Volatility Parameter Research Articles

Computational Modelling of the Impact of Evaporation on In-Vitro Dermal Absorption.

Volatiles are common in personal care products and dermatological drugs. Determining the impact of evaporation of volatiles on skin permeation is crucial to evaluate and understand their delivery, bioavailability, efficacy and safety. We aim to develop an in-silico model to simulate the impact of evaporation on the dermal absorption of volatiles. The evaporation of volatile permeants was modelled using vapour pressure as the main factor. This model considers evaporation as a passive diffusion process driven by the concentration gradient between the air-vehicle interface and the ambient environment. The evaporation model was then integrated with a previously published physiologically based pharmacokinetic (PBPK) model of skin permeation and compared with published in vitro permeation test data from the Cosmetics Europe ADME Task Force. The evaporation-PBPK model shows improved predictions when evaporation is considered. In particular, good agreement has been obtained for the distributions in the evaporative loss, and the overall percutaneous absorption. The model is further compared with published in-silico models from the Cosmetics Europe ADME Task Force where favourable results are achieved. The evaporation of volatile permeants under finite dose in vitro permeation test conditions has been successfully predicted using a mechanistic model with the intrinsic volatility parameter vapour pressure. Integrating evaporation in PBPK modelling significantly improved the prediction of dermal delivery.

Bayesian Lower and Upper Estimates for Ether Option Prices with Conditional Heteroscedasticity and Model Uncertainty

This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower and upper estimates for prices of Ether options, that is, options written on Ethereum, with conditional heteroscedasticity and model uncertainty. Specifically, a discrete-time generalized conditional autoregressive heteroscedastic (GARCH) model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum, and Bayesian nonlinear expectations are adopted to introduce model uncertainty, or ambiguity, about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov’s principle is employed to change probability measures for introducing a family of alternative GARCH models and their risk-neutral counterparts. The Bayesian credible intervals for “uncertain” drift and volatility parameters obtained from conjugate priors and residuals obtained from the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD. Comparisons with a model incorporating conditional heteroscedasticity only and a model capturing ambiguity only are presented.

Trading Option Portfolios Using Expected Profit and Expected Loss Metrics

When trading in the call and put contracts of option chains, the portfolios of strikes must be selected. The trader must also decide whether to take long or short positions at the selected strikes. Dynamic strategies for making these decisions are discussed in this paper. On any day, the strategies estimate the drift and volatility parameters of the future probability distribution of the price of the underlying asset. From this distribution, the trader can further estimate the future expected profit and expected loss that may be experienced for any portfolio of strikes of the call and put contracts. Expected profit and expected loss are the reward and risk metrics of such portfolios. An optimal portfolio can then be selected by making the reward as high as possible under the risk tolerance set by the trader. Extensive back-testing applications to historical data of SPY option chains illustrate the effectiveness of these strategies, particularly when dealing with short-term expiry options and when acting as a seller of put and call options.

ASEAN-5 Stock Indexes Predictions using Geometric Brownian Motion

Even though the ASEAN-5 region has recently experienced enormous economic growth and advancement, this expansion has been associated with increased market volatility. Analysing historical data to assess stock market performance is crucial to comprehending these markets' dynamics and spotting potential hazards and opportunities. This study seeks to determine the mean and volatility parameters of the Geometric Brownian Motion (GBM) model for stock indexes to see trends and patterns in the ASEAN-5 stock market from 2017 to 2022. Once these parameters are determined, they are used in the GBM model to forecast the stock market indexes. Consequently, this research intends to highlight the value of incorporating GBM into stock indexes and assist investors in making short-term price predictions. The geometric Brownian motion involving randomness, volatility, and drift that might aid investors in making sensible investment decisions will be elaborated on in this research.

Real Options Volatility Surface for Valuing Renewable Energy Projects

Real Options Volatility Surface for Valuing Renewable Energy Projects

Patent Value Evaluation of Communication Enterprises Based on Real Option Method

Patents are an important asset for a company, especially for telecommunications companies. Evaluating the value of patent portfolios and analyzing the benefits brought by patents is of great significance to the development of communication enterprises with multiple patents. In this paper, the gray GM(1,1) model, random forest algorithm and Monte Carlo simulation are introduced into the B-S option pricing model of the real option method. The gray GM(1,1) model has unique utility for the analysis and modeling of short time series and few statistical data, and has the characteristics of high prediction accuracy and good effect. The random forest algorithm has great advantages in the calculation of revenue sharing rate, and Monte Carlo simulation can optimize volatility parameters. Therefore, the B-S model is used to analyze the 5G patent data of ZTE Co., Ltd. with access technology functions, which has certain operability and reference.

