Volatility Coefficient Research Articles

Estimation of the Volatility and Apparent Activity Coefficient of Levoglucosan in Wood-Burning Organic Aerosols

Estimation of the Volatility and Apparent Activity Coefficient of Levoglucosan in Wood-Burning Organic Aerosols

Modeling and pricing credit risk with a focus on recovery risk

Consider a defaultable bond traded in a financial market that is subject to shocks and regime shifts. Its recovery payment has a hybrid structure, comprising two components: one contingent on historical information up to the time of default, and the other an independent variable indexed by the regime at the time of default. The default intensity, interest rate, and reference rate are assumed to be general deterministic functions of certain state variables, while these state variables jointly follow a jump-diffusion process, with drift and volatility coefficients governed by the regime and with jumps induced by shocks. We construct a risk-neutral pricing measure that prices all risk sources in an integrated manner. A rigorous verification of this pricing measure reveals the corresponding time-dependent market prices of these risk sources. The resulting pricing framework is applicable to most defaultable bonds and credit derivatives.

Improved ant colony optimization algorithm based on islands type for mobile robot path planning

According to the characteristics of ant colony optimization (ACO) algorithm in mobile robot path planning, such as local optimal solution, slow convergence speed, low search efficiency, and propensity to produce numerous deadlock ants, an improved ACO algorithm based on island type (insular ACO (INACO)) is introduced. In this algorithm, firstly, several islands are established between the starting and the ending position of environment, serving as intermediate nodes for the paths searched by the ants. This greatly reduces the number of deadlock ants. Additionally, rectangular areas are initialized with non-uniform pheromone levels between adjacent islands, while other areas are set to a constant minimum pheromone value. This prevents blind searches during the initial stages of path planning. Furthermore, an adaptive volatilization coefficient is introduced into the global pheromone update rules to balance the convergence and global search ability. Finally, optimal parameter combinations of INACO are determined by simulation. INACO algorithm is simulated in various grid maps and compared with other improved ACO algorithms. Results demonstrate its superior global optimal search capability and rapid convergence speed. The average iterative times is 2.7 in a 20 × 20 grid environment and 4.7 in a 30 × 30 grid environment. Notably, INACO produces very few lost ants, with mean values of 50.1 and 95.2 in 20 × 20 and 30 × 30 grid environments, respectively.

ECG arrhythmia classification based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability

Electrocardiograph (ECG) is one of the most critical physiological signals used for arrhythmia diagnosis. In recent years, ECG arrhythmia classification devices consisting of multi-module sensors, clustering algorithms and neural networks play an important role in monitoring and diagnosing cardiovascular diseases. However, the commonly used ECG arrhythmia classification methods are still facing some problems such as the complex model structure and long running time. To address the above problems, this paper proposes an ECG arrhythmia classification method based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability (SFP-FACC), which uses LSTM to fit the cluster centers and avoids the time consumption of updating the cluster centers during the classification process. The spatiotemporal feature perception ability of this model with the dynamic time warping (DTW) algorithm is improved. The classification is achieved by applying the combination of Euclidean distance and DTW. The convergence speed of the model is improved by using dynamic pheromone volatility coefficient; and finally the optimal solution of the model is determined by using radix sort. Based on the MIT-BIH arrhythmia dataset, the overall accuracy of the proposed classification method in this paper achieves 99.04 %, and even the accuracy of certain types of classification achieves 100 %, and the running time is about 3.5 times faster than that of the basic models. The experiments show that the method proposed in this paper has certain advantages.

Intelligent planning of fire evacuation routes in buildings based on improved adaptive ant colony algorithm

Intelligent planning of fire evacuation routes is an important guarantee for rapid emergency response. Ant colony optimization, as an intelligent bionic algorithm, has notable advantages in route planning. However, traditional ant colony optimization corresponds to a low convergence rate, is easily caught in local optimal solution, and regards route length as the only constraint. To resolve these problems, an improved adaptive ant colony optimization (IAACO) algorithm was proposed in this study. Risk, energy consumption, and route length were taken as key factors to improve the heuristic function, optimize the pheromone update function, and establish multi-objective constraints, The standards for fire evacuation better align with practical requirements. Meanwhile, the adaptive pheromone volatile coefficient was introduced to balance convergence and global searching ability. In addition, the hazard range on the grid map was visualized. The results indicate that under various complex obstacle grid maps, the path inferiority of IAACO is reduced by 61.7% and 58.4%, 43.6% and 36.7%, and 41.6% and 67.7% compared to ACO and IACO, respectively; under the condition of multiple exits, the inferiority is reduced by 63.8% and 54.6%; under the condition of multiple fire sources, the inferiority is reduced by 40.1% and 34.6%; compared with other algorithms, IAACO shows the lowest path inferiority index, 26.6. IAACO is applicable to both the dynamic planning of fire evacuation routes and the evacuation simulation software, Pathfinder, and it performs better than the built-in algorithms of Pathfinder. Facts have proved that the IAACO algorithm significantly improves the safety level of evacuation compared to traditional evacuation methods and other optimization algorithms.

