Dynamic relationship between volume and volatility in the Chinese stock market:evidence from the MS-VAR model

Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This study examines the dynamic,nonlinear correlations between Chinese stock volatility,trading volume,and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model(MS-VAR).The empirical findings are as follows:(1)The Chinese stock market can be divided into three regional systems:steady downward,steady upward,and high volatility.The three states have similar frequencies of occurrence,and their corresponding stable probabilities are not high,indicating that the Chinese stock market is unstable.(2)Asymmetric dynamic relationships exist between market volatility,investment return,and trading volume.For different regimes,while the effect of trading volume on volatility and return appears to be insignificant,the impacts of volatility and return on trading volume are considerably strong.(3)A regime-dependent,contemporaneous correlation between volatility and return is observed,which also reflects the behavior of the Chinese stock market“chasing up and down”.However,a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes,indicating that uncertainty in the Chinese stock market is closely related to information inflow.

Dependencies between price duration, volatility, volume and return on the Warsaw Stock Exchange

The successive changes of asset prices are the most visible manifestation of financial markets dynamics. There exist different views about factors generating these changes, but many researchers and practitioners agree that the most important among them is the impact of information flow. According to the market microstructure theories, it depends mainly on the behavior of informed and uniformed traders. In the paper, we investigate dependencies between the possible proxies of information process： price duration and corresponding to it volume change and return. Our main objective is to answer the question about the most important factor in the process of discovering information by uniformed traders. We apply a set of models for volatility, volume and duration data. Our analysis is performed for selected equities listed on the Warsaw Stock Exchange and uses tick-by-tick data. The obtained results show that the stock liquidity on this leading stock market in Central and Eastern Europe is the most important factor influencing the process of discovering information by uninformed traders.

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.

Applications of nonferrous metal price volatility to prediction of China's stock market

The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to December of 2011, empirical results show that the price volatility of basic nonferrous metals is a good predictor of value-weighted stock portfolio at various horizons in both in-sample and out-of-sample regressions. The predictive power of metal copper volatility is greater than that of aluminum. The results are robust to alternative measurements of variables and econometric approaches. After controlling several well-known macro pricing variables, the predictive power of copper volatility declines but remains statistically significant. Since the predictability exists only during our sample period, we conjecture that the stock market predictability by metal price volatility is partly driven by commodity financialization.

基于Volatility的内存信息调查方法研究

随着反取证技术的发展,调查人员越来越难于在磁盘介质中寻找到有价值的证据或线索。针对内存信息的调查分析研究由此成为计算机法庭科学领域日益关注的焦点。通过以内存调查取证开源软件Volatility为背景,从进程及DLL、内存及VAD、驱动程序及内核对象、网络连接与注册表等多个角度描述内存信息的调查方法,并结合实例说明所述方法在实际工作中的具体应用。

Effects of ultrasonic-assisted extraction on bioactive compounds,volatile flavors and antioxidant activities of vine tea water extracts

Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigates the influence of different extraction methods(room temperature water extraction,boiling water extraction,ultrasonic-assisted room temperature water extraction,and ultrasonic-assisted boiling water extraction,referred to as room temperature water extraction(RE),boiling water extraction(BE),ultrasonic assistance at room temperature water extraction(URE),and ultrasonic assistance in boiling water extraction(UBE))on the yield,dihydromyricetin(DMY)content,free amino acid composition,volatile aroma components,and antioxidant properties of vine tea extracts.Results:A notable influence of extraction temperature on the yield of vine tea extracts(P<0.05),with BE yielding the highest at 43.13±0.26%,higher than that of RE(34.29±0.81%).Ultrasound-assisted extraction significantly increased the DMY content of the extracts(P<0.05),whereas DMY content in the RE extracts was 59.94±1.70%,that of URE reached 66.14±2.78%.Analysis revealed 17 amino acids,with L-serine and aspartic acid being the most abundant in the extracts,nevertheless ultrasound-assisted extraction reduced total free amino acid content.Gas chromatography-mass spectrometry analysis demonstrated an increase in the diversity and quantity of compounds in the vine tea water extracts obtained through ultrasonic-assisted extraction.Specifically,69 and 68 volatile compounds were found in URE and UBE extracts,which were higher than the number found in RE and BE extracts.In vitro,antioxidant activity assessments revealed varying antioxidant capacities among different extraction methods,with RE exhibiting the highest DPPH scavenging rate,URE leading in ABTS•+free radical scavenging,and BE demonstrating superior ferric ion reducing antioxidant activity.Conclusion:The findings suggest that extraction methods significantly influence the chemical composition and antioxidant properties of vine tea extracts.Ultrasonic-assisted extraction proved instrumental in elevating the DMY content in vine tea extracts,thereby enriching its flavor profile while maintaining its antioxidant properties.