Markov Chain Monte Carlo (MCMC) Estimation of Heston Models Using Fourier Transform in Option Pricing

This study examines the role of stochastic volatility in option pricing through the Heston model, employing advanced computational techniques such as Markov Chain Monte Carlo (MCMC) methods and Fourier transforms. By comparing the pricing accuracy of the Heston model, which captures stochastic volatility, to the Black- Scholes model, which assumes constant volatility, we demonstrate the superior flexibility and robustness of the Heston framework. Our results show that the Heston model consistently outperforms Black-Scholes, particularly when volatility parameters estimated via MCMC are well-calibrated, enabling more accurate representation of market dynamics. These findings underscore the importance of precise parameter estimation and advanced modeling techniques in improving option pricing accuracy.

The estimations of drift parameters for the Gaussian Vasicek process with time-varying volatility

This article mainly considers the parameter estimation problem for the Gaussian Vasicek process defined as d X t = ( α − β X t ) d t + σ t d G t , t≥0 with unknown parameters α∈ℝ and β < 0, where G t is a continuous centered Gaussian process. The volatility parameter σ t is allowed to vary with time t. Based on the time-continuous observations and least squares method, the Least squares estimations of drift parameters for the non ergodic Gaussian Vasicek model with time-varying volatility are obtained. The strong consistency and asymptotic distributions of proposed estimations are verified under some sufficient conditions. Finally, we verify that the results are still valid under different forms of the volatility function when G t is fractional Brownian motion, sub-fractional Brownian motion, and bi-fractional Brownian motion, respectively. In some sense, our results extend the findings of El Machkouri, Es-Sebaiy, and Ouknine (2016) and Es-Sebaiy and Sebaiy (2021).

Quasi-likelihood analysis of fractional Brownian motion with constant drift under high-frequency observations

Consider an estimation of the Hurst parameter H∈(0,1) and the volatility parameter σ>0 for a fractional Brownian motion with a drift term under high-frequency observations with a finite time interval. In the present paper, we propose a consistent estimator of the parameter θ=(H,σ) combining the ideas of a quasi-likelihood function based on a local Gaussian approximation of a high-frequently observed time series and its frequency-domain approximation. Moreover, we prove an asymptotic normality property of the proposed estimator for all H∈(0,1) when the drift process is constant.

Robust estimation of integrated and spot volatility

We introduce a new method to estimate the integrated volatility (IV) and the spot volatility (SV) based on noisy high-frequency data. Our method employs the ReMeDI approach introduced by Li and Linton (2022) to estimate the moments of microstructure noise and thereby eliminate their influence, and the pre-averaging method to target the volatility parameter. The method is robust: it can be applied when the efficient price exhibits stochastic volatility and jumps, the observation times are random, and the noise process is nonstationary, autocorrelated, asymptotically vanishing and dependent on the efficient price. We derive the limit distributions for the proposed estimators under the infill asymptotics in a general setting. Our extensive simulation studies demonstrate the robustness, accuracy and computational efficiency of our estimators compared to several alternative estimators recently proposed in the literature. Empirically, we show that neglecting the complexities of noise and the random observation times generates substantial biases in volatility estimation and may yield a different intraday volatility pattern.

Statistical inference in discretely observed fractional Ornstein–Uhlenbeck processes

In this paper, we develop a two-stage method for estimating all the unknown parameters in the fractional Ornstein–Uhlenbeck model from discrete observations. The estimation procedure is built upon the marriage of the power variation and the minimum contrast estimation. In the first stage, the Hurst coefficient is estimated by the increment Bernoulli statistic and the volatility parameter is estimated by power variations. The asymptotic theory of the proposed estimators in the diffusion term are established under an in-fill asymptotic scheme. In the second stage, two drift parameters are estimated based on the minimum contrast estimation. Their asymptotic theory are analyzed via use of a double asymptotic scheme. The results from Monte Carlo studies illustrate that the proposed estimators have reasonable finite sample properties. An empirical illustration based on realized volatility indicates that the realized volatility is rough.

Effect of dimethyl carbonate addition on ethanol–gasoline fuel blend

The growing need for renewable and environmentally friendly sources of energy has motivated a lot of researchers to direct their efforts to meet these challenges. The use of renewable additives to gasoline, such as ethanol and methanol, has been a successful solution. However, blending ethanol into gasoline has some drawbacks, including increased gasoline volatility and significant changes in the distillation curve. This study investigated the effects of blending the eco-friendly dimethyl carbonate (DMC) with various concentrations into ethanol-gasoline fuel blend (E10) on some volatility parameters and octane number, which have not been previously reported in the literature. The fuel samples were formulated by mixing E10 with (0.0%, 2.0%, 4.0%, 6.0%, 8.0%, and 10.0%) of dimethyl carbonate. The main properties of the fuel samples were measured such as distillation curve, and octane number. The distillation process was carried out in accordance with ASTM-D86 while vapor pressure was measured in accordance with ASTM-D5191. The obtained results revealed interesting outcomes that may spark the interest of refineries in this promising fuel additive. Addition of DMC to gasoline-ethanol blend was found to have insignificant impact on the volatility of fuel. The results demonstrate that addition of ethanol to gasoline causes a significant decrease in T50 by about 20 °C, while addition of 10 volume percent of DMC to E10 causes an increase in T50 by about 2 °C. The obtained results showed also that the addition of 10 vol% of DMC to E10 fuel blend considerably increases the RON and MON by about 4 and 3.5 points, respectively.