Effects of polyols at low concentration on the release of sweet aroma compounds in model soda beverages

This study investigated the effect of polyols erythritol, d-mannitol, and maltitol on the volatility of aroma compounds γ-butyrolactone, 3-methyl-1-butanol, and 2-phenylethanol in aqueous solution. Headspace solid-phase microextraction/gas chromatography and diffusion-ordered nuclear magnetic resonance techniques were used to obtain information on aroma–food matrix interaction. Results demonstrated that adding polyols at final low concentrations of 5% or 10% (w/w) to an aqueous solution of 2-phenylethanol reduced the release of vapor-phase aromas, except in the case of 3-methyl-1-butanol, which was not affected by the presence of polyols in the liquid matrix. Polyols also reduced the diffusion coefficients of all three aroma compounds, probably due to friction between the molecules. At low polyol concentrations, aroma compound volatility and diffusion coefficient values were altered compared to those of aromas released from pure water. This observation is related to the physicochemical properties of the aroma compounds. These insights may help guide the use of the combination of aroma compounds and polyols in the formulation of sugar-free and reduced-sugar beverages. Chemical compoundsγ-butyrolactone (PubChem CID: 7302), 3-methyl-1-butanol (PubChem CID: 31260), 2-phenylethanol (PubChem CID: 6054), erythritol (PubChem CID: 222285), d-mannitol (PubChem CID: 6251), maltitol (PubChem CID: 493591).

Research on logistics cooperative scheduling method based on multi-intelligent body system in supply chain management

Abstract Collaborative scheduling of logistics is one of the core problems in supply chain management. In this paper, a multi-intelligent system is used to establish the path planning algorithm of intelligent bodies and optimize logistics scheduling in the supply chain by improving the ant colony algorithm. The improved MAPPO algorithm is obtained by introducing the deformation encoder and attention mechanism on the basis of MAPPO to realize information synergy between multiple intelligences. The candidate order table is established to update the local and global pheromones, and the ACO algorithm is improved with the help of adaptive adjustment of volatility coefficients to avoid the basic ACO algorithm from falling into a local optimum in the object classification scheduling problem. The improved MAPPO algorithm significantly outperforms other algorithms in terms of convergence speed and stability in multi-intelligent body fixed and non-fixed goal path planning, and the path planning results and reasoning time also show obvious superiority among the compared algorithms. Taking the logistics scheduling of peaches as an example, the algorithm in this paper can significantly save costs and reduce the delayed pre-cooling cost to 0, effectively solving the problem of pre-cooling delay in logistics scheduling.

Heat transfer in a channel with internal intensifiers during azeotropic mixture circulation

Today, the use of mixtures as refrigerants and coolants in various energy systems is widespread. The volatility coefficients for the components of a non-azeotropic binary mixture vary significantly. In an azeotropic binary mixture, the volatility coefficients for both components are the same. This paper presents the experimental results on the intensity of heat transfer to an azeotropic alcohol-water mixture during forced circulation in a round heated channel. Experiments are carried out both in a smooth channel and in a channel with a spiral intensifier. The inner diameter of the channel is D = 7.6 mm, the length is 2 m. The intensifier surface has a hydrophobic coating. The experiments are carried out at low (0.03 – 0.04 MPa) pressure in the system, in the range of mass velocities 36 < M < 376 kg/m2s and Reynolds numbers of 360 – 3700. The heat flux density in these experiments is 8000 W/m2. In the single-phase flow of an azeotropic alcohol-water mixture in the channel with a spiral intensifier, the heat transfer coefficient is up to 5–6 times higher than that in a smooth channel. In this case, the higher the mass flow velocity, the greater the excess of the heat transfer coefficient. In the two-phase flow of the mixture in the channel with a spiral intensifier, the heat transfer coefficient is up to 2–3 times higher than that in a smooth channel.