Understanding the influence of microwave on the relative volatility used in the pyrolysis of Indonesia oil sands

In this paper, pyrolysis of Indonesian oil sands （lOS） was investigated by two different heating methods to develop a better understanding of the microwave-assisted pyrolysis. Thermogravimetric analysis was conducted to study the thermal decomposition behaviors of lOS, showing that 550 ℃ might be the pyrolysis final temperature. A explanation of the heat-mass transfer process was presented to demonstrate the influence of mi- crowave-assisted pyrolysis on the liquid product distribution. The heat-mass transfer model was also useful to explain the increase of liquid product yield and heavy component content at the same heating rate by two differ- ent heating methods. Experiments were carried out using a fixed bed reactor with and without the microwave irradiation. The results showed that liquid product yield was increased during microwave induced pyrolysis, while the formation of gas and solid residue was reduced in comparison with the conventional pyrolysis. Moreover, the liquid product characterization by elemental analysis and GC-MS indicated the significant effect on the liquid chemical composition by microwave irradiation. High polarity substances （ε 〉 10 at 25 ℃）, such as oxy- organics were increased, while relatively low polarity substances （ε 〈 2 at 25℃）, such as aliphatic hydrocarbons were decreased, suggesting that microwave enhanced the relative volatility of high polarity substances. The yield improvement and compositional variations in the liquid product promoted by the microwave-assisted pyrolysis deserve the further exploitation in the future,

Effect of occurrence mode of heavy metal elements in a low rank coal on volatility during pyrolysis

The harmful trace elements will be released during coal utilization, which can cause environment pollution and further endangering human health, especially for heavy metal elements. Compared to combustion, the release of heavy metal elements during coal pyrolysis process, as a critical initial reaction stage of combustion, has not received sufficient attention. In the present paper, a low rank coal, from Xinjiang province in China, was pyrolyzed in a fixed bed reactor from room temperature, at atmospheric pressure, with the heating rate of 10 °C/min, and the final pyrolysis temperature was from 400 to 800℃ with the interval of 100℃. The volatility of heavy metal elements (including As, Hg, Cd and Pb) during pyrolysis process was investigated. The results showed the volatility of all heavy metal elements increased obviously with increasing temperature, and followed the sequence as Hg > Cd > As > Pb, which was mainly caused by their thermodynamic property and occurrence modes in coal. The occurrence modes of heavy metals were studied by sink-andfloat test and sequential chemical extraction procedure, and it can be found that the heavy metal elements were mainly in the organic and residual states (clay minerals) in the raw coal. And most of the organic heavy metals escaped during the pyrolysis process, the remaining elements were mainly in the residual state, and the elements in Fe-Mn state also tended to remain in the char.

Quantitative method for evaluating detailed volatility of wind power at multiple temporal-spatial scales

With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.

