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Identifying new classes of financial price jumps with wavelets

Author

Listed:
  • Cecilia Aubrun

    (b LadHyX UMR CNRS 7646 , École Polytechnique , Palaiseau Cedex 91128 , France)

  • Rudy Morel

    (d Center for Computational Mathematics, Flatiron Institute , New York , NY 10010)

  • Michael Benzaquen

    (e Capital Fund Management , Paris 75007 , France)

  • Jean-Philippe Bouchaud

    (f Académie des Sciences , Paris 75006 , France)

Abstract

We introduce an unsupervised classification framework that leverages a multiscale wavelet representation of time-series and apply it to stock price jumps. In line with previous work, we recover the fact that time-asymmetry of volatility is the major feature that separates exogenous, news-induced jumps from endogenously generated jumps. Local mean-reversion and trend are found to be two additional key features, allowing us to identify new classes of jumps. Using our wavelet-based representation, we investigate the endogenous or exogenous nature of cojumps, which occur when multiple stocks experience price jumps within the same minute. Perhaps surprisingly, our analysis suggests that a significant fraction of cojumps result from an endogenous contagion mechanism.

Suggested Citation

  • Cecilia Aubrun & Rudy Morel & Michael Benzaquen & Jean-Philippe Bouchaud, 2025. "Identifying new classes of financial price jumps with wavelets," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 122(6), pages 2409156121-, February.
    • Handle: RePEc:nas:journl:v:122:y:2025:p:e2409156121
    DOI: 10.1073/pnas.2409156121
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