Driving style identification and its association with risky driving behaviors among truck drivers based on GPS, load condition, and in-vehicle monitoring data

This study provides an approach to identify driving style of truck drivers by using GPS, load condition, and in-vehicle monitoring data and investigates the association of driving styles with risky driving behaviors from macro and micro perspectives. The naturalistic driving data used in this study were collected from 4,357 trucks in Hangzhou, China over three months in 2021. Six driving volatility parameters and six warning parameters were used to characterize the driving styles. Then, three driving styles under the two load conditions were identified using k-means clustering methods and principal component analysis. Finally, one-way MANOVA and ANOVA were used to analyze the relationship between driving styles and driving risk. It was found that truck drivers have different thresholds for aggressive and cautious driving style under different load conditions. Truck drivers who exhibited aggressive driving under both load conditions exhibited high driving risk. Although most truck drivers exhibited safe or normal driving under both conditions, the few who exhibited aggressive driving contribute to a disproportionate driving risk. These results can help distinguish differences in truck drivers’ driving styles under different load conditions, thus providing a more comprehensive safety assessment of truck drivers’ performance for monitoring purposes.

Dynamic effects and driving intermediations of oil price shocks on major economies

Interest in oil price shocks' economic effects has grown in recent years. However, previous studies mostly failed to clarify the dynamic transmissions of oil price shocks on representative economies from global and driver perspectives, even ignoring oil price fluctuations when linking oil prices and economy together. This paper examines the dynamic relationships and driving intermediations between multiple oil price shocks and macroeconomy by applying Bayesian vector autoregressive models with stochastic volatility and time-varying parameters, using the USA, China, the Euro-19, and Japan as research objects. Results show that, in the whole sample, all oil prices have the strongest effects on Japan, followed by China, Euro-19, and the USA, with possible directional differences. All oil prices' economic effects intensified during the crisis and Covid-19, accompanying significant oil price fluctuations. Regarding asymmetry, in the whole sample and critical times, stronger effects of rising oil prices show in the short term, but opposite in the long term. Consumer price, interest rate, and exchange rate are the general intermediations of oil prices in China and the USA, Euro-19, and Japan, respectively, and exchange rate is the additional intermediation in China, Euro-19, and Japan during the crisis and Covid-19. Overall, the results are solid.

ACID-BASE PROPERTIES AND VOLATILIITY OF FLUORIDES OF s-, p-, d-METALS

A correlation between acid-base properties and volatility parameters (temperature and enthalpy of vaporization) of fluorides of s-, p-, and d-metals was revealed. As a characteristic of acid-base properties, the basicity parameter proposed in the work, based on the size-charge characteristics of the ions that make up the compound, is taken. The value of the basicity parameter naturally decreases from fluorides of low-valent metals (I–III) to fluorides of highvalent metals (IV–VII). The indicated cation substitution results in a significant increase in the volatility of compounds, which is explained by a change in the type of crystal structure from ionic-covalent to molecular one typical for acid-type fluorides. Metal fluorides with intermediate values of the basicity parameter (Zr(IV), Hf(IV), Al(III)) are characterized by significantly higher values of temperature and evaporation enthalpy, that is, lower volatility compared to metal fluorides with a molecular type of structure, which indicates the manifestation of ionic-covalent type of structure. The entropy of evaporation of metal fluorides with a molecular type of structure, as well as with an ionic-covalent type, is characterized by low values (80 ÷ 140 J/mol∙K), which indicates minor changes in the structure of the molecules during evaporation. On the other hand, in the case of compounds with intermediate basicity (ZrF4, AlF3), the values of the entropy of evaporation are significantly higher (over 180 J/mol∙K), which indicates significant structural changes. A method of experimental evaluation of the volatility of compounds by measuring the rate of the coating condensation on the substrate is proposed. It is shown the possibility of combining metal fluorides into complex compounds based on the principle of basicity and on the known values of the evaporation temperature and the conventional temperature. The nature of the influence of anionic substitution in Zirconium compounds with the same charge of ions (F– → Cl– → Br– → l–) and with a change in charge (F– → O2– → N3– → C4–) on the basicity and volatility of the compounds was established. If in the first case there are no significant changes in the nature of the compounds, then in the second there is a sharp increase in melting and evaporation temperatures due to an increase in basicity.

