High Volatility Research Articles

Predicting Ethereum Price Using Machine Learning Models: A Comparative Analysis

The cryptocurrency market, known for its high volatility and immense data availability, provides an excellent opportunity for predictive modeling. This paper explores the prediction of Ethereum’s price using four distinct models: Random Forest, Logistic Regression, Long Short-Term Memory Networks (LSTM), and CNN-LSTM hybrid models. The study evaluates the performance of these models based on metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Rsquared ( R2 ), and Accuracy (%). The findings highlight that Logistic Regression outperformed the other models with the lowest MSE (6741.12) and highest accuracy (98.66% ) [Table 1]. This research demonstrates the potential of combining traditional and advanced machine learning techniques to achieve robust price prediction in the cryptocurrency domain.

Carbazole-Bismaleimide Based Hyper-Cross-Linked Porous Organic Polymer for Efficient Iodine Capture.

Radioactive iodine, a key waste product of nuclear energy, has been a significant concern among nuclear materials because of its high volatility and its ability to easily enter the human metabolism. Porous materials containing a large number of N-heterocyclic units such as carbazole in the skeletons use as effective adsorbents showing high iodine capture capacities. Herein, a new carbazole-bismaleimide-based hyper-cross-linked porous organic polymer (CzBMI-POP) was successfully prepared from a new tetra-armed carbazole-maleimide monomer (Bis-Cz(BMI)), which contains biscarbazole units and maleimide side groups. To produce CzBMI-POP, a free radical polymerization reaction was carried out via the unsaturated double bonds of Bis-Cz(BMI), enabling the construction of the N-rich porous skeleton in a simple and practical way. A high surface area carbazole-bismaleimide-based POP with polymer backbone having affinity for iodine uptake and sponge-like pore structures ranging from 2 to 20 nm showed iodine uptake capacity up to 215 wt %. The study highlights new opportunities to use POPs as iodine capture platform from nuclear waste, highlighting their potential for environmental remediation due to their easy synthesis and low cost.

Topological Scheme and Analysis of Operation Characteristics for Medium-Voltage DC Wind Turbine Photovoltaic Powered Off-Grid Hydrogen Production System

Renewable energy has high volatility in the traditional off-grid AC hydrogen (H2) production system, which leads to low reliability of the system operation. To address this issue, this paper designs the topology scheme of wind-photovoltaic generation powered off-grid H2 production system. Firstly, a DC off-grid system topology scheme with the wind turbine (WT) and photovoltaic (PV) is connected to the medium voltage DC bus by two-stage conversion is proposed. The power fluctuation of WT and PV generation systems and the power-adjustable characteristics of electrolyzers are taken into consideration. Meanwhile, the scheme of distributed access of energy storage (ES) to the WT side and PV side to provide the voltage support for the system is proposed. Secondly, the operating characteristics of DC microgrids and AC microgrids under abnormal operating conditions, such as the fault of the source side, the fault of the load side, and communication interruption, are analyzed in this paper. Finally, the electromagnetic transient simulation model of the DC off-grid H2 production system and the traditional AC off-grid H2 production system is established. The effectiveness of the proposed topology scheme is verified by simulation of typical operating conditions.

