Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning
This paper models stochastic process of price time series of $ CSI $ $ 300 $ index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorff-Nielsen and Shephard model, the lag caused by asynchrony of market information a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | Electronic Research Archive |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023070?viewType=HTML |
| _version_ | 1797833677503201280 |
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| author | Xianfei Hui Baiqing Sun Indranil SenGupta Yan Zhou Hui Jiang |
| author_facet | Xianfei Hui Baiqing Sun Indranil SenGupta Yan Zhou Hui Jiang |
| author_sort | Xianfei Hui |
| collection | DOAJ |
| description | This paper models stochastic process of price time series of $ CSI $ $ 300 $ index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorff-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 different magnitudes offers 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. |
| first_indexed | 2024-04-09T14:26:59Z |
| format | Article |
| id | doaj.art-3b7f99a3c41845c68735c824c06d597c |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-04-09T14:26:59Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-3b7f99a3c41845c68735c824c06d597c2023-05-04T01:42:08ZengAIMS PressElectronic Research Archive2688-15942023-01-013131365138610.3934/era.2023070Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learningXianfei Hui0Baiqing Sun 1Indranil SenGupta2Yan Zhou3Hui Jiang 41. School of Management, Harbin Institute of Technology, Harbin 150001, China1. School of Management, Harbin Institute of Technology, Harbin 150001, China2. Department of Mathematics, North Dakota State University, Fargo ND 58108-6050, USA1. School of Management, Harbin Institute of Technology, Harbin 150001, China3. College of Management and Economics, Tianjin University, Tianjin 300072, ChinaThis paper models stochastic process of price time series of $ CSI $ $ 300 $ index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorff-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 different magnitudes offers 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.https://www.aimspress.com/article/doi/10.3934/era.2023070?viewType=HTMLstochastic volatility modelingjumplévy processhigh-frequency datamachine learning and deep learning |
| spellingShingle | Xianfei Hui Baiqing Sun Indranil SenGupta Yan Zhou Hui Jiang Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning Electronic Research Archive stochastic volatility modeling jump lévy process high-frequency data machine learning and deep learning |
| title | Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning |
| title_full | Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning |
| title_fullStr | Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning |
| title_full_unstemmed | Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning |
| title_short | Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning |
| title_sort | stochastic volatility modeling of high frequency csi 300 index and dynamic jump prediction driven by machine learning |
| topic | stochastic volatility modeling jump lévy process high-frequency data machine learning and deep learning |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023070?viewType=HTML |
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