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.

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。

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。

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。

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.

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.

基于厚尾分布下Realized GARCH模型的中国股票市场波动研究

金融模型的正确选择是估计收益序列的分布和波动率至关重要的一步.本文采用误差项服从正态分布、t分布、偏t分布、NIG分布的Realized GARCH模型,拟合上证综指的收益率分布和波动率,并与误差项服从正态分布、t分布、偏t分布、NIG分布的GARCH模型进行对比,证明厚尾分布下的Realized GARCH模型能够更为精确地描述中国股市的波动性.

Some New Estimators of Integrated Volatility

We develop higher order accurate estimators of integrated volatility in a stochastic volatility models by using kernel smoothing method and using different weights to kernels. The weights have some relationship to moment problem. As the bandwidth of the kernel vanishes, an estimator of the instantaneous stochastic volatility is obtained. We also develop some new estimators based on smoothing splines.

Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model： A Realized Volatility Approach

Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.

Realized volatility forecast of financial futures using timevarying HAR latent factor models

We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factors is determined using Chan and Grant's(2016)deviation information criteria.The predictors in our model include lagged daily,weekly,and monthly volatility variables,the corresponding volatility factors,and a speculation variable.In addition,the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models,including size,inclusion probabilities,and coefficients,are examined.We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts.Furthermore,the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.

面向深度行情因子挖掘的分布式训练关键技术研究

深度行情数据是沪深交易所的新一代实时行情数据产品,是普通基础行情数据的升级版,是目前国内信息密度最高、蕴含信息量最大、挖掘最不充分的行情数据,对挖掘证券市场潜在风险具有重要价值。但是,现有研究缺少基于深度行情数据面向证券市场的风险度量和计算分析,且全市场深度行情数据规模大,用于提取信息的深度学习模型也越来越复杂,尽管当下硬件的计算能力也在一直不断地发展与提高,但仍然无法解决训练耗时长、效率低等问题。因此,基于沪深300成分股深度行情数据,利用深度学习等方法挖掘高频波动率因子,构建了基于TabNet与LightGBM的高频波动率预测模型。同时,提出了一种基于并行差分进化的分布式训练算法Parallel_DE,用于模型分布式训练过程中的参数计算,并详细阐述了其场景映射方案与整体流程设计。针对上述2项工作基于自有分布式训练平台进行充分验证,实验结果表明,高频波动率预测模型可以对已实现波动率进行高精度预测,且效果相较于其他方法具有一定优越性;Parallel_DE算法可以在一定程度保留参数多样性的同时,有效减少本地参数在测试集上的误差,从而高效率分布式地训练出性能优良的深度学习模型,为证券市场的风险识别提供了面向深度行情数据的相关技术与方法。

基于赋权修正测度的时变参数Realized HAR GARCH模型及其实证研究

基于高频数据视角,文章提出时变参数(TV)Realized HAR GARCH混合预测模型,同时考虑资产波动率的聚集性、长记忆以及由测量误差引起的参数衰减偏差效应.进一步,为充分利用价格信息并提升估计效率,本文基于日内“OHLC”数据构建赋权修正已实现信息波动率(WRIV),并将其用于驱动条件方差的动态变化.在偏t分布假设下,以沪深300指数为样本探究中国股票市场的波动性规律,并在实证中评估所提模型在收益率拟合、波动率预测以及风险度量上的能力.结果显示:中国股票市场的收益波动存在明显的异质性与长记忆特征,TV-Realized HAR GARCH能够充分捕捉指数波动率的动态变化,在样本内拟合效果和样本外波动率与风险预测准确性上均能显现出优势,且WRIV测度的引入能显著提升模型的预测精度,凸显出日内高频数据信息的充分利用对于波动率刻画与风险测度的重要性,综合而言,TV-Realized HAR GARCH(WRIV)模型具有最优的整体实证表现.

无模型隐含波动率的信息含量与定价能力——基于上证50ETF期权的实证研究

本文从上证50ETF期权价格中提取无模型隐含波动率并检验其信息含量,基于随机折现因子理论推导波动率风险的系统性与正负性判定公式,从波动率风险溢酬和相关性两方面验证波动率是否为系统性风险,进而基于A股市场的个股数据检验波动率风险在股票截面收益上的定价能力。研究结果表明:无模型隐含波动率包含BS隐含波动率中的所有信息和历史波动率中的大部分信息,是未来已实现波动率的有效估计;市场波动率为系统性风险因子且存在显著为负的风险溢酬;组合分析表明,对市场波动率暴露较大的股票组合在未来的收益较低,且暴露最大与最小股票组合的收益率之差显著为负,该结论在控制经典风险因子和改变交易策略之后依然稳健;Fama-MacBeth两步法结果表明波动率风险被定价且风险价格显著为负。

中国原油期货价格波动时段特征分析及预测

关于原油期货价格波动率的预测研究主要集中在国外市场,中国原油期货合约在一个交易日内被分为3个交易时段,这与国外市场有着很大的不同。交易时间分段可能使中国市场上波动率的结构与国外存在差异。通过异质自回归已实现波动率(HAR-RV)模型框架与半秒钟采样频率的高频期货合约交易数据,对价格波动率的结构特征及预测问题进行研究。研究验证了中国市场上预测的时间尺度由日缩小到交易时段尺度的可行性,发现了中国原油期货价格波动率有时段波动的特征,且时段特征的加入显著提高模型的预测性能。此外,研究还发现,预测时间尺度的缩小促进已实现波动序列平稳性的改善,发展了非平稳时序下HAR-RV模型研究问题,波动率的预测结果可为投资者和管理者对中国原油期货市场设计出更为精准的风险管理工具。

高频数据下投资组合风险预测模型比较

“已实现”协方差矩阵是对投资组合波动性及相关性的一种全新的度量方法。系统介绍基于高频交易数据的“已实现”波动率及由它拓展而来的“已实现”协方差矩阵。利用样本数据对模型进行检验,并比较分析该方法与DCC-GARCH方法的优劣。对比结果说明,这种基于高频交易数据的多元RV估计方法在估计精度和计算简便程度上明显优于DCC-GARCH方法。

隔夜收益率能提高高频波动率模型的预测能力吗

将隔夜收益率作为解释变量加入到高频波动率模型中,研究其对模型预测精度的影响.以沪深300指数为例,运用样本外预测技术及新颖的模型可信集检验方法,同时选取比RV更为稳健的两尺度已实现波动率为基准波动率,实证研究表明,隔夜收益率对短期波动率存在显著的非对称效应;隔夜收益率能改善各波动率模型的拟合能力,并能显著提高模型的短期预测能力;在预测短、中及长期波动率时,已实现和已实现极差高频波动率模型的预测表现并不一致.