Volatility Coefficient Research Articles

Portfolio performance analysis: a case study of cryptocurrencies

The study measures the risk level linked with different portfolios of cryptocurrencies by using portfolio diversification techniques. Data on prices and trade volumes of cryptocurrencies were collected on a daily basis, from 2012 till 2018. Ten portfolios were constructed based on diverse types and numbers of cryptocurrencies. The results of the study confirm a negative relationship between the average number of cryptocurrencies and the average risk level of the portfolio. Involving more cryptocurrencies within the portfolio reduces the diversification risk of the portfolio. The average volatility and average correlation coefficient both drop when moving towards portfolios with more cryptocurrencies. Average returns stand against portfolio theories, where more risky portfolios offer less daily weighted average returns and the other way around. Outcomes of the study provide indications for the individual investors and financial institutions on the risk characteristic attached to the portfolio of cryptocurrencies.

Study on Safe Evacuation Routes Based on Crowd Density Map of Shopping Mall

When there are too many people in large shopping malls, crowd congestion accidents are likely to occur. To ensure the rapid and safe evacuation of indoor crowds, this paper uses crowd density maps to determine the location of crowded areas and uses an improved ant colony algorithm to optimize the evacuation route from this location to the exit. First, a crowd density map is generated from the collected image data by the improved multiscale convolutional neural network algorithm, and the location of the high-density crowd is determined as the initial location. Then, the pheromone volatility coefficient $\rho $ is measured through adaptive adjustment by using the exponential decline method and the introduction of elite ants to optimize and update the ant colony pheromone to improve the ant colony algorithm, and the optimal evacuation route from the location of the crowded area to the exit is obtained. The research in this paper uses Beijing Xidan Joy City as an example. The results show that the method in this paper can optimize evacuation routes and reduce the turning points of the evacuation route by 25% and reduce the route length by 10%. Therefore, it can be seen that the proposed method can achieve the optimal evacuation path with the shortest distance and the least turning points, which has feasibility and practicability.

Research on Multi-AGVs Path Planning and Coordination Mechanism

Reasonable automatic guided vehicle path planning can shorten the transportation time of materials and improve the production efficiency of the intelligent assembly workshop. Ant colony algorithm is a widely used path planning method, however, it suffers from the shortcomings that being easy to fall into local optimum and low search efficiency. To overcome these shortcomings, first, this paper proposes a step optimization method to improve the search efficiency of the ant colony algorithm, and a path simplification method to avoid getting blindly tortuous paths; Second, to overcome the problem that the ant colony algorithm is easy to fall into the local optimum, this paper proposes an adaptive pheromone volatilization coefficient strategy, which uses different pheromone volatilization coefficients at different stages of the search path; third, for the path conflict problem of multiple automatic guided vehicles, this paper proposes a load balancing strategy to avoid it, which is based on the consideration that, path conflicts are caused by excessive concentration of multiple automatic guided vehicles paths. Extensive simulation results demonstrate the feasibility and efficiency of the proposed methods.

DEMAND-SIDE MANAGEMENT IN TERRITORIAL ENTITIES BASED ON THEIR VOLATILITY TRENDS

This article explores the issues of assessing electricity consumption by individual countries and territorial entities based on their demand volatility. The current demand-side management (DSM) programs deployed in different countries of the world do not take into account individual characteristics of energy demand, which affects their effectiveness. This article describes the methodology developed by the authors to analyze demand volatility in individual countries and territorial entities using a demand volatility map. The authors introduce new indicators for assessing the electricity demand volatility such as: “Annual Load Volatility Factor,” “Daily Load Factor” and “Demand Volatility Coefficient,” which underlie a demand volatility map. The map is used to identify similar demand volatility trends among individual countries and territorial entities and offer recommendations and management decisions for decreasing volatility in the grid on the level of individual countries and territorial entities. This article presents the results of testing the methodology in the form of two demand volatility maps – one built for the EU, the other for Russia. The EEC volatility map emphasized the need for a differentiated approach to DSM in different countries. It also allowed clustering the countries and revealed states with similar trends where similar DSM models could be deployed. The Russia-wide volatility map showed the regions that are best positioned for DSM and allowed the authors to produce recommendations on DSM for similar regional groups. The results of the study have high theoretical and practical importance which manifests in their potential application for the decision-making in the field of DSM in individual countries and territorial entities, aimed at reducing energy consumption and improving energy efficiency.

Application of improved ant colony optimization in mobile robot trajectory planning.

