High-frequency pairs trading in Chinese stock market: Based on Lévy-OU processes

被引:0
|
作者
Zhao H. [1 ]
Luo P. [1 ]
Wang S. [2 ]
机构
[1] School of Economics, Xiamen University, Xiamen
[2] Head Office, China Guangfa Bank Co., Ltd, Guangzhou
基金
中国国家自然科学基金;
关键词
heterogeneous autoregressive model; high-frequency pairs trading; Lévy-OU process;
D O I
10.12011/SETP2022-2978
中图分类号
学科分类号
摘要
The paper proposes the LOU-RV-HAR pairs trading strategy by the Lévy process of the double-exponential compound Poisson distribution to capture the jumps of stock pairs spread, the Ornstein-Uhlenbeck (OU) process to characterize the mean-reversion, stock pairs selection with the mean-reversion spread and realized volatility (RV), and the heterogeneous autoregressive (HAR) model to predict the volatility of the spread. The empirical results of the five-minute high-frequency data of the constituent stocks of the CSI 300 index show the LOU-RV-HAR strategy achieves a better pairs trading performance and the Sharpe ratio is 1.6072, greatly exceeding performance of the CSI 300 index during the same period. Comparing with the four comparative trading strategies established by adjusting the spread model, stock selection method and trading threshold, the annualized return and Sharpe ratio of the LOU-RV-HAR strategy are better than those of the comparative strategies. Further analysis shows that the LOU-RV-HAR strategy displays robust results in different market conditions or different trading thresholds, and the overall pairs trading performance of CSI 300 component stocks is higher than the performance of sub-industries. © 2023 Systems Engineering Society of China. All rights reserved.
引用
收藏
页码:2251 / 2265
页数:14
相关论文
共 43 条
  • [11] Stubinger J, Endres S., Pairs trading with a mean–reverting jump–diffusion model on high–frequency data[J], Quantitative Finance, 18, 10, pp. 1735-1751, (2018)
  • [12] Endres S, Stubinger J., Optimal trading strategies for Lévy–driven ornstein-uhlenbeck processes[J], Applied Economics, 51, 29, pp. 3153-3169, (2019)
  • [13] Avellaneda M, Lee J H., Statistical arbitrage in the US equities market[J], Quantitative Finance, 10, 7, pp. 761-782, (2010)
  • [14] Clegg M, Krauss C., Pairs trading with partial cointegration[J], Quantitative Finance, 18, 1, pp. 121-138, (2018)
  • [15] Bertram W K., Analytic solutions for optimal statistical arbitrage trading[J], Physica A: Statistical Mechanics and Its Applications, 389, 11, pp. 2234-2243, (2010)
  • [16] Cai Y, Wang L, Xu L L., Research on paired trading based on stochastic spread method[J], Financial Theory and Practice, 34, 8, pp. 30-35, (2012)
  • [17] Liu Y H, Zhang D., Pairs trading strategies based on co-integration theory and OU stochastic process[J], Management Review, 29, 9, pp. 28-36, (2017)
  • [18] Bi X C, Yu X Y, Zhang S G., Pairs trading based on genetic algorithm — Partial cointegration theory[J], Journal of Statistical Research, 37, 9, pp. 82-94, (2020)
  • [19] Dong C H, Zhao Z W., Pairs trading strategies underlain by nonparametric additive cointegrations[J], Systems Engineering — Theory & Practice, 42, 6, pp. 1694-1720, (2022)
  • [20] Zhao H, Qin K J., Jumps in stock prices and macro information release[J], Journal of Statistical Research, 31, 4, pp. 79-88, (2014)