Application of Elzaki Transform Method to Market Volatility Using the Black-Scholes Model

Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of European Stock options and establish the theoretical foundation for Option pricing. Therefore, this paper evaluates the Black-Schole model in simulating the European call in a cash flow in the dependent drift and focuses on obtaining analytic and then approximate solution for the model. The work also examines Fokker Planck Equation (FPE) and extracts the link between FPE and B-SM for non equilibrium systems. The B-SM is then solved via the Elzaki transform method (ETM). The computational procedures were obtained using MAPLE 18 with the solution provided in the form of convergent series.

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

Optimal Quota-Share and Excess-of-Loss Reinsurance and Investment with Heston’s Stochastic Volatility Model

An optimal quota-share and excess-of-loss reinsurance and investment problem is studied for an insurer who is allowed to invest in a risk-free asset and a risky asset.Especially the price process of the risky asset is governed by Heston's stochastic volatility(SV)model.With the objective of maximizing the expected index utility of the terminal wealth of the insurance company,by using the classical tools of stochastic optimal control,the explicit expressions for optimal strategies and optimal value functions are derived.An interesting conclusion is found that it is better to buy one reinsurance than two under the assumption of this paper.Moreover,some numerical simulations and sensitivity analysis are provided.

Model Estimation of Volatilization of Ammonia Appliedwith Surface Film-Forming Material

Greenhouse experiments were conducted to determine the ammonia volatilization loss with or withoutapplication of surface film-forming material (SFFM). Ammonia volatilization loss was estimated by the modeldeveloped by Jayaweera and Mikkelsen. The results showed that the model could estimate and predict wellammonia volatilization loss also in case of SFFM addition. There was an emended factor B introduced tothe model calculation when SFPM was used. Simulated calculation showed that the effect of factor B onNHa loss was obvious. The value of B was governed by SFFM and the environmental conditions. Sensitivityanalysis suggested that pH was the main factor coatrolling NH3 volatilization loss from the floodwater.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria:Current state of the art

Pathogenic microorganisms produce numerous metabolites,including volatile organic compounds(VOCs).Monitoring these metabolites in biological matrices(e.g.,urine,blood,or breath)can reveal the presence of specific microorganisms,enabling the early diagnosis of infections and the timely implementation of tar-geted therapy.However,complex matrices only contain trace levels of VOCs,and their constituent com-ponents can hinder determination of these compounds.Therefore,modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed.In this paper,we discuss bacterial VOC analysis under in vitro conditions,in animal models and disease diagnosis in humans,including techniques for offline and online analysis in clinical settings.We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis,in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species in-teractions,the kinetics of VOC metabolism,and species-and drug-resistance specificity.

Model Research on the Effect of Surface Film on Ammonia Volatilization from Rice Field

Pan and field experiments were conducted to investigate the effect of surface film on ammonia volatilization from water and paddy soil. The results showed that the addition of the surface film on floodwater reduced the rate of ammonia volatilization, however, the reduction of the latter varied greatly with its rates of addition. Jayaweera-Mikkelsen ammonia volatilization model with the introduction of a parameter Kf, a relative measure of the resistance of the surface film on ammonia volatilization, was used to elucidate the effectiveness of the surface film on lowering ammonia volatilization. The Kf value was calculated from the results ob-

A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET

Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models： Logistic, AR（1） and AR（2）. The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR（1） model and the AR（2） model of zero-mean series of the weekly dosing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price, Six common statistical methods for error prediction are used to test the predicting results. These methods are： mean error （ME）, mean absolute error （MAE）, root mean squared error （RMSE）, mean absolute percentage error （MAPE）, Akaike＇s information criterion （AIC）, and Bayesian information criterion （BIC）. The investigation shows that AR（1） model exhibits the best predicting result, whereas AR（2） model exhibits predicting results that is intermediate between AR（1） model and the Logistic regression model.

Comparative Study of Volatility Forecasting Models： The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponential Weighted Moving Average （EWMA）, Autoregressive Integrated Moving Average （ARIMA） and Generalized Auto-Regressive Conditional Heteroscedastic （GARCH）, in four stock markets Indonesia, Malaysia, Japan and Hong Kong. Using monthly closing stock index prices collected from 1 st January 1998 to 31 st December 2015 for the four selected countries, results obtained confirm that volatility in developed markets is not necessarily always lower than the volatility in emerging markets. Among all the three models, GARCH （1, l） model is found to be the best forecasting model for stock markets in Malaysia, Indonesia, and Japan, while EWMA model is found to be the best forecasting model for Hong Kong stock market. The outperformance of GARCH （1, 1） found supports again what is found in Minkah （2007）.

A Gibbs Sampling Algorithm to Estimate the Parameters of a Volatility Model: An Application to Ozone Data

In this work we consider a stochastic volatility model, commonly used in financial time series studies, to analyse ozone data. The model considered depends on some parameters and in order to estimate them a Markov chain Monte Carlo algorithm is proposed. The algorithm considered here is the so-called Gibbs sampling algorithm which is programmed using the language R. Its code is also given. The model and the algorithm are applied to the weekly ozone averaged measurements obtained from the monitoring network of Mexico City.