The limit where Black-Scholes "works"

The seminal (Black and Scholes, 1973) publication celebrated in this issue introduced a (stock price evolution) model and an (option value) formula�two ideas worth distinguishing. Nowadays, the formula is used to represent option prices via an implied volatility, IV (T, K), where T is a time-to-maturity and K is a strike price. The formula is very useful. At the same time, because of the K-dependence, the model is strongly rejected in statistical tests. However, with T very large at fixed K, IV (T, K) typically flattens IV(T,K)? s8imp , where s8imp is independent of K. Flattening happens both with real data and many (more complicated) models. At fixed strike, the implied volatility parameter tends to a pure constant. But a constant volatility returns us to the original BS model in a sense. As somewhat of an abuse, I say that in the limit, the BS model again works�. What is this mysterious s8imp and how do we compute it? How is the limit approached? Answering those questions is the subject of this note. While my approach is largely expository, results for non- standard cases in the Heston model are likely novel. Their analysis requires a generalized saddle point method.

How to calibrate Gaussian two-factor model using swaption.

We propose an efficient approximation of the swaption normal volatility to estimate the mean reversion separately from the other volatility parameters in the Gaussian two-factor model. We compare our two-step approach with a one-step method that calibrates all parameters simultaneously. The comparison is based on the data from interest rate market of Korea and the US. The parameter estimates of our proposed two-step method are more stable than those of the one-step method in that the latter is overly sensitive to market changes whereas the former is not. The proposed approach also eliminates many existing problems in the Gaussian two-factor model.

System of Non-Linear Stochastic Differential Equations with Financial Market Quantities

In this paper, two systems of modified stochastic differential equations were considered. The variable coefficient problem was solved using Ito’s theorem to obtain an analytical solutions which was used to generate various behaviors of asset values which shows as follows: (i) increase in when are fixed increases the value of asset returns. (ii) a little increase on time when return rates and stock volatility are fixed increases the value of assets.(iii) an increase in the volatility parameter increases the value of asset pricing and parameter shows the various levels of long term investment plans, (iv) increase in rate of mean-reversion parameter reduces the value of asset. (v) An increase in the volatility parameter decreases the value of asset pricing (vi) The goodness of fit probability QQplots are not statistically significant and besides do come from a common distribution which has a vital meaning in the assessment of asset values for capital market investments. Nevertheless, the Tables 1,2 and 3 are best in comparisons with Tables 4,5 and 6 in terms of predictions for capital investments. The governing investment equations are unique and therefore are found to be satisfactory.

The Effects of Stochastic Variables on the Analysis of Stock Market Prices

In this paper, stochastic differential equation with some imposed parameters in the model was considered. The problem was solved by adopting Ito’s theorem to obtain an analytical solution which was used to generate various discrepancies on various asset prices. The asset values were obtained through the influences of some key stochastic variables which shows as follows:(i) increase in when are fixed increases the value of asset returns (ii) a little increase on time when return rates and stock volatility are fixed also increases the value of assets (iii) an increase in the volatility parameter increases the value of asset pricing (iv) , (v) a measure of parameter shows the various levels of long term investment plans . Finally, the normality probability plots are not statistically significant and besides do come from a common distribution which has a vital meaning in the assessment of asset values for capital market investments. However, the Tables, graphs and other stock variables were discussed. The governing investment equation is reliable and therefore is found to be adequate.

Robust retirement and life insurance with inflation risk and model ambiguity

We study a robust consumption-investment problem with retirement and life insurance decisions for an agent who is concerned about inflation risk and model ambiguity. Assuming that an inflation-linked index bond and a stock are available in the market, this paper considers a comprehensive setup of ambiguity in the return, volatility, and correlation parameters in the joint dynamics of their market prices. With a finite planning horizon, the agent has a general utility function with different marginal utilities of consumption before and after retirement. Combining the classical dual approach and the G-stopping time theory, we derive the novel robust strategies using integral equation representations. We numerically and extensively investigate the effects of ambiguity from different sources on the robust decisions. While model ambiguity generally leads the ambiguity- and risk-averse agent to decrease the consumption rate, life insurance purchase, and investment demands, it also generates contrasting effects on robust retirement time and wealth level. Specifically, model ambiguity lowers the target wealth level to immediate retirement of a young agent but increases the retirement time of an older agent compared to the case of known parameters. A rich agent takes ambiguity more seriously than a poor agent in the sense of adjusting the strategies on a more significant scale. Our simulation and comparison study demonstrate the significance of addressing the ambiguity in volatility and correlation in addition to the ambiguity in return.