Path Planning Method in Taigu County Based on the Hybrid Ant Colony Optimization-Genetic Algorithm in the Context of COVID-19

<p>During the period of COVID-19, there is a mixture of areas that are susceptible to COVID-19 infection and areas that are not susceptible to COVID-19 infection in cities. Blind wandering is often accompanied by the risk of infection. Hence, in order to improve the safety of people’s travel, this paper uses the hybrid Ant Colony Optimization-Genetic Algorithm (ACO-GA) to plan the central path of Taigu County. The volatilization coefficient of pheromone in Ant Colony Optimization (ACO) is changed dynamically. Pheromones in high-risk areas that are susceptible to epidemic infection are more volatile, while pheromones in low-risk areas that are less suscep-tible to epidemic infection are less volatile. Adjust the selection of “gene” mutation in Genetic Algorithm (GA). Vulner-able areas should be closed off to cut off the source of infection. As long as the shortest route which is of lower risk is formulated, people should stay away from high-risk areas that are susceptible to infection as much as possible to reduce the spread of COVID-19 and ensure the safety of people’s lives. The path under the influence of the COVID-19 is predicted and analyzed in the form of a simulation. The experimental results show that the algorithm can help to effectively avoid areas susceptible to the COVID-19 and reduce the risk of people getting sick.</p> <p> </p>

A Study of the Effect of Pressure on the Relative Volatility of the Components in the Process of Rectification of Mixtures of Different Natures

The effect of pressure on the coefficient of relative volatility of binary mixtures of different natures and ternary mixtures containing potential separating agents such as dimethylsulfoxide (DMSO) and butyl propionate is studied. A wide range of information is obtained based on the data on the vapor–liquid equilibrium, and effects of the pressure on the relative volatility of various nature and cause for a fixed composition of the initial mixture are determined. In the presence of points of intersection of the phase-equilibrium curves in the initial binary mixture and pseudobinary mixture (in the section with a constant concentration of the third high-boiling component), the concentration simplex is divided into regions in which the values of the coefficient of relative volatility decrease (increase) upon varying pressure. This fact makes it possible to improve the procedure of optimization of the process of separation at the stage of selection of the working pressure in the columns.

Oil Price-Exchange Rate Interdependence: Relevance of Theory of Exchange Rate Overshooting in Nigeria

This article investigated the interdependence between crude oil variability and foreign exchange rate changes in Nigeria. Oil price variability is decomposed into fluctuation on Brent Oil, West Texas Intermediate, OPEC Basket Reference Crude Oils and Bonny Light Crude Oil Price Variability. All the data that used in this research come from secondary sources. Annual time series data for Nigeria are used from 1993 to 2022. The research design for the study is ex post facto research analysis of annual multivariate time series data. The study examined the historical data in order to understand the current state of foreign exchange rate in Nigeria and causal connections crude oil price variability using ARDL Bound test, Johansen approach and Error-Correction Mechanism (ECM) models. In the long run, the coefficient of oil price volatility shows a positive and significant relation with the f o r e i g n e x c h a n g e r a t e at 10 percent level of significance. This implies that a 10 percent, increase in oil price volatility induces foreign exchange rate by 0.61 percent. The positive coefficient of oil price volatility tends to reveal that Dornbusch (1976) Overshooting hypothesis of foreign exchange rate holds for Nigeria. This finding is in conformity with previous studies like (Driskill & McCafferty, 1982); Turnovsky & Bhandari, 1982; Driskill & McCafferty, 1982). Furthermore, the coefficient of BOP and OPEC show a negative and insignificant relationship with the foreign exchange rate in the long run. This implies that BOP and OPEC hav e no impact on foreign exchange rate. This is in contrast with economic theory particularly Dutch diseases proposition and in conformity with foreign exchange rate overshooting theory. The negative nexus can be blamed on the inability of government to manage the foreign exchange supply and demand effectively in the country. Diversifying away from oil to other non-oil activities that would generate foreign exchange sh

Why is firewood use so persistent? The role of income uncertainty

Despite significant public efforts towards energy transition, rural households in the developing world often continue to burn traditional biomass as an energy source, sometimes even when they have already partially adopted modern fuels. The motivations for such “energy stacking” (as opposed to switching) remain poorly understood. This study highlights the role of income uncertainty in explaining the slow uptake of modern fuels and the persistence of “stacking”. We construct an analytical framework of energy choices and show energy stacking is rational for households with uncertain incomes, then empirically test the impact of off-farm income volatility on the energy choices of rural households. Using ordered and multinomial logistic regressions on data from 8 midwestern provinces of China, we show that households who had more volatile off-farm income in the past are significantly less likely to adopt modern fuels, partially or fully. A one-unit increase in past income volatility (coefficient of variation) is associated with increased likelihood of using only traditional energy for cooking and heating, by 5.5 % and 3.3 % respectively. This effect is more pronounced for more essential energy uses including cooking and, for cold areas, heating. Our findings highlight the importance of household income stability in the transition to clean energy.