Under the condition of known static environment and dynamic environment, an improved ant colony optimization is proposed to solve the problem of slow convergence, easily falling into local optimal solution, deadlock phenomenon and other issues when the ant colony optimization is constructed. Based on the traditional ant colony optimization, the ant colony search ability at the initial moment is strengthened and the range is expanded to avoid falling into the local optimal solution by adaptively changing the volatility coefficient. Secondly, the roulette operation is used in the state transition rule which improves the quality of the solution and the convergence speed of the algorithm effectively. Finally, through the elite selection and the node crossover operation of the better path, the global search efficiency and convergence speed of the algorithm are effectively improved. Several experimental results have also been obtained by applying the improved ant colony optimization to obstacle avoidance. The experimental results demonstrate the feasibility and effectiveness of the algorithm.

Distribution of Hydrogen Isotopes between Phases at Vapor–Liquid Equilibrium in Aqueous Salt Solutions

A single separation effect between deuterium and protium was studied experimentally under phase equilibrium conditions in a water vapor–water salt solution system at temperatures from 59 to 94°C. The vapor–liquid equilibrium was investigated by the dynamic method in a Bushmakin device. The presence of urea in water significantly increased the separation coefficient αL–V in the given temperature range. The characteristic increase in the relative volatility coefficient of the hydrogen isotopes of water remained unchanged at lower temperatures in the presence of salts. The isotope salt effects, both negative and positive, increased with the salt concentration.

QMLE for Periodic Time-Varying Asymmetric $$\log $$ GARCH Models

This paper establishes probabilistic and statistical properties of the extension of time-invariant coefficients asymmetric $$\log $$ GARCH processes to periodically time-varying coefficients ($$P\log $$ GARCH) one. In these models, the parameters of $$\log -$$volatility are allowed to switch periodically between different seasons. The main motivations of this new model are able to capture the asymmetry and hence leverage effect, in addition, the volatility coefficients are not a subject to positivity constraints. So, some probabilistic properties of asymmetric $$P\log $$ GARCH models have been obtained, especially, sufficient conditions ensuring the existence of stationary, causal, ergodic (in periodic sense) solution and moments properties are given. Furthermore, we establish the strong consistency and the asymptotic normality of the quasi-maximum likelihood estimator (QMLE) under extremely strong assumptions. Finally, we carry out a simulation study of the performance of the QML and the $$P\log $$ GARCH is applied to model the crude oil prices of Algerian Saharan Blend.

Comparison of Active Nitrogen Loss in Four Pathways on a Sloped Peanut Field with Red Soil in China under Conventional Fertilization Conditions

Active nitrogen loss mainly includes ammonia (NH3) volatilization, nitrous oxide (N2O) emission, NO3−-N and NH4+-N deep leakage (N leaching), and NO3−-N and NH4+-N surface runoff (N runoff), resulting in serious environmental problems. To analyze the characteristics of active nitrogen loss in the four pathways on sloped farmland under conventional fertilization, six lysimeters with a slope of 8° were used. Losses due to NH3 volatilization, N2O emission, N leaching, and N runoff were investigated after urea application on a peanut field with red soil in China during the growing season from 2017–2018. Results reveal that at conventional nitrogen levels of 150 and 172 kg hm−2, the total active nitrogen loss caused by fertilization accounting for the total nitrogen applied was 5.57% and 14.21%, respectively, with the N2O emission coefficients of 0.18% and 0.10%, respectively; the NH3 volatilization coefficients of 2.24% and 0.31%, respectively; the N leakage loss rates of 3.07% and 10.50%, respectively; and the N runoff loss rates of 0.08% and 3.30%, respectively. The dry year was dominated by leaching and NH3 volatilization, while the wet year was dominated by leaching and runoff; the base fertilizer period was dominated by leakage, while the topdressing period was dominated by leakage and runoff, which suggests that the loss of active nitrogen in the soil-peanut system on a sloped red soil was mainly affected by rainfall and fertilization methods. Taken together, reasonable fertilization management and soil and water conservation measures appear to be effective in minimizing the loss of active nitrogen from nitrogen fertilizer.

Bayesian inference approach to inverse problems in a financial mathematical model

This paper investigates an inverse problem of option pricing in the extended Black–Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in financial markets using a Bayesian inference approach. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC), which explores the posterior state space. The efficient sampling strategy of an MCMC enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown drift and volatility coefficients from the measured data.

Impact of the volume and the profile of volatile fatty acids in the rumen fermentation on cow productivity and milk composition

The aim of the research was to establish relations between the selected rumen fermentation products and the milk composition in dairy cows. The experiment was conducted on 10 Polish Holstein-Friesian cows of black and white variety. The animals were kept in the free stall housing system and fed with the total mixed ration (TMR). The herd’s milk production was assessed using the A4 method and the milk analyses were obtained from the milk recordings. The rumen digesta were sampled two hours after the morning delivery of TMR using a stomach pump. The sampled digesta were diluted with buffer, homogenized and the obtained suspension was analysed using gas chromatograph with the FID detector to determine the total concentration of volatile fatty acids (VFA) and the percentage of individual VFAs. Milk composition was obtained from the RW-1 reports, and the Pearson correlation coefficient was calculated for the obtained parameters. It was determined that propionate and the relation of propionate to butyrate in the rumen digesta increased the protein content and its fractions in milk. The volatile fatty acids utilization coefficient in the rumen digesta a positive correlation to protein contents and a negative correlation to the level of caseins in milk were shown. Processes taking place in the cow rumen significantly influence the composition and quality of milk. The knowledge of volatile fatty acids content and the of individual VFAs allows for the management of the rumen fermentation in order to obtain better technological parameters of milk.

