Realised volatility prediction of high-frequency data with jumps based on machine learning

Abstract

Asset price jumps are very common in financial markets, and they are essential to accurately predict volatility. This article focuses on 50 randomly selected stocks from the Chinese stock market, utilising high-frequency data to construct two jump models, the heterogeneous autoregressive quarticity jump model (HARQ-J) and the full heterogeneous autoregressive quarticity jump model (HARQ-F-J), which take into account jump variables based on existing models (HARQ and HARQ-F). To further enhance the accuracy of our volatility forecasts, the study combines the newly constructed models with the machine learning (ML) to form a hybrid model. Finally, the empirical research shows that the new hybrid model performs better than existing traditional prediction methods. In particular, the long- and short-term memory (LSTM) function is significantly better than other machine learning functions. Among all the LSTM models tested by the model confidence set (MCS), the HARQ-F-J-LSTM model has the highest prediction accuracy, followed by the HARQ-J-LSTM model.

Keywords:

• Realised volatility
• jump
• neural network
• MCS test

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 since there is noise in the price itself, the shorter the data interval, the more unreliable the estimated value of the Volatility Prediction, which will lead to a large accuracy error. According to the literature studies, Yong (2012) and Wuyi and Baiqi (2012).

2 50 stocks in Shanghai and Shenzhen Stock Exchange: group A: Shanxi coking coal, Shenzhen energy, Desai battery, Yantian port, Zoomlion Heavy Technology, Financial Street, Tongcheng Holdings, Dong'e Ejiao, Huatian Hotel, Jiangling Automobile, Taishan Petroleum, Fiberhome Electronics, Su Changchai a, Guofeng new materials, Jilin Aodong, SHUNFA Hengye, Yanjing Beer, Sichuan Meifeng, Guangfa Securities, ultrasonic electronics, Luxi Chemical, Zhangyu a, Weichai heavy machinery, Emeishan a, Jinling pharmaceutical Hisense home appliances, China Resources Sanjiu, Siyuan electric, Huafu fashion, Huate dyne, Ping An Bank, Shenzhen property, Liugong, Shantui shares, Zhenghong technology; Group B: Angang Steel, Zhongcheng, Hunan investment, yuehongyuan a, Gujing gongjiu, Xingrong environment, Jinkong electric power, Tieling new city, Shandong Haihua, Chengde Lulu, Changyuan electric power, Denghai seed industry, state machinery Seiko, Hainan expressway, and China nuclear technology.

Additional information

Funding

The project was supported by National Natural Science Foundation of China [grant numbers 71901118, 11901289].