Parameter Estimation in Rough Bessel Model

In this paper, we construct consistent statistical estimators of the Hurst index, volatility coefficient, and drift parameter for Bessel processes driven by fractional Brownian motion with H<1/2. As an auxiliary result, we also prove the continuity of the fractional Bessel process. The results are illustrated with simulations.

On the Stochastic Volatility in the Generalized Black-Scholes-Merton Model

This paper discusses the generalized Black-Scholes-Merton model, where the volatility coefficient, the drift coefficient of stocks, and the interest rate are time-dependent deterministic functions. Together with it, we make the assumption that the volatility, the drift, and the interest rate depend on a gamma or inverse-gamma random variable. This model includes the models of skew Student’s t- and variance-gamma-distributed stock log-returns. The price of the European forward-start call option is derived from the considered models in closed form. The obtained formulas are compared with the Black-Scholes formula through examples.

A self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism

Background and objectiveAs a representative type of cardiovascular disease, persistent arrhythmias can often become life-threatening. In recent years, machine learning-based ECG arrhythmia classification aided methods have been effective in assisting physicians with their diagnosis, but these methods have problems such as complex model structures, poor feature perception ability, and low classification accuracy. MethodsIn this paper, a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism is proposed. This method does not distinguish between subjects when establishing the dataset in order to reduce the effect of differences in ECG signal features between individuals, thus improving the robustness of the model. When the classification is achieved, a correction mechanism is introduced to correct outliers caused by the accumulation of errors in the classification process in order to improve the classification accuracy of the model. According to the principle that the flow rate of gas can be increased under the convergence channel, a dynamically updated pheromone volatilization coefficient ρ, namely the increased flow rate ρ, is introduced to help the model converge more stably and faster. As the ants move, the next transfer target is selected by a truly self-adjusting transfer method, and the transfer probability is dynamically adjusted according to the pheromone concentration and the path distance. ResultsBased on the MIT-BIH arrhythmia dataset, the new algorithm achieved classification of five heart rhythm types, with an overall accuracy of 99.00%. Compared to other experimental models, the classification accuracy of the proposed method represents a 0.2% to 16.6% improvement, and compared to other current studies, the classification accuracy of the proposed method is 0.65% to 7.5% better. ConclusionsThis paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. Experiments demonstrate the superiority of the proposed method compared to basic models as well as those with improved partial structures. Furthermore, the proposed method achieves very high classification accuracy with a simple structure and fewer iterations than other current methods.

Econometric and stochastic analysis of stock price before and during COVID-19 in India.

Unexpected and sudden spread of the novel Coronavirus disease (COVID-19) has opened up many scopes for researchers in the fields of biotechnology, health care, educational sectors, agriculture, manufacturing, service sectors, marketing, finance, etc. Hence, the researchers are concerned to study, analyze and predict the impact of infection of COVID-19. The COVID-19 pandemic has affected many fields, particularly the stock markets in the financial sector. In this paper, we have proposed an econometric approach and stochastic approach to analyze the stochastic nature of stock price before and during a COVID-19-specific pandemic period. For our study, we considered the BSE SENSEX INDEX closing pricing data from the Bombay Stock Exchange for the period before and during COVID-19. We have applied the statistical tools, namely descriptive statistics for testing the normal distribution of data, unit root test for testing the stationarity, and GARCH and stochastic model for measuring the risk, also investigated drift and volatility (or diffusion) coefficients of the stock price SDE by using R Environment software and formulated the 95% confidence level bound with the help of 500 times simulations. Finally, the results have been obtained from these methods and simulations are discussed.