Non-standard inference for augmented double autoregressive models with null volatility coefficients

This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows the applicable scope in studying heavy-tailed data. To overcome this deficiency, this paper develops a systematic statistical inference procedure based on the self-weighted GQMLE for the augmented DAR model. Except for the Lagrange multiplier test statistic, the Wald, quasi-likelihood ratio and portmanteau test statistics are all shown to have non-standard asymptotics. The entire procedure is valid as long as the data are stationary, and its usefulness is illustrated by simulation studies and one real example.

A note on parameter estimation for discretely sampled SPDEs

We consider a parameter estimation problem for one-dimensional stochastic heat equations, when data is sampled discretely in time or spatial component. We prove that, the real valued parameter next to the Laplacian (the drift), and the constant parameter in front of the noise (the volatility) can be consistently estimated under somewhat surprisingly minimal information. Namely, it is enough to observe the solution at a fixed time and on a discrete spatial grid, or at a fixed space point and at discrete time instances of a finite interval, assuming that the mesh-size goes to zero. The proposed estimators have the same form and asymptotic properties regardless of the nature of the domain –bounded domain or whole space. The derivation of the estimators and the proofs of their asymptotic properties are based on computations of power variations of some relevant stochastic processes. We use elements of Malliavin calculus to establish the asymptotic normality properties in the case of bounded domain. We also discuss the joint estimation problem of the drift and volatility coefficient. We conclude with some numerical experiments that illustrate the obtained theoretical results.

The Determinants of the Financing Decision of Listed Companies on the Bucharest Stock Exchange

The previous literature attempted to provide explanations for the differences identified in the capital structure of companies, with a set of more or less practically applicable theories. This paper aims to capture the influence of the main factors identified in the relevant literature on the capital structure of a sample of listed companies on the main market of the Bucharest Stock Exchange (BVB) and, in particular, to validate or invalidate some of the theories that attempted to explain the differences in the capital structure in the literature. The results show that the capital structure of the Romanian listed companies is positively correlated with the existence of growth opportunities and negatively with the profitability, liquidity, asset tangibility and volatility coefficient.

A THRESHOLD MODEL FOR LOCAL VOLATILITY: EVIDENCE OF LEVERAGE AND MEAN REVERSION EFFECTS ON HISTORICAL DATA

In financial markets, low prices are generally associated with high volatilities and vice-versa, this well-known stylized fact is usually referred to as the leverage effect. We propose a local volatility model, given by a stochastic differential equation with piecewise constant coefficients, which accounts for leverage and mean-reversion effects in the dynamics of the prices. This model exhibits a regime switch in the dynamics according to a certain threshold. It can be seen as a continuous-time version of the self-exciting threshold autoregressive (SETAR) model. We propose an estimation procedure for the volatility and drift coefficients as well as for the threshold level. Parameters estimated on the daily prices of 351 stocks of NYSE and S&P 500, on different time windows, show consistent empirical evidence for leverage effects. Mean-reversion effects are also detected, most markedly in crisis periods.

Characteristics of volatile release rate and fuel nitrogen conversion for three typical coals under high temperature fast pyrolysis

Drop Tube Furnace experiments were carried out to investigate the fast pyrolysis characteristics of three typical coals. The process of fast pyrolysis was simulated by a one-step pyrolysis model as well as a Chemical Percolation Devolatilization (CPD) model. The results indicate that coal rank, reaction temperature and residence time have complex influences on volatile release and nitrogen conversion. Both activation energy and frequency factor, which decrease with increasing coal rank, show obvious differences for the three considered coals. The volatile release coefficient QR is relative to coal rank and heating rate which fluctuates from 1.02 to 1.72. Predictions of volatile release and coal nitrogen conversion obtained from CPD model agree well with experiments. In addition, the one-step pyrolysis model could predict the volatile release of low-rank coal, whose precision is related to coal rank.

Heteroskedasticity in excess Bitcoin return data: Google trend vs. GARCH effects

This paper examines the mixture of distribution properties associated with heteroskedastic excess Bitcoin return data, using the volume of Google search queries as a proxy for the information arrival time, from a monthly data sampling period of June 2010 to May 2019. The results show that the volatility coefficients become highly statistically insignificant when the lagged volume of search queries is included in the conditional variance equation of the GJR-GARCH-M model. This clearly suggests that the volume of search queries is shown to provide significant explanatory power regarding the variance of heteroskedastic excess Bitcoin return, which can be traced from the ARCH process defined in the GJR-GARCH-M specification. A significant negative relationship between the conditional volatility and the volume of search queries indicates that Internet (online) information arrival reduces the risk premium in the Bitcoin market, which may improve market stability.

