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Good and bad cojump dynamics: A network modeling perspective

Author

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  • Xia, Wenjing
  • Ye, Wuyi
  • Zhou, Yi

Abstract

This study examines cojump dynamics through network modeling, analyzing both positive and negative cojumps using 5-min high-frequency data from 194 stocks within the CIS 300 index. We apply eigenvector centrality to identify key stocks within these networks and utilize community detection method to classify clusters of stocks exhibiting similar cojump patterns. The findings indicate that negative cojump network demonstrates stronger inter-stock linkages, identifies the most influential stocks in finance and real estate sectors, and exhibits greater community intensity compared to the positive cojump network. Portfolios constructed using negative cojump rankings achieve higher Sharpe ratios than those based on positive cojump networks, and the integrating of negative cojump community information further improves return predictions. Overall, these insights underscore the vital role of negative cojump dynamics in optimizing investment strategies and strengthening risk management.

Suggested Citation

  • Xia, Wenjing & Ye, Wuyi & Zhou, Yi, 2026. "Good and bad cojump dynamics: A network modeling perspective," The North American Journal of Economics and Finance, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:ecofin:v:85:y:2026:i:c:s1062940826000914
    DOI: 10.1016/j.najef.2026.102669
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    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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