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.

Forecasting Realized Volatility Using Subsample Averaging

When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.

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.

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Distribution,abundance,and realized niches of meroplankton by two different mesh size nets during spring 2017 in the Southern Yellow Sea,China

Meroplankton play a crucial role in both benthic and pelagic ecosystems.Existing quantitative research on estimating the quantities of meroplankton groups is both underrepresented and inaccurate.To investigate and evaluate the influence of varying mesh sizes(505 and 160μm)on the sampling efficiency of meroplankton,we conducted an examination using two commonly used plankton nets during the spring season in the Southern Yellow Sea(SYS).Our study revealed a total of 12 meroplankton groups,with 9 groups identified in the 505-μm mesh nets and 11 groups in the 160-μm mesh nets.The results demonstrated the superior collection efficiency of the 160-μm net compared to the 505-μm net across the majority of meroplankton groups.Furthermore,we focused on exploring the abundance,distribution patterns,and realized niches of meroplankton collected by the two mesh size nets,and observed that the distribution of meroplankton closely resembled the distribution of possible benthic adults in the SYS.Correlation analysis of the six dominant groups collected in the 160-μm mesh nets revealed that seawater temperature and salinity emerged as the key environmental factors driving variations in meroplankton abundance within the SYS.This study also found that a smaller mesh size net does not necessarily capture meroplankton more comprehensively.A comprehensive understanding of the ecological characteristics of meroplankton requires the combination of two types of nets for research.Our research significantly advances our understanding of the quantification,abundance,and distribution of meroplankton,serving as a valuable contribution to the broader landscape of detailed quantitative meroplankton studies.

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.

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.

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.

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.

Realized GAS-GARCH及其在VaR预测中的应用

论文提出了新的波动率模型Realized GAS-GARCH,并推导了该模型的QMLE参数估计.该模型结合了Generalized Autoregressive Score(GAS)模型的基本思路,把Realized GARCH模型扩展到包含厚尾分布的情形,并采用了与厚尾分布参数相依的冲击响应函数.与简单的厚尾分布扩展模型相比,这种设定对于回报率中的极端值更加稳健.在基于沪深300指数高频数据的实证结果中,使用GAS冲击响应函数的模型对"在险价值"VaR的预测能力显著的超过了传统的厚尾Realized GARCH模型.

基于Expectile和Realized GARCH模型的波动率预测

Realized GARCH模型是预测波动率的经典模型之一,最小化非对称二次损失函数的Expectile对收益率尾部分布更加敏感,我们在Realized GARCH模型的基础上引入Expectile提出Expectile-Realized GARCH模型。以沪深300指数的高频收益率为例建模分析,对比不同模型下的波动率预测效果,发现Expectile-Realized GARCH模型较Realized GARCH模型对波动率预测能力更好。其中,当风险水平为95%时,对应的Expectile-Realized GARCH波动率预测能力最好。

非对称厚尾分布的混频Realized GARCH模型构建

文章基于日内动量效应的视角,增加日内收益率作为解释变量,并考虑日内交易的交互关系,推广得到结合混合频率的条件均值方程和条件方差方程,构建混频Realized GARCH模型(Mixed Frequency Realized GARCH模型),进一步考虑传统的正态分布假设不能够刻画金融时间序列的非对称性、非正态性、厚尾性等特征,将偏t分布引入混频Realized GARCH模型中,构建了基于偏t分布的混频Realized GARCH模型,推导其参数估计方法,并运用滚动时间窗预测技术和优于SPA检验的MCS检验判别扩展模型对我国黄金期货市场波动的预测结果。由样本内估计结果和MCS检验证实表明:考虑高频信息的Realized GARCH模型在样本内估计和样GARCH模型和Realized GARCH模型有着更高的拟合优度和预测精度,其中基于偏t分布的MF Realized GARCH模型在6种模型中是拥有最佳表现的波动率模型。