Characterization of Organic Aerosol at a Rural Site in the North China Plain Region:Sources,Volatility and Organonitrates

The North China Plain(NCP)is a region that experiences serious aerosol pollution.A number of studies have focused on aerosol pollution in urban areas in the NCP region;however,research on characterizing aerosols in rural NCP areas is comparatively limited.In this study,we deployed a TD-HR-AMS(thermodenuder high-resolution aerosol mass spectrometer)system at a rural site in the NCP region in summer 2013 to characterize the chemical compositions and volatility of submicron aerosols(PM_(1)).The average PM_(1)mass concentration was 51.2±48.0μg m^(−3) and organic aerosol(OA)contributed most(35.4%)to PM_(1).Positive matrix factorization(PMF)analysis of OA measurements identified four OA factors,including hydrocarbon-like OA(HOA,accounting for 18.4%),biomass burning OA(BBOA,29.4%),lessoxidized oxygenated OA(LO-OOA,30.8%)and more-oxidized oxygenated OA(MO-OOA,21.4%).The volatility sequence of the OA factors was HOA>BBOA>LO-OOA>MO-OOA,consistent with their oxygen-to-carbon(O:C)ratios.Additionally,the mean concentration of organonitrates(ON)was 1.48−3.39μg m−3,contributing 8.1%-19%of OA based on cross validation of two estimation methods with the high-resolution time-of-flight aerosol mass spectrometer(HRToF-AMS)measurement.Correlation analysis shows that ON were more correlated with BBOA and black carbon emitted from biomass burning but poorly correlated with LO-OOA.Also,volatility analysis for ON further confirmed that particulate ON formation might be closely associated with primary emissions in rural NCP areas.

Can the Baidu Index predict realized volatility in the Chinese stock market?

This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting

This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models:sGARCH,girGARCH,eGARCH,iGARCH,aGARCH,TGARCH,NGARCH,NAGARCH,and AVGARCH along with value at risk estimation and backtesting.We use daily data for Total Nigeria Plc returns for the period January 2,2001 to May 8,2017,and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations.This investigation of the volatility,VaR,and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach.We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable.Additionally,for student t innovation,the sGARCH and girGARCH models failed to converge;the mean reverting number of days for returns differed from model to model.From the analysis of VaR and its backtesting,this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices.Furthermore,risk was reflected by significant up and down movement in the stock price at a 99%confidence level,suggesting that high risk brings a high return.

Dynamics of volatility spillover between stock market and foreign exchange market: evidence from Asian Countries

Background:The purpose of this study is to examine volatility spillover effects between stock market and foreign exchange market in selected Asian countries;Pakistan,India,Sri Lanka,China,Hong Kong and Japan.This study considered daily data from 4th January,1999 to 1st January,2014.Methods:This study opted EGARCH(Exponential Generalized Auto Regressive Conditional Heteroskedasticity)model for the purpose of analyzing asymmetric volatility spillover effects between stock and foreign exchange market.Results:The EGARCH analyses reveal bidirectional asymmetric volatility spillover between stock market and foreign exchange market of Pakistan,China,Hong Kong and Sri Lanka.The results reveal unidirectional transmission of volatility from stock market to foreign exchange market of India.The analysis reveals no evidence of volatility transmission between the two markets in reference to Japan.Conclusions:The result of this study provide valuable insights to economic policy makers for financial stability perspective and to investors regarding decision making in international portfolio and currency risk strategies.

Effects of investor sentiment on stock volatility:new evidences from multi-source data in China’s green stock markets

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry.With the proposal of China’s"dual carbon"target,green stocks have gradually become an essential branch of Chinese stock markets.Focusing on 106 stocks from the new energy,environmental protection,and carbon–neutral sectors,we construct two investor sentiment proxies using Internet text and stock trading data,respectively.The Internet sentiment is based on posts from Eastmoney Guba,and the trading sentiment comes from a variety of trading indicators.In addition,we divide the realized volatility into continuous and jump parts,and then investigate the effects of investor sentiment on different types of volatilities.Our empirical findings show that both sentiment indices impose significant positive impacts on realized,continuous,and jump volatilities,where trading sentiment is the main factor.We further explore the mediating effect of information asymmetry,measured by the volume-synchronized probability of informed trading(VPIN),on the path of investor sentiment affecting stock volatility.It is evidenced that investor sentiments are positively correlated with the VPIN,and they can affect volatilities through the VPIN.We then divide the total sample around the coronavirus disease 2019(COVID-19)pandemic.The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments,especially to Internet sentiment.Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.