Models for assessment of volatile organic compound diffusivities in leather or leather imitates by mercury intrusion porosimetry test

Recent studies have been conducted on the characteristics of volatile organic compound emissions; most of them are focused on wood-based building materials, scarcely mentioned about leather or leather imitates. The volatile organic compound emissions of these products may cause poor indoor (vehicle cabins included) air quality. This article takes formaldehyde as an example to study its diffusion in leather. The porous structure of leather was characterized by carrying out mercury intrusion porosimetry test. The formaldehyde diffusivities in leather was assessed by Blondeau et al.’s, Ataka et al.’s, and Xiong et al.’s models which represent parallel, equivalent, and series interconnection of pores, respectively, and validated through independent liquid inner tube diffusion film emission experiments. The results demonstrated that the fractal dimension of leather is 2.19, which indicated it is a porous medium with self-similar pore structure. Most of the pores in leather were macro pores with average diameter 16.56 μm which contributed to 95.9% of whole porosity and the meso pore with average diameter 0.60 μm which contributed to 4.1%. As far as the liquid inner tube diffusion film emission experiment shows, the Xiong model agrees much better with the experimental results than the other two. The two kinds of pores were considered to be serially connected. It is useful for predicting volatile organic compounds diffusion coefficient in a short time and can be applied to the design and development of low volatile organic compound emission leather or leather imitates within further research.

On periodic parameter identification in stochastic differential equations

Periodic parameters are common and important in stochastic differential equations (SDEs) arising in many contemporary scientific and engineering fields involving dynamical processes. These parameters include the damping coefficient, the volatility or diffusion coefficient and possibly an external force. Identification of these periodic parameters allows a better understanding of the dynamical processes and their hidden intermittent instability. Conventional approaches usually assume that one of the parameters is known and focus on the recovery of rest parameters. By introducing the decorrelation time and calculating the standard Gaussian statistics (mean, variance) explicitly for the scalar Langevin equations with periodic parameters, we propose a parameter identification approach to simultaneously recovering all these parameters by observing a single trajectory of SDEs. Such an approach is summarized in form of regularization schemes with noisy operators and noisy right-hand sides and is further extended to parameter identification of SDEs which are indirectly observed by other random processes. Numerical examples show that our approach performs well in stable and weakly unstable regimes but may fail in strongly unstable regime which is induced by the strong intermittent instability itself.

СПОСОБ ОПРЕДЕЛЕНИЯ ТЕМПЕРАТУРЫ КИПЕНИЯ НЕИДЕАЛЬНОЙ МНОГОКОМПОНЕНТНОЙ СМЕСИ

A method for determining the boiling point of a multicomponent mixture through the
 volatility coefficients of the components is considered. A numerical experiment was carried out to calculate the boiling point and concentration of the vapor phase components under a wide range of system pressure and composition of the components in the liquid.

The impact of patent citation information flow regarding economic innovation on common stock returns: Volume vs. patent citations

This study examines whether the number of forward patent citations (along with alternative patent data)—when used as a proxy for the mixing variable—could infer the aggregate amount of economic-innovation information arriving at the New York Stock Exchange (NYSE) in the United States. The results show that the number of forward patent citations, when used as a mixing variable, fails to eliminate total volatility persistence in the conditional variance equation of the exponential generalized autoregressive conditional heteroscedastic (EGARCH) model. However, the trading volume successfully eliminates total volatility persistence, thus confirming the validity of the framework used. When the volatility is modeled with an expectation of mean return, the persistence of conditional variance is deterministically increased, and the sum of the volatility coefficients exceeds unity. The inclusion of trading volume with a time trend in the variance equation rectifies the deterministic increase in the conditional volatility. These findings suggest that the form of heteroscedasticity (i.e., as per the autoregressive conditional heteroscedastic model, ARCH model) in NYSE portfolio returns is based on the type of shocks to volatility (e.g., deterministic vs. stochastic), which manifests as news arrivals (i.e., new information arrivals proxied by trading volume) at the stock market. The volume therefore reflects the time dependence in the innovations to the ARCH error generation process. The response of volatility to volume persists over time when the volatility estimates are derived from the EGARCH model with an expectation for the mean of return. Backward patent citations, patent applications, and patents issued have been found to interact somewhat with trading volume, suggesting that each of these variables could play the role of an absorptive capacity variable as the new information flow associated with economic innovation (i.e., flow of firms’ stock of new knowledge) could be picked up by the trading volume.