The Role of Sugarcane Trash in Soil Fertility and Plant Growth: A Review

Sugarcane needs a hot and humid climate and can be grown on a variety of soils that can retain moisture. Owing to its long life cycle it heavily depletes soil nutrients during its growth period. Sugarcane, being a heavy feeder requires nearly 208 kg N, 53 kg P2O5, and 280 kg K2O from the soil to produce 100 t ha-1 sugarcane. Sugarcane, being a C4 plant, produces a large quantity of biomass. Approximately 10-15 t of trash, constituting 10-12% weight of cane harvested, is produced by the sugarcane crop. Generally, cane trash contains 68% organic matter, 0.42% N, 0.15% P, 0.57% K, 0.48% Ca, and 0.12% Mg, besides 25.7, 20.45, 236.4, and 16.8 ppm Zn, Fe, Mn and Ca, respectively. Trash conserves soil moisture, C, and N in the soil and provides energy to increase the yield. The trash application conserves the nutrients in the soil, also aids their availability to the plant, and reduces the fertilizer requirements through recycling nutrients in the soil from residue. Organic mulches also create a better physical, chemical, and biological environment of soils and in turn, improve crop productivity. Burning of trash deteriorate the fertility as well as soil health. It also reduces the soil microbial activity and destabilize of soil aggregates. Significant N losses from residues can occur during burning by high temperature volatilization. In the burnt system, >70% of the organic matter and nutrients in the trash are lost to the atmosphere.

Granger predictability of real oil prices by us money and inflation in Markov-switching regimes

Abstract This paper presents new evidence that US money supply growth and inflation rates Granger predict real oil prices in a two-regime Markov switching vector autoregression (MS-VAR) model. An asset pricing theory motivates the empirical work by showing how jumps in real oil prices approximately follow jumps in the discount factor to keep constant the competitive return to oil capital. Using monthly data from January 1978 to June 2024, we consider alternative data combinations of US money supply growth rates, US inflation rates, and real oil prices to establish volatility regimes through goodness of fit testing. We set baseline model as that model with the highest likelihood in explaining the real oil price, which combines M2, the CPI less energy prices (CPIE), and real oil prices. Robustness considers two M2 variants combined with the CPIE that have the next highest likelihoods, for two alternative models. In the high volatility regime, results show robust Granger predictability of real oil prices by the baseline M2 and the M2 variants. In the low volatility regime for the baseline model, the CPIE inflation rate Granger predicts real oil prices. The paper contributes these new MS-VAR results that combined with the theory provide nuanced non-conventional support that monetary factors contribute to heightened real oil price episodes in volatile times as well as in calmer periods.

Is There a High Rice Price Volatility in the Philippines: Insights from Bayesian Seasonal Autoregressive Distributed Lag (BARDL)

This paper analyzes the high volatility in rice prices in the Philippine market from October 1994 to April 2024 using a Bayesian seasonal autoregressive distributed lag (BARDL) model with three lags. Using a dataset of 160 observations, this model controls for lagged prices and seasonal effects that showed significant persistence in rice prices through the first lag, market corrections through the second lag, and minimal impacts from seasonality. Model diagnostics, including WAIC and LOOIC, indicate the model's goodness but show considerable unseen variability σ, and external factors seem to be prevailing. The findings thereby emphasize the failure of using market mechanisms to stabilize prices and the crucial need for strategic government interferences. It discusses market failure effects, the pendulum approach to economic policy, and welfare economics, urging for a balanced approach that combines market intervention with more supportive policies toward farmers to stabilize the rice market.

Carbon-contract-for-difference (CCfD) and green transition of coal power under dual price uncertainties in the Korean market—a real options approach

ABSTRACT Carbon pricing under the Korean Emission Trading System (K-ETS) is the most widely accepted policy for internalizing the externalities caused by greenhouse gas emissions to firms’ production decisions. However, the effect of K-ETS has been questioned because of two problems, namely low level and high volatility of carbon prices and time-inconsistent preferences of governments, which causes firms to hold their investment decisions. Carbon contract for difference (CCfD) can be an effective solution to these problems by serving as a credible commitment vehicle. This study analyzes the optimum investment trigger prices of carbon and subsidy for CACPs based on the Geometric Brownian Motion (GBM). As coal power generates about 80% of the power sector’s carbon emissions in Korea, CACPs are rising as an important alternative to reduce carbon emissions and mitigate the social cost of phasing out coal plants with remaining operational life expectancy. The uncertainty of both carbon prices and subsidy prices is considered. The results indicate that the investment trigger price under the current ETS is 95.0 USD/ton-CO2 and that under CCfD is 52.5 USD/ton-CO2. This paper shows that CCfD can reduce the burden that customers must eventually bear to meet Korea’s Nationally Determined Contribution (NDC) goal.

