Intraday high-frequency pairs trading strategies for energy futures: evidence from China

被引:2
|
作者
Luo, Jing [1 ]
Lin, YuCheng [2 ]
Wang, Sijia [1 ]
机构
[1] Southwestern Univ Finance & Econ, Swufe Inst, Chengdu, Peoples R China
[2] Southwestern Univ Finance & Econ, Inst Chinese Finance Studies SWUFE, Chengdu, Peoples R China
关键词
Chinese energy futures; pairs trading; Markov regime switching; Ornstein-Uhlenbeck process; high-frequency trading; COMMODITY FUTURES; STATISTICAL ARBITRAGE; MODEL; OIL; COINTEGRATION; PERFORMANCE; TESTS;
D O I
10.1080/00036846.2022.2161993
中图分类号
F [经济];
学科分类号
02 ;
摘要
We investigate the performance of pairs trading strategies based on Ornstein-Uhlenbeck (OU) process with jump-diffusion and regime-switching using minute-level data for five Chinese energy futures from 2 January 2020 to 30 November 2021 and compare them with traditional pairs trading strategies. Our results indicate that OU models can obtain an average return of 50.62% per annum and a Sharpe ratio of 2.63, which significantly exceed those of traditional pairs trading strategies. However, none of them could 'win' in every subperiod with diverse market conditions. Meanwhile, we find that introducing jump-diffusion indeed improves the performance (additional 25.37% annualized return and 1.12 Sharpe ratio). In contrast, considering more regimes does not always bring additional benefits. Robustness checks show that the superior performance of three-regime switching OU model (3RS-OUM) persists even under harsh trading conditions.
引用
收藏
页码:6646 / 6660
页数:15
相关论文
共 50 条
  • [21] RISK METRICS AND FINE TUNING OF HIGH-FREQUENCY TRADING STRATEGIES
    Cartea, Alvaro
    Jaimungal, Sebastian
    MATHEMATICAL FINANCE, 2015, 25 (03) : 576 - 611
  • [22] CAN HIGH-FREQUENCY TRADING STRATEGIES CONSTANTLY BEAT THE MARKET?
    Manahov, Viktor
    INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, 2016, 21 (02) : 167 - 191
  • [23] Novel modelling strategies for high-frequency stock trading data
    Xuekui Zhang
    Yuying Huang
    Ke Xu
    Li Xing
    Financial Innovation, 9
  • [24] Hybrid ARMA-GARCH-Neural Networks for intraday strategy exploration in high-frequency trading
    Alaminos, David
    Salas, M. Belen
    Partal-Urena, Antonio
    PATTERN RECOGNITION, 2024, 148
  • [25] ALGORITHMIC AND HIGH-FREQUENCY TRADING
    Sajter, Domagoj
    EKONOMSKA MISAO I PRAKSA-ECONOMIC THOUGHT AND PRACTICE, 2013, 22 (01): : 321 - 335
  • [26] Computerized and High-Frequency Trading
    Goldstein, Michael A.
    Kumar, Pavitra
    Graves, Frank C.
    FINANCIAL REVIEW, 2014, 49 (02) : 177 - 202
  • [27] High-frequency trading and institutional trading costs
    Chen, Marie
    Garriott, Corey
    JOURNAL OF EMPIRICAL FINANCE, 2020, 56 : 74 - 93
  • [28] The role of high-frequency data in volatility forecasting: evidence from the China stock market
    Liu, Min
    Lee, Chien-Chiang
    Choo, Wei-Chong
    APPLIED ECONOMICS, 2021, 53 (22) : 2500 - 2526
  • [29] Predicting energy futures high-frequency volatility using technical indicators: The role of interaction
    Gong, Xue
    Ye, Xin
    Zhang, Weiguo
    Zhang, Yue
    ENERGY ECONOMICS, 2023, 119
  • [30] Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index Futures
    Chen, Jing-Chao
    Zhou, Yu
    Wang, Xi
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 492 : 1664 - 1678