Volatility forecasting in Chinese nonferrous metals futures market

This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.

Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models:a comparison based on normal and Student's t-error distribution

Background:Modeling exchange rate volatility has remained crucially important because of its diverse implications.This study aimed to address the issue of error distribution assumption in modeling and forecasting exchange rate volatility between the Bangladeshi taka(BDT)and the US dollar($).Methods:Using daily exchange rates for 7 years(January 1,2008,to April 30,2015),this study attempted to model dynamics following generalized autoregressive conditional heteroscedastic(GARCH),asymmetric power ARCH(APARCH),exponential generalized autoregressive conditional heteroscedstic(EGARCH),threshold generalized autoregressive conditional heteroscedstic(TGARCH),and integrated generalized autoregressive conditional heteroscedstic(IGARCH)processes under both normal and Student’s t-distribution assumptions for errors.Results and Conclusions:It was found that,in contrast with the normal distribution,the application of Student’s t-distribution for errors helped the models satisfy the diagnostic tests and show improved forecasting accuracy.With such error distribution for out-of-sample volatility forecasting,AR(2)–GARCH(1,1)is considered the best.

The protective performance of a molten salt frozen wall in the process of fluoride volatility of uranium

The fluoride volatility method (FVM) is a technique tailored to separate uranium from fuel salt of molten salt reactors. A key challenge in R&D of the FVM is corrosion due to the presence of molten salt and corrosive gases at high temperature. In this work, a frozen-wall technique was proposed to produce a physical barrier between construction materials and corrosive reactants. The protective performance of the frozen wall against molten salt was assessed using FLiNaK molten salt with introduced fluorine gas, which was regarded as a simulation of the FVM process. SS304, SS316L, Inconel 600 and graphite were chosen as the test samples. The extent of corrosion was characterized by an analysis of weight loss and scanning electron microscope studies. All four test samples suffered severe corrosion in the molten salt phase with the corrosion resistance as: Inconel 600>SS316L>graphite>SS304. The presence of the frozen wall could protect materials against corrosion by molten salt and corrosive gases, and compared with materials exposed to molten salt, the corrosion rates of materials protected by the frozen wall were decreased by at least one order of magnitude.

Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH‑MIDAS model

This study investigates the impact of economic policy uncertainty(EPU)on the volatility of European Union(EU)carbon futures prices and whether it has predictive power for the volatility of carbon futures prices.The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance(EUA)futures.We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models.Our empirical results show that the GARCH-MIDAS models,which exhibit superior out-of-sample predictive ability,outperform GARCH-type models.The results also indicate that EPU has noticeable effect on the volatility of EUA futures.Specifically,the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index.Robustness checks further confirm that the EPU index(especially the EPU index of the EU)has strong predictive power for EUA futures prices.Additionally,using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index,investors can construct their portfolios to realize economic returns.

Occurrence and volatility of several trace elements in pulverized coal boiler

The contents of eight trace elements(Mn, Cr, Pb, As, Se, Zn, Cd, Hg) in raw coal, bottom ash and fly ash were measured in a 220 t/h pulverized coal boiler. Factors affecting distribution of trace elements were investigated, including fly ash diameter, furnace temperature, oxygen content and trace elements' characters. One coefficient of Meij was also improved to more directly show element enrichment in combustion products. These elements may be classified into three groups according to their distribution: Group 1: Hg, which is very volatile. Group 2: Pb, Zn, Cd, which are partially volatile. Group 3: Mn, which is hardly volatile. Se may be located between groups 1 and 2 Cr has properties of both group 1 and 3 In addition, the smaller diameter of fly ash, the more relative enrichment of trace elements(except Mn). The fly ash showed different adsorption mechanisms of trace elements and the volatilization of trace elements rises with furnace temperature. Relative enrichments of trace elements(except Mn and Cr) in fly ash are larger than that in bottom ash. Low oxygen content can not always improve the volatilization of trace elements. Pb is easier to form chloride than Cd during coal combustion. Trace elements should be classified in accordance with factors.