Modeling Stock Market Volatility Using GARCH Models: A Case Study of Nairobi Securities Exchange (NSE)

The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities Exchange (NSE). The conditional variance is estimated using the data from March 2013 to February 2016. We use both symmetric and asymmetric models to capture the most common features of the stock markets like leverage effect and volatility clustering. The results show that the volatility process is highly persistent, thus, giving evidence of the existence of risk premium for the NSE index return series. This in turn supports the positive correlation hypothesis: that is between volatility and expected stock returns. Another fact revealed by the results is that the asymmetric GARCH models provide better fit for NSE than the symmetric models. This proves the presence of leverage effect in the NSE return series.

Volatile Organic Compound Emissions from Surface Coating Facilities: Characterization of Facilities, Estimation of Emission Rates, and Dispersion Modeling of Off-Site Impacts

Surface coating facilities are major sources of volatile organic compounds (VOCs) in urban areas. These VOCs can contribute to ground-level ozone formation, and many are hazardous air pollutants (HAPs), including xylene, ethylbenzene, and toluene. This project was conducted in order to provide information for updating the Texas Commission on Environmental Quality (TCEQ), USA, permit by rule for Surface Coating Facilities. Project objectives were: 1) To develop a database of information regarding surface coating facilities in Texas;2) To estimate maximum emission rates for various VOC species from surface coating facilities in Texas;3) To conduct dispersion modeling to estimate off-site impacts from surface coating facilities. The database was developed using 286 TCEQ permit files authorizing surface coating facilities in Texas during 2006 and 2007. The database was designed to include information important for estimating emission rates, and for using as inputs to the dispersion model. Hourly and annual emissions of volatile organic compounds (VOCs), particulate matter (PM), and exempt solvents (ES) were calculated for each permitted entity/ company in the database, according to equations given by TCEQ. Dispersion modeling was then conducted for 3 facility configurations (worst-case stack height, good practice stack height, and fugitive emissions), for urban and rural dispersion parameters, for 8-hour and 24-hour operating scenarios, and for 1-hour, 24-hour, and annual averaging times, for a total of 36 scenarios. The highest modeled concentrations were for the worst-case stack height, rural dispersion parameters, 24-hour operation scenario, and 1-hour averaging time. 108 specific chemical species, which are components of surface coatings, were identified as candidates for further health impacts review.

The Volatilization of Pollutants from Soil and Groundwater: Its Importance in Assessing Risk for Human Health for a Real Contaminated Site

Pollution of different elements (air, water, soil and subsoil) resulting both from accidental events and from ordinary industrial and civil activities causes negative effects on the human health and on the environment. The present paper examines the analysis of a contaminated site, focusing the attention on the negative effects for receptors exposed to soil and groundwater contamination caused by industrial activities. The case study investigated is a contaminated area located in the industrial district of Trento North once occupied by the Italian Carbochimica plant. Pollution in that area is mainly due to contamination of soil and groundwater with polycyclic aromatic hydrocarbons. The methodology applied is the risk evaluation for human health, in terms of individual cancer risk and hazard index. In particular the attention has been focused on a specific migration way: if pollutants in the soil or in the groundwater undergo a phase change, they spread and get to the soil surface, causing a dispersion of vapors in the atmosphere. In this case risk assessment calls for the evaluation of volatilization factor. Among the different models dealing with the estimation of volatilization factor, those mostly known and used in the national and international field of Human Health Risk Assessment were chosen: Jury’s and Farmer’s models. A sensitivity analysis of models was performed, in order to identify the most significant parameters to estimate the volatilization factors among the wide range of input parameters for the application of models. Performing an accurate selection and data processing of the contaminated site, models for the volatilization factors calculation are applied, thus evaluating air concentrations and Human Health Risk. The analysis of the resulting estimates is an excellent aid to draw interesting conclusions and to verify if the soil and groundwater pollutants volatilization affects the human health considerably.

Some Explicit Formulae for the Hull and White Stochastic Volatility Model

An explicit formula for the transition probability density function of the Hull and White stochastic volatility model in presence of nonzero correlation between the stochastic differentials of the Wiener processes on the right hand side of the model equations is presented. This formula gives the transition probability density function as a two dimensional integral of an explicitly known integrand. Previously an explicit formula for this probability density function was known only in the case of zero correlation. In the case of nonzero correlation from the formula for the transition probability density function we deduce formulae (expressed by integrals) for the price of European call and put options and closed form formulae (that do not involve integrals) for the moments of the asset price logarithm. These formulae are based on recent results on the Whittaker functions [1] and generalize similar formulae for the SABR and multiscale SABR models [2]. Using the option pricing formulae derived and the least squares method a calibration problem for the Hull and White model is formulated and solved numerically. The calibration problem uses as data a set of option prices. Experiments with real data are presented. The real data studied are those belonging to a time series of the USA S&P 500 index and of the prices of its European call and put options. The quality of the model and of the calibration procedure is established comparing the forecast option prices obtained using the calibrated model with the option prices actually observed in the financial market. The website: http://www.econ.univpm.it/recchioni/finance/w17 contains some auxiliary material including animations and interactive applications that helps the understanding of this paper. More general references to the work of the authors and of their coauthors in mathematical finance are available in the website: http://www.econ.univpm.it/recchioni/finance.

