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An Improved Whale Optimization Algorithm for Global Optimization and realized volatility Prediction

Computers, Materials & Continua 2023年第12期77卷 2935-2969页

作者： Xiang Wang Liangsa Wang Han Li Yibin Guo School of Civil Engineering and Architecture Zhengzhou University of AeronauticsZhengzhou450015China School of Civil and Architectural Engineering Jiangsu University of Science and TechnologyZhenjiang212003China

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

关键词： Whale optimization algorithm chaos mechanism opposition-based learning long short-term memory realized volatility

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

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Financial Innovation 2021年第1期7卷 154-184页

作者： Wei Zhang Kai Yan Dehua Shen College of Management and Economics Tianjin UniversityNo.92 Weijin RoadNankai DistrictTianjin 300072China

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 ***,the predictability of the Baidu Index is found to rise as the forecasting horizon *** 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 *** findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

关键词： realized volatility HAR model Baidu Index Chinese stock market

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Empirical Evidence of the Leverage Effect in a Stochastic volatility Model： A realized volatility Approach

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Frontiers of Economics in China-Selected Publications from Chinese Universities 2012年第1期7卷 22-43页

作者： Dinghai Xu Yuying Li Department of Economics University of Waterloo Waterloo ON N2L 3G1 Canada School of Computer Science University of Waterloo Waterloo ON N2L 3G1 Canada

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.

关键词： realized volatility stochastic volatility model leverage effect high frequency data MLE trust-region method

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

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Financial Innovation 2023年第1期9卷 645-666页

作者： Rangan Gupta Christian Pierdzioch Department of Economics University of PretoriaPretoria 0002South Africa Department of Economics Helmut Schmidt UniversityHolstenhofweg 85P.O.B.70082222008 HamburgGermany

Because the *** 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 *** address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)*** estimate the models,we use quantile-regression and quantile machine learning(Lasso)*** estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized *** weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

关键词： Oil price realized volatility Economic conditions indexes Quantile Lasso Prediction models

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A hybrid econometrics and machine learning based modeling of realized volatility of natural gas

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Financial Innovation 2024年第1期10卷 2956-2987页

作者： Werner Kristjanpoller Departamento de Industrias Universidad Técnica Federico Santa MaríaAv.España 1680ValparaísoChile

Determining which variables affect price realized volatility has always been *** paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day *** methodological proposal is based on using the best econometric and machine learning models to forecast realized *** particular,the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index,euro-US dollar exchange rate,price of gold,and price of Brent crude oil on the realized volatility of natural *** financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed;the euro-US dollar exchange rate was the primary financial asset and explained 40.1% of the *** results of the proposed daily analysis differed from those of the methodology used to study the entire *** traditional model,which studies the entire period,cannot determine temporal effects,whereas the proposed methodology *** proposed methodology allows us to distinguish the effects for each day,week,or month rather than averages for entire periods,with the flexibility to analyze different frequencies and *** methodological capability is key to analyzing influences and making decisions about realized volatility.

关键词： Deep learning Heterogeneous autoregressive model Long short-term memory model realized volatility volatility forecasting framework

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Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

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Journal of Management Science and Engineering 2023年第2期8卷 191-213页

作者： Xu Gong Boqiang Lin School of Management China Institute for Studies in Energy PolicyXiamen UniversityXiamen361005China

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on *** day-of-the-week effects,structural breaks,or both,we propose three classes of HAR models to forecast electricity volatility based on existing HAR *** estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon,whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily,weekly,and monthly *** out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility,and in most cases,dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week *** out-of-sample results were robust across three different *** importantly,we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

关键词： Day-of-the-weekeffects Structural breaks volatility forecasting realized volatility Electricitymarket

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

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Financial Innovation 2022年第1期8卷 2107-2136页

作者： Yang Gao Chengjie Zhao Bianxia Sun Wandi Zhao School of Economics and Management Beijing University of TechnologyNo.100 PingleyuanChaoyang DistrictBeijing 100124People’s Republic of China Department of Finance Southern University of Science and TechnologyRoom 3#317Wisdom ValleyNo.1088 Xueyuan Rd.Nanshan DistrictShenzhen 518055People’s Republic of China School of Statistics Capital University of Economics and BusinessFengtai DistrictBeijing 100070People’s Republic of China

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial *** the proposal of China’s"dual carbon"target,green stocks have gradually become an essential branch of Chinese stock *** 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,*** Internet sentiment is based on posts from Eastmoney Guba,and the trading sentiment comes from a variety of trading *** addition,we divide the realized volatility into continuous and jump parts,and then investigate the effects of investor sentiment on different types of *** empirical findings show that both sentiment indices impose significant positive impacts on realized,continuous,and jump volatilities,where trading sentiment is the main *** 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 *** is evidenced that investor sentiments are positively correlated with the VPIN,and they can affect volatilities through the *** then divide the total sample around the coronavirus disease 2019(COVID-19)*** empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments,especially to Internet *** study is of great significance for maintaining the stability of green stock markets and reducing market volatility.

关键词： Internet sentiment Trading sentiment realized volatility Mediating effect

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The role of oil futures intraday information on predicting US stock market volatility

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Journal of Management Science and Engineering 2021年第1期6卷 64-74页

作者： Yusui Tang Xiao Xiao M.I.M.Wahab Feng Ma School of Economics&Management Southwest Jiaotong UniversityChengduChina Department of Mechanical and Industrial Engineering Ryerson UniversityTorontoCanada

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR ***-sample results indicate that oil futures intraday information is helpful to increase the ***,compared to the benchmark model,the proposed models improve their predictive ability with the help of oil futures realized *** particular,the multivariate HAR model outperforms the univariate ***,considering the contemporaneous connection is useful to predict the US stock market ***,these findings are consistent across a variety of robust checks.

关键词： volatility forecasting The US stock Market Oil market volatility realized volatility DCC model

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The Quadratic-Form Representation of the Pre-Averaging Estimator

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Journal of Statistical Science and Application 2014年第4期2卷 142-147页

作者： Selma Chaker Bank of Canada

volatility forecasts are central to many financial issues, including empirical asset pricing finance and risk management. In this paper, I derive a new quadratic-form representation of the pre-averaging volatility est... 详细信息

关键词： realized volatility market microstructure noise eigenfunction stochastic volatility models Mincer-Zamowitz regression.

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中国金融市场波动率模型预测能力比较研究

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统计与精算 2009年第6期 20-26页

作者：殷炼乾邵锡栋

使用高频数据构造的实现类波动率估计量已经越来越多的用于波动率模型预测精度的衡量与比较。本文采用来自沪深两市的主要股指日内高频数据，构建了两类日内波动率的非参估计量实现波动RV与实现极差RR，分别用于GARCH和CARR类模型的比... 详细信息

使用高频数据构造的实现类波动率估计量已经越来越多的用于波动率模型预测精度的衡量与比较。本文采用来自沪深两市的主要股指日内高频数据，构建了两类日内波动率的非参估计量实现波动RV与实现极差RR，分别用于GARCH和CARR类模型的比较与扩展；并使用Mincer-Zarnowitz(MZ)回归方程和基于此基础之上的一个渐进正态检验统计量对各模型的相对优劣及统计显著性进行对比研究。结果显示，标准CARR表现最好，而GARCH类扩展以及CARR类扩展模型均未能显著提升模型的预测能力，从实证上说明了CARR模型使用每日价格极差信息对波动率建模是充分有效的。

关键词：实现极差实现波动 CARR GARCH 高频数据 realized range realized volatility CARR GARCH high frequency data

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