades. Their accumulation potentially decreases the activity of microbes and related enzymes, as well as disturbs the nitrogen cycle in farmland soils. In previous studies, the influence of natural factors or nitrogen fertilization on the soil nitrogen cycle have frequently been examined, but the role of herbicides has been ignored. This study was conducted to examine the effects of herbicides on NH3 volatilization- and denitrification-related nitrogen loss through three rotation cycles from 2013 to 2016. The four treatments included no urea fertilizer (CK), urea (CN), urea+acetochlor-fenoxaprop-ethyl (AC-FE), and urea+2,4D-dicamba (2,4D-DI) approaches. The results showed that the application of nitrogen fertilizer significantly increased the nitrogen losses from ammonia volatilization and denitrification in the soil. Ammonia volatilization was the main reason for the gaseous loss of urea nitrogen in a wheat–maize rotation system in the North China Plain (NCP), which was significantly higher than the denitrification loss. In the CK treatment, the cumulative nitrogen losses from ammonia volatilization and denitrification during the three crop rotation cycles were 66.64 kg N hm−2 and 8.07 kg N hm−2, respectively. Compared with CK, the nitrogen losses from ammonia volatilization and denitrification under the CN treatment increased 52.62% and 152.88%, respectively. The application of AC-FE and 2,4D-DI significantly reduced the nitrogen gas losses from the ammonia volatilization and denitrification in the soil. Ammonia volatilization reduction mainly occurred during the maize season, and the inhibition rates of AC-FE and 2,4D-DI were 7.72% and 11.80%, respectively, when compared with CN. From the perspective of the entire wheat–maize rotation cycle, the inhibition rates were 5.41% and 7.23% over three years, respectively. Denitrification reduction also mainly occurred in the maize season, with the inhibition rates of AC-FE and 2,4D-DI being 34.12% and 30.94%, respectively, when compared with CN. From the perspective of the entire wheat–maize rotation cycle, the inhibition rates were 28.39% and 28.58% over three years, respectively. Overall, this study demonstrates that herbicides could impact the nitrogen cycle of farmland soil ecosystems via the suppression of ammonia volatilization and denitrification rates, thus reducing gaseous N losses and mitigating global climate change.

Can night trading reduce price volatility? Evidence from China's corn and corn starch futures markets

AbstractSince 2013, China's futures exchanges have implemented night trading for agricultural futures to reduce the overnight risk and price jump of futures products by extending trading hours. This study uses difference‐in‐differences (DID) to examine the impacts of night trading on daytime price volatility in corn and corn starch futures markets. On the basis of tick‐by‐tick data for these futures, we find that night trading has significantly reduced daytime volatility and contributed to price volatility stability in the corresponding futures market. Moreover, we make DID estimations for separate daytime sessions and find that the reduction of the daytime volatility takes place mainly during the first trading session. Robustness and placebo tests further support our main conclusions. Our results provide valuable guidance for futures exchanges and regulators seeking to formulate night trading policies for futures and options.

Does public information facilitate price consensus? Characterizing USDA announcement effects using realized volatility

The provision of public information in commodity markets is justified in part by the idea that public information generates consensus among market participants about the fundamental value of the commodity and reduces price volatility. Significant reductions in options-implied volatility following report releases have been presented as evidence of this market-calming effect. We scrutinize this finding in more detail by comparing implied volatility to realized volatility measures from intraday price data. We show that while implied volatility does indeed fall after report releases, realized volatility does not decrease. We measure realized volatility using intraday data and find evidence of much higher volatility on report days only within minutes of the report release. This pattern is consistent with changes in implied volatility being driven by the resolution of uncertainty about the information contained in the report, rather than changes in volatility expectations that may reflect the consensus among traders about forthcoming price volatility.