The Calibration of Some Stochastic Volatility Models Used in Mathematical Finance

Stochastic volatility models are used in mathematical finance to describe the dynamics of asset prices. In these models, the asset price is modeled as a stochastic process depending on time implicitly defined by a stochastic differential Equation. The volatility of the asset price itself is modeled as a stochastic process depending on time whose dynamics is described by a stochastic differential Equation. The stochastic differential Equations for the asset price and for the volatility are coupled and together with the necessary initial conditions and correlation assumptions constitute the model. Note that the stochastic volatility is not observable in the financial markets. In order to use these models, for example, to evaluate prices of derivatives on the asset or to forecast asset prices, it is necessary to calibrate them. That is, it is necessary to estimate starting from a set of data the values of the initial volatility and of the unknown parameters that appear in the asset price/volatility dynamic Equations. These data usually are observations of the asset prices and/or of the prices of derivatives on the asset at some known times. We analyze some stochastic volatility models summarizing merits and weaknesses of each of them. We point out that these models are examples of stochastic state space models and present the main techniques used to calibrate them. A calibration problem for the Heston model is solved using the maximum likelihood method. Some numerical experiments about the calibration of the Heston model involving synthetic and real data are presented.

Volatility Risk Management of Chinese Stock Grading Market——An Empirical Study of GARCH-VaR Model

This paper mainly through the comparison of GARCH-VaR China stock market board,small board and gem in the United States correspond to the three stock index volatility,volatility between stock indexes obtained U.S.stock market volatility risk multi-level market differences.As a suggestion and reference for investors,it can also provide reference for the supervision department of stock market risk.Based on the empirical research,analyzes the advantages and disadvantages of traditional risk measurement methods,and combined with GARCH model with high degree of complexity and the practice effect analysis,trying to find the objective measure stock model analysis.In the specific study of the volatility of the stock market,through the comparison of China’s three major plates and the market classification mechanism of mature U.S.stock market,combined with the objective situation of the market,draw conclusions and change expectations.From the empirical results,the U.S.stock market has recovered after the financial crisis,and its performance on risk volatility is better than China’s three major plates.From the comparison of the stock market in the same country,the small and medium-sized plates tend to have greater risks,while the risks of the main board and the gem have the characteristics of low average value but frequent fluctuations.

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

In this study,we proposed a new model to improve the accuracy of fore-casting the stock market volatility pattern.The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange(Tada-wul).The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations.The proposed forecasting model combines the best maximum overlapping discrete wavelet transform(MODWT)function(Bl14)and exponential generalized autoregressive conditional heteroscedasticity(EGARCH)model.The results show the model's ability to analyze stock market data,highlight important events that contain the most volatile data,and improve forecast accuracy.The results were compared from a number of mathematical mod-els,which are the non-linear spectral model,autoregressive integrated moving aver-age(ARIMA)model and EGARCH model.The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error(MAPE),Mean absolute scaled error(MASE)and Root means squared error(RMSE).

Exploring Apple’s Stock Price Volatility Using Five GARCH Models

The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related fields.This paper evaluates the volatility of Apple Inc.(AAPL)returns using five generalized autoregressive conditional heteroskedasticity(GARCH)models:sGARCH with constant mean,GARCH with sstd,GJR-GARCH,AR(1)GJR-GARCH,and GJR-GARCH in mean.The distribution of AAPL’s closing price and earnings data was analyzed,and skewed student t-distribution(sstd)and normal distribution(norm)were used to further compare the data distribution of the five models and capture the shape,skewness,and loglikelihood in Model 4-AR(1)GJR-GARCH.Through further analysis,the results showed that Model 4,AR(1)GJR-GARCH,is the optimal model to describe the volatility of the return series of AAPL.The analysis of the research process is both,a process of exploration and reflection.By analyzing the stock price of AAPL,we reflect on the shortcomings of previous analysis methods,clarify the purpose of the experiment,and identify the optimal analysis model.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

Volatility Prediction via Hybrid LSTM Models with GARCH Type Parameters

Since the establishment of financial models for risk prediction,the measurement of volatility at risky market has improved,and its significance has also grown.For high-frequency financial data,the degree of investment risk,which has always been the focus of attention,is measured by the variance of residual sequence obtained following model regression.By integrating the long short-term memory(LSTM)model with multiple generalized autoregressive conditional heteroscedasticity(GARCH)models,a new hybrid LSTM model is used to predict stock price volatility.In this paper,three GARCH models are used,and the model that can best fit the data is determined.