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。

Realized GAS-EGARCH模型及其应用

文章扩展了Realized EGARCH模型,即结合Generalized Autoregressive Score(GAS)模型推导不同偏峰厚尾分布下的分布相依冲击响应函数,构建Realized GAS-EGARCH模型,应用于金融资产的波动率与VaR预测,并采用损失函数、MCS检验与VaR后验分析法分别探究新模型对波动率和风险的预测能力。对申万银行指数高频数据的实证结果显示:(1)基于偏峰厚尾分布的Realized GAS-EGARCH模型对波动率的刻画能力优于相应分布的Realized EGARCH模型;(2)损失函数与MCS检验结果显示,条件分布为偏广义误差分布(SGED)的Realized GAS-EGARCH模型对波动率的预测精度最高;(3)对VaR预测序列进行无条件覆盖性检验、独立性检验和条件覆盖性检验分析,认为Realized GAS-EGARCH模型能够有效测度市场风险。

基于异方差和时变波动的Realized HAR GARCH模型研究

文章选取上证综指5分钟收盘价序列高频数据,采用ACF拟合、多种损失函数、SPA检验和VaR回测检验对不同误差分布下的包含时变波动、异方差结构和加权已实现极差的Realized HAR GARCH模型进行研究。实证结果表明,新模型相比于以往模型更能够捕捉上证综指的波动特征,具有更好的波动率拟合和预测效果,且VaR度量效果更优。研究丰富了时变长记忆高频波动率模型,从时变波动和噪声异方差结构视角为投资者和监管机构进行风险管控提供参考。

基于Realized GARCH模型的沪深300指数波动率研究

基于中国沪深300指数,采用5 min高频数据计算已实现极差作为波动率估计量。建立Realized GARCH模型,并假设收益率残差分别服从正态分布和广义双曲线分布。实证结果表明:无论是选择已实现方差还是已实现极差作为已实现测度,服从广义双曲线分布的Realized GARCH模型拟合效果都比服从正态分布的Realized GARCH模型要好。无论残差服从广义双曲线分布还是正态分布,采用已实现极差作为已实现测度的Realized GARCH模型的拟合效果都比采用已实现方差作为已实现测度的Realized GARCH模型要好。另一方面,从似然值提高的程度来看,改变波动率估计量比改变残差分布带来更大的似然值提高,说明选择一个合适的波动率估计量对Realized GARCH模型拟合效果起着至关重要的作用。

基于时变Markov状态转换的RealizedGARCH族模型及其对期货波动率的预测

近年来,Realized GARCH族模型在金融市场波动率研究中展现了良好的预测效果.该文在两个Realized GARCH族模型基础上,考虑波动率存在非线性结构特征,引入基于显著跳跃方差测度的时变Markov状态转换机制以构建时变MRS-Realized GARCH族模型,推导其参数估计方法,并应用DM检验和MCS检验来评估模型的预测精度.最后,分别基于不同的评估方法,误差分布假设,滚动窗口长度,采样区间和跳跃测度对模型进行稳健性检验.以沪深300股指期货数据为例,实证研究表明:沪深300股指期货市场存在高波动和低波动状态,跳跃测度在低波动状态会对未来一期波动产生显著的正向影响,而在高波动状态会抑制未来一期波动;DM检验和MCS检验显示,时变MRS-Realized GARCH族模型在任意的损失函数指标下均具有最佳的波动预测效果;另外,稳健性检验结果证实该模型在不同情况下均具有最佳的预测表现.

高频农产品期货波动率和相关性预测——基于Realized Copula-DCC模型的视角

本文构建了Realized Copula-DCC模型,整合Realized GARCH模型和Copula-DCC模型对农产品期货的波动率和动态相关性进行研究。农产品期货不仅表现出波动聚类现象、偏斜和尖峰厚尾的特征,还呈现出非正态性。基于Skewed-t分布的Realized GARCH模型比其他模型更好地刻画了农产品期货的波动率特征。农产品期货的相关性呈现出动态变化,tCopula-DCC模型比其他时变Copula模型更好地反映了农产品期货相关性的动态演化过程。