Release Profile and Antibacterial Activity of Thymus sibthorpii Essential Oil-Incorporated, Optimally Stabilized Type I Collagen Hydrogels.

Antimicrobial resistance is one of the drastically increasing major global health threats due to the misuse and overuse of antibiotics as traditional antimicrobial agents, which render urgent the need for alternative and safer antimicrobial agents, such as essential oils (EOs). Although the strong antimicrobial activity of various EOs has already been studied and revealed, their characteristic high sensitivity and volatility drives the need towards a more efficient drug administration method via a biomaterial system. Herein, the potential of Thymus sibthorpii EO incorporated in functionalized antibacterial collagen hydrogels was investigated. At first, the optimally stabilized type I collagen hydrogels via six different multi-arm poly (ethylene glycol) succinimidyl glutarate (starPEG) crosslinkers were determined by assessing the free amine content and the resistance to enzymatic degradation. Subsequently, 0.5, 1, and 2% v/v of EO were incorporated into optimized collagen hydrogels, and the release profile, as well as release kinetics, were studied. Finally, biomaterial cytocompatibility tests were performed. Thymus sibthorpii EO was released from the hydrogel matrix via Fickian diffusion and showed sustained release and 0.5% v/v EO-loaded hydrogels showed adequate antibacterial activity against Staphylococcus aureus and did not show any statistically significant difference compared to penicillin (p < 0.05). Moreover, none of the fabricated composite antibacterial scaffolds displayed any cytotoxicity on NIH-3T3 fibroblasts. In conclusion, this work presents an innovative antibacterial biomaterial system for tissue engineering applications, which could serve as a promising alternative to antibiotics, contributing to coping with the issue of antimicrobial resistance.

Investment Strategies in Turbulent Times: A Comparative Analysis of Developed and Emerging Markets

ABSTRACT This study examines portfolio diversification between emerging (China, Malaysia, the Philippines, and Thailand) and developed markets (the USA, UK, Singapore, Hong Kong, and Australia) from January 2006 to August 2022 using MGARCH-DCC analysis. Malaysia’s stock index shows low volatility but a declining trend. The USA index offers the lowest volatility and correlation, making it ideal for diversification with Asian markets. The Philippine index also provides diversification benefits. However, China’s and Thailand’s indices pose risks due to high volatility. Correlations spiked during the 2007 subprime crisis but later stabilized. The findings guide investors on diversification strategies and highlight caution with volatile markets.

The Role of Ionic Liquids in Textile Processes: A Comprehensive Review.

Thanks to their unique physicochemical properties, ionic liquids (ILs) have moved from niche academic interest to critical components in various industrial applications. The textile industry, facing significant environmental and economic pressures, has begun to explore ILs as sustainable alternatives to traditional solvents and chemicals. This review summarizes research on the use of ILs in various textile processes, including dyeing, finishing, and fiber recycling, where their high thermal stability, tunable solubility, and low volatility are exploited to reduce resource consumption and environmental impact. The discussion also extends to the integration of ILs in textile waste recycling, highlighting innovative approaches to fiber dissolution and regeneration aimed at circular economy goals. Despite these advances, challenges such as high production costs and scalability remain barriers to the widespread adoption of ILs in the textile sector. Addressing these barriers through continued research and development is essential to fully realize the potential of ILs for sustainable transformation in textiles.

Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection

Abstract A posterior probability and intra-cluster feature weight online dynamic feature selection algorithm is proposed to address the issues of high dimensionality and high volatility of data in the basic oxygen furnace (BOF) steelmaking production process. First, a genetic algorithm with fixed feature space dimensions is introduced, which narrows the solution space by predefining the number of selected features, thereby enhancing the stability of feature selection. Second, the posterior probability of samples and intra-cluster feature weights are used to weigh and calculate the feature importance of the current sample, obtaining the optimal features that align with the current operating conditions. Finally, the dynamically selected features are used in a regression model to predict the carbon content and temperature of the BOF steelmaking process data. Simulations of actual BOF steelmaking process data showed that the prediction accuracy was 86% within a carbon content error range of 0.02, and 88% within a temperature error range of 10°C.

