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

2025-12-08

AI Summary

This study investigates risk contagion mechanisms among Chinese stock market indices, focusing on market trend persistence and sectoral rotation. To model these dynamics, we propose a spatiotemporal Hawkes process integrating both self-excitation and self-inhibition effects, where the conditional intensity function captures dynamic auto- and cross-correlations in multivariate daily return series. Unlike conventional models, our framework jointly characterizes two canonical contagion patterns—trend reinforcement during high-activity regimes and style switching during low-activity regimes—while identifying nonlinear features such as long-range dependence and oversold rebound effects. Empirical analysis demonstrates that the proposed model significantly improves both modeling accuracy and economic interpretability of financial contagion dynamics. It thus offers a novel methodological foundation for systemic risk monitoring and cross-sector investment decision-making.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy

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 over- sold 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.

Problem

• Modeling stock market contagion using Hawkes processes
• Analyzing autocorrelation and cross-correlation in Chinese indices
• Explaining sector rotation and trending behavior dynamics

Innovation

• Hawkes processes model autocorrelation and cross-correlation
• Conditional intensity functions explain sector rotation patterns
• Spatiotemporal Hawkes processes capture long-term dependencies