High-frequency lead-lag relationships in the Chinese stock index futures market: tick-by-tick dynamics of calendar spreads

ArXiv ID: 2501.03171 “View on arXiv”

Authors: Unknown

Abstract

Lead-lag relationships, integral to market dynamics, offer valuable insights into the trading behavior of high-frequency traders (HFTs) and the flow of information at a granular level. This paper investigates the lead-lag relationships between stock index futures contracts of different maturities in the Chinese financial futures market (CFFEX). Using high-frequency (tick-by-tick) data, we analyze how price movements in near-month futures contracts influence those in longer-dated contracts, such as next-month, quarterly, and semi-annual contracts. Our findings reveal a consistent pattern of price discovery, with the near-month contract leading the others by one tick, driven primarily by liquidity. Additionally, we identify a negative feedback effect of the “lead-lag spread” on the leading asset, which can predict returns of leading asset. Backtesting results demonstrate the profitability of trading based on the lead-lag spread signal, even after accounting for transaction costs. Altogether, our analysis offers valuable insights to understand and capitalize on the evolving dynamics of futures markets.

Keywords: lead-lag relationships, high-frequency trading, market microstructure, futures pricing, information flow, futures

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper uses advanced econometric methods like the Hayashi-Yoshida estimator and bootstrap testing, indicating high mathematical complexity. It also demonstrates high empirical rigor by using extensive tick-by-tick data, performing backtests with transaction costs, and presenting profitable out-of-sample results.
  flowchart TD
    A["Research Goal:<br>Investigate lead-lag relationships<br>in Chinese index futures market"] --> B["Data Source:<br>High-frequency tick-by-tick data<br>from CFFEX (Near, Next, Qtr, Semi-Annual)"]
    B --> C["Methodology:<br>Time-series regression &<br>Calendar spread analysis"]
    C --> D["Key Analysis:<br>Identify lead-lag spread &<br>Negative feedback effects"]
    D --> E["Validation:<br>Backtest trading strategy<br>using spread signals"]
    E --> F{"Key Outcomes"}
    F --> G["Near-month leads others<br>by one tick"]
    F --> H["Negative feedback on<br>leading asset"]
    F --> I["Profitable trading<br>after costs"]