Analysis of Problems and Countermeasures in China's Stock Market

As an important part of the global financial system, China's stock market has unique characteristics and challenges, such as high market volatility, information asymmetry, policy risks, and market mechanism defects. This paper aims to analyze these major problems facing the Chinese stock market and propose practical solutions. By studying the challenges of market volatility, information asymmetry, policy risks, insufficient investor education, and imperfect legal framework, this paper proposes countermeasures to improve the stability and efficiency of China's stock market. These countermeasures include improving policy transparency, promoting institutional investor participation, strengthening investor education, improving information disclosure mechanisms, and strengthening market supervision. The results show that by implementing these measures, market volatility can be effectively reduced, information transparency can be improved, market efficiency can be enhanced, and support can be provided for the sustainable development of China's stock market. This study provides valuable insights for policymakers, investors, and scholars to promote the healthy and stable development of China's stock market.

Comparison of the asymmetric multifractal behavior of green and U.S. bonds against benchmark financial assets

Presently, financial portfolio managers lack a solid basis for building a reliable risk management strategy for green debt instrument investments due to the lack of compelling growth and resilience data. Therefore, this study assesses the role of green bonds in financial markets by assessing and correlating their complex scaling behaviors across multiple periods with those of key benchmark assets (e.g., conventional bonds, high-yield bonds, Euro-Dollar exchange, Dow Jones Industrial Index, Bitcoin, and Gold). Specifically, we explore linear and nonlinear correlation patterns using cross-correlation tests and the dynamic conditional correlation model, focusing on bond interactions under various degrees of freedom. Our analysis reveals that although most assets exhibit nonlinear correlations, Bitcoin uniquely aligns linearly with U.S. bonds under certain conditions. Green bonds, however, display nonlinear correlations with Bitcoin and stand out for their distinct upward financial persistence. We find also that green bonds are primary drivers in the financial domain, highlighted by their pronounced interactions and the consistent cross-correlation with the Euro-Dollar exchange rate. Moreover, green bonds have the lowest multifractality, showing persistent upward trends and antipersistent downward trends, rendering them quite resilient during periods of high volatility. These results imply that green bonds may be advantageous to portfolio risk management strategies, especially during crises when diversification and hedging tactics are needed.

Modern methods of strategic management in the oil and gas industry

The necessity to develop new methods of strategic management in the oil and gas industry is due to many factors, among which one can note the increase in the cost of material and technical components for equipment, high volatility of prices for oil and gas industry resources, lack of stability of financial markets, increased competition for the development of new fields, which requires the search for a new vector of successful development of oil and gas companies engaged in production, transportation and sale of resources.The article provides a comparative analysis of strategic management methods in the oil and gas industry and identifies the main trends in strategic management in accordance with new processes in the global and national economy.

A hybrid stock prediction method based on periodic/non-periodic features analyses

Stock investment is an economic activity characterized by high risks and high returns. Therefore, the prediction of stock prices or fluctuations is of great importance to investors. Stock price prediction is a challenging task due to the nonlinearity and high volatility of stock time series. Existing deep learning models may not capture the periodic and non-periodic features of stock data effectively. In this paper, we propose a novel model that leverages Complete Ensemble Empirical Mode Decomposition (CEEMD), Time2Vec, and Transformer to better capture and utilize various patterns in stock data for enhanced prediction performance, and we call it ETT. CEEMD decomposes the stock data into different frequency components based on their intrinsic scales. Time2Vec provides a time vector representation that captures both periodic and non-periodic patterns while being invariant to time scaling. Transformer learns the long-term dependencies and global information from the data. We apply ETT to predict stock prices in the Chinese A-share market and compare it with several baseline models. The results show that ETT reduces the mean squared error (MSE) by an average of 4% and increases the average cumulative return by 58% on the CSI 100 and Hushen 300 datasets.

