[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128447-en":3,"doc-seo-128447-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128447,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning - Research article","This paper develops a stochastic volatility framework for CSI300 index price time series in the Chinese market, focusing on volatility patterns in intraday high-frequency observations. It adopts a generalized Barndorff-Nielsen and Shephard structure that accounts for lag effects from market information asynchrony and microstructure noise, while addressing insufficient long-term dependence. Machine learning and deep learning methods are used to estimate parameters and assess forecasts, with jump tracking supporting simulation of dynamic price behavior and prediction of future jumps. Results indicate robust capture of deterministic components across short and long windows, informing investors and regulators concerned with high-frequency-driven market dynamics.","arXiv :2204 .02891v3 [ q-fin . ST] 3 Jan 2023  \n[http:](http://www.aimspress.com/journal/era)[//](http://www.aimspress.com/journal/era)[www.aimspress.com](http://www.aimspress.com/journal/era)[/](http://www.aimspress.com/journal/era)[journal](http://www.aimspress.com/journal/era)[/](http://www.aimspress.com/journal/era)[era](http://www.aimspress.com/journal/era)  \nERA, x(x): xxx–xxx DOI:  \nReceived:  \nRevised:  \nAccepted:  \nPublished:  \nResearch article  \nStochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning  \nXianfei Hui 1 , Baiqing Sun 1 , Indranil SenGupta2 , Yan Zhou 1; * and Hui Jiang3  \n1 School of Management, Harbin Institute of Technology, Harbin 150001, China  \n2 Department of Mathematics, North Dakota State University, Fargo, ND 58108-6050, USA  \n3 College of Management and Economics, Tianjin University, Tianjin 300072, China  \n* Correspondence: [zyhittxzz@126.com](zyhittxzz@126.com).  \nAbstract: This paper models stochastic process of price time series of CSI300 index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndor􀀋-Nielsen and Shephard model, the lag caused by asynchrony of market information and market microstructure noises are considered, and the problem of lack of long-term dependence is solved. To speed up the valuation process, several machine learning and deep learning algorithms are used to estimate parameter and evaluate forecast results. Tracking historical jumps of di􀀋erent magnitudes o􀀋ers promising avenues for simulating dynamic price processes and predicting future jumps. Numerical results show that the deterministic component of stochastic volatility processes would always be captured over short and longer-term windows. Research finding could be suitable for influence investors and regulators interested in predicting market dynamics based on high-frequency realized volatility.  \nKeywords: Stochastic volatility modeling, Jump, Lvy process, High-frequency data, Machine learning and deep learning  \n1. Introduction  \nAs we all know, financial fluctuations may come not only from the financial system itself, but also from other aspects of social and economic life. For example, COVID-19, has caused frequent and violent fluctuations in global financial markets [1, 11] . In the post-COVID-19 era, a􀀋ected by internal and external factors in the market, the price of financial assets has been unstable during the first half of 2021 . Facing a world with more dynamic economic situation, enterprises and research circles are realising the importance of the challenges and opportunities presented by financial fluctuations. The volatility of financial assets, which is the intensity of changes in the rate of return of financial  \nassets over a period of time, is unobservable [12] . The measurement of volatility, which describes the potential deviation from the expected value, is the core issue in the study of financial volatility. The accurate prediction of financial volatility is the key factor for successful financial asset pricing [17], economic forecasting [10], risk management [4], portfolio optimization [23], and quantitative investment [9] . Volatility Analysis of financial time series is a practical method to study the law of volatility and estimate volatility.  \nAn e􀀋ective way to fit dynamic asset changes is stochastic volatility modeling in the research of financial quantification. There are a lot of derivative pricing models that could be used to model stock prices. He et al. (2021) [19] proposed a new stochastic volatility model to provide a better fit to real data and showed numerically the validity by comparing the results with the Monte Carlo simulation results. He et al. (2021) [18] used the FMLS (finite moment log-stable) model with the stochastic volatility to analyse the e􀀋ect of both jumps and stochastic volatility. The numerical experiments that it is e􀀋ective and converges","cbCaibtTvw00prs2","https://ap.wps.com/l/cbCaibtTvw00prs2","pdf",1950002,1,21,"English","en",105,"# Introduction\n## Stochastic volatility and volatility forecasting\n## High-frequency volatility estimation and microstructure noise\n## Jump modeling and realized measures\n## Barndorff-Nielsen and Shephard framework and challenges","[{\"question\":\"What problem does the paper address regarding CSI300 high-frequency data?\",\"answer\":\"It models the stochastic volatility of CSI300 price time series and analyzes volatility characteristics from intraday high-frequency data, accounting for lag from information asynchrony and microstructure noise.\"},{\"question\":\"How does the proposed model handle dependence and market noise effects?\",\"answer\":\"It uses a generalized Barndorff-Nielsen and Shephard model that considers asynchrony and microstructure noises, and it resolves the lack of long-term dependence.\"},{\"question\":\"How are machine learning methods used in this study?\",\"answer\":\"Several machine learning and deep learning algorithms estimate model parameters and evaluate forecasting performance to accelerate the valuation process and improve prediction quality.\"}]","Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning - Research article | PDF",1786001103,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"stochastic-volatility-modeling-of-high-frequency-csi-300-index-and-dynamic-jump-prediction-driven-by-machine-learning-research-article","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/stochastic-volatility-modeling-of-high-frequency-csi-300-index-and-dynamic-jump-prediction-driven-by-machine-learning-research-article/128447/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address regarding CSI300 high-frequency data?","Question",{"text":76,"@type":77},"It models the stochastic volatility of CSI300 price time series and analyzes volatility characteristics from intraday high-frequency data, accounting for lag from information asynchrony and microstructure noise.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed model handle dependence and market noise effects?",{"text":81,"@type":77},"It uses a generalized Barndorff-Nielsen and Shephard model that considers asynchrony and microstructure noises, and it resolves the lack of long-term dependence.",{"name":83,"@type":74,"acceptedAnswer":84},"How are machine learning methods used in this study?",{"text":85,"@type":77},"Several machine learning and deep learning algorithms estimate model parameters and evaluate forecasting performance to accelerate the valuation process and improve prediction quality.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]