Analysis of Contagion in China’s Stock Market: A Hawkes Process Perspective

ArXiv ID: 2512.08000 “View on arXiv”

Authors: Junwei Yang

Abstract

This study explores contagion in the Chinese stock market using Hawkes processes to analyze autocorrelation and cross-correlation in multivariate time series data. We examine whether market indices exhibit trending behavior and whether sector indices influence one another. By fitting self-exciting and inhibitory Hawkes processes to daily returns of indices like the Shanghai Composite, Shenzhen Component, and ChiNext, as well as sector indices (CSI Consumer, Healthcare, and Financial), we identify long-term dependencies and trending patterns, including upward, downward, and oversold rebound trends. Results show that during high trading activity, sector indices tend to sustain their trends, while low activity periods exhibit strong sector rotation. This research models stock price movements using spatiotemporal Hawkes processes, leveraging conditional intensity functions to explain sector rotation, advancing the understanding of financial contagion.

Keywords: Hawkes Processes, Contagion, Sector Rotation, Self-Exciting Processes, Multivariate Time Series, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper heavily employs advanced mathematical theory, including multivariate Hawkes processes, stochastic calculus, and spectral methods, indicating high complexity. While it uses real market data (Chinese indices) and discusses fitting processes, the summary and excerpt lack specific backtest results, performance metrics, or detailed implementation code, suggesting moderate empirical rigor focused on theoretical modeling rather than fully backtested trading strategies.

  flowchart TD
    Start["Research Goal: Analyze contagion & trends in China's stock market using Hawkes processes"] --> Data["Data Inputs<br>Daily returns: Shanghai Composite, Shenzhen Component, ChiNext<br>Sector Indices: CSI Consumer, Healthcare, Financial"]
    Data --> Method["Methodology<br>Multivariate Hawkes Process<br>Self-exciting & Inhibitory models"]
    Method --> Comp["Computational Process<br>Fit conditional intensity functions<br>Analyze autocorrelation & cross-correlation"]
    Comp --> Out1["Findings 1: Long-term dependencies & trending patterns (upward, downward, oversold)"]
    Comp --> Out2["Findings 2: High activity sustains trends; low activity drives sector rotation"]
    Out1 --> End["Outcome: Spatiotemporal model explains financial contagion & sector rotation"]
    Out2 --> End