Perfluoro(7-methylbicyclo[4.3.0]nonane) and Perfluoro(butylcyclohexane): Physicochemical, Thermophysical, and Spectral Data

New physicochemical data for perfluoro(7-methylbicyclo[4.3.0]nonane) and perfluoro(butylcyclohexane) with a purity ≥ 0.991 mass fraction are presented: the temperature and heat of the solid–liquid phase transition, the dependence of saturated vapor pressure, viscosity, density (liquid molar volume), and refractive index on temperature; the coefficients of the Antoine equation; 19F and 13C NMR and FTIR spectra; and gas chromatography–mass spectrometry data. For the perfluoro(7-methylbicyclo[4.3.0]nonane)–perfluoro(butylcyclohexane) binary system, the dependences are given: refractive index (at 15 °C) and density (at 20 °C) on the composition and boiling point on the composition in the pressure range from 20 kPa to atmospheric pressure. The average integral value of the relative volatility coefficient for various concentration ranges at atmospheric pressure is determined.

Partial Vapor Pressures, Activity Coefficients, Henry Volatility, and Infinite Dilution Activity Coefficients of Nicotine from Binary Mixtures with Glycerol and with 1,2-Propanediol at 298.15 K.

The equilibrium headspace concentration of nicotine in nitrogen gas was measured by gas chromatography for binary mixtures of nicotine with glycerol and with 1,2-propanediol at temperatures near 298.15 K. The storage temperature ranged from 296.25 to 298.25 K. The nicotine mole fraction ranged from 0.0015 ± 0.00010 to 0.998 ± 0.0016 for the glycerol mixtures and 0.00506 ± 0.000019 to 0.999 ± 0.0038 for the 1,2-propanediol mixtures (k = 2 expanded uncertainty). The headspace concentration was converted to nicotine partial pressure at 298.15 K using the ideal gas law, followed by the Clausius-Clapeyron equation. Both solvent systems had a positive deviation of nicotine partial pressure from ideal behavior, but the deviation of the glycerol mixtures was much greater than that of the 1,2-propanediol mixtures. For mole fractions of about 0.02 or less, the glycerol mixtures had nicotine activity coefficients of 11, while that for the 1,2-propanediol mixtures was 1.5. The Henry's law volatility constant and infinite dilution activity coefficient ± expanded uncertainty for nicotine from glycerol mixtures (51.4 ± 1.8 Pa and 12.4 ± 1.5, respectively) were approximately an order of magnitude greater than those from 1,2-propanediol mixtures (5.26 ± 0.52 Pa and 1.42 ± 0.14, respectively).

Statistical analysis of discretely sampled semilinear SPDEs: a power variation approach

Motivated by problems from statistical analysis for discretely sampled SPDEs, first we derive central limit theorems for higher order finite differences applied to stochastic processes with arbitrary finitely regular paths. These results are proved by using the notion of \(\Delta \)-power variations, introduced herein, along with the Hölder-Zygmund norms. Consequently, we prove a new central limit theorem for \(\Delta \)-power variations of the iterated integrals of a fractional Brownian motion. These abstract results, besides being of independent interest, in the second part of the paper are applied to estimation of the drift and volatility coefficients of semilinear stochastic partial differential equations in dimension one, driven by an additive Gaussian noise white in time and possibly colored in space. In particular, we solve the earlier conjecture from Cialenco et al. (Stat. Inference Stoch. Process. 23:83-103, 2020) about existence of a nontrivial bias in the estimators derived by naive approximations of derivatives by finite differences. We give an explicit formula for the bias and derive the convergence rates of the corresponding estimators. Theoretical results are illustrated by numerical examples.

Optimal reduction and equilibrium carbon allowance price for the thermal power industry under China’s peak carbon emissions target

As the largest source of carbon emissions in China, the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle. Its conversion to clean-energy generation technologies is also an important means of reducing CO2 emissions and achieving the carbon peak and carbon neutral commitments. This study used fractional Brownian motion to describe the energy-switching cost and constructed a stochastic optimization model on carbon allowance (CA) trading volume and emission-reduction strategy during compliance period with the Hurst exponent and volatility coefficient in the model estimated. We defined the optimal compliance cost of thermal power enterprises as the form of the unique solution of the Hamilton–Jacobi–Bellman equation by combining the dynamic optimization principle and the fractional Itô’s formula. In this manner, we obtained the models for optimal emission reduction and equilibrium CA price. Our numerical analysis revealed that, within a compliance period of 2021–2030, the optimal reductions and desired equilibrium prices of CAs changed concurrently, with an increasing trend annually in different peak-year scenarios. Furthermore, sensitivity analysis revealed that the energy price indirectly affected the equilibrium CA price by influencing the Hurst exponent, the depreciation rate positively impacted the CA price, and increasing the initial CA reduced the optimal reduction and the CA price. Our findings can be used to develop optimal emission-reduction strategies for thermal power enterprises and carbon pricing in the carbon market.