Triazenide based metal precursors for vapour deposition.

Molecules featuring a metal centre in a positive valence surrounded by 1,3-dialkyltrianzenide ligands, Mx+[R-NN-N-R']x, were shown to have both high thermal stability and volatility, making them interesting as precursors in chemical vapour deposition (CVD) and atomic layer deposition (ALD). So far, metals from groups 11-14 and lanthanoids form stable triazenides and the In and Ga triazenides have proven to be excellent precursors for InN, In2O3, GaN and InGaN. We believe the exploration of the triazenides as CVD and ALD precursors has only begun and hope to inspire further research with this perspective.

Design and performance evaluation of low-volatility and low-viscosity absorbents for CO2 capture

This study synthesized a novel homogeneous non-aqueous absorbent and analyzed hydrogen bonds and viscosities, which were rarely studied before.

Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent.

Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set of assumptions that are not supported by data in high volatility markets such as the technological sector or cryptocurrencies. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management (PM) is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator, also called gymnasium. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, capable of representing this distribution over time, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, to assess whether they follow a reasonable behaviour, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, driven by the motivation of making DRL explainable, we developed a novel Explainable DRL (XDRL) approach for PM, integrating the Proximal Policy Optimization (PPO) DRL algorithm with the model agnostic explainable machine learning techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent's suggestions. We empirically illustrate it by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time. We propose the first explainable post hoc PM financial policy of a DRL agent.

Physicochemical and functional properties of cinnamon essential oil emulsions stabilized by galactomannan-rich aqueous extract from Gleditsia sinensis seeds and soy protein isolate.

Cinnamon essential oil has gained widespread attention in the food industry as a safe and effective preservative. However, its low water solubility and high volatility limit its application in food, making the use of natural emulsifiers for its emulsification an increasingly popular focus of research. This study focuses on the extraction of galactomannan-rich aqueous extracts from Gleditsia sinensis seeds using a low-energy, low-pollution microwave-assisted method. The extracted aqueous extracts from Gleditsia sinensis seeds was combined with soy protein isolate to prepare a cinnamon essential oil emulsion, followed by physicochemical characterization and stabilization mechanism studies. The emulsions demonstrated excellent storage stability at 4 °C, along with robust ionic, pH, temperature, and freeze-thaw stability. Furthermore, the emulsions exhibited significant antioxidant activity and effectively inhibited the growth of Staphylococcus aureus and Listeria monocytogenes, highlighting their potential for application in food preservation. Preservation trials with orange juice confirmed that our emulsion achieved preservation comparable to that of the commercial food preservative potassium sorbate. These findings provide valuable insights for developing stable and functional natural food emulsifiers.

Substantiation of directions for improving the management mechanism of the construction complex in the Donetsk People’s Republic

The relevance of the study of management mechanisms of the construction complex is due to its fundamental role in the economic development and structural transformations of the national economy. The construction complex acts not only as a producer of material goods, but also as a catalyst for economic growth, creating a multiplier effect in related industries and stimulating innovative activity. In a dynamically changing market environment characterised by high volatility of supply and demand as well as by an accelerated pace of technological innovation, improving management approaches in the construction industry becomes critically important. In addition, modern imperatives of sustainable development, including environmental, social and economic aspects, require a revision of traditional management models and introduction of innovative strategies. This article is aimed at a comprehensive analysis and scientific justification of the directions of optimisation of the management mechanism of the construction complex. The aim of the research is to develop an integrated management approach that will increase competitiveness of the industry at the national and international levels, ensure long-term economic efficiency and sustainability in the face of global challenges. Special attention is paid to the analysis of innovative management practices, digitalisation of management processes, and adaptation of international experience to the Russian realities.