Identifying new classes of financial price jumps with wavelets

Cecilia Aubrun, Rudy Morel (), Michael Benzaquen and Jean-Philippe Bouchaud
Additional contact information
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

Proceedings of the National Academy of Sciences, 2025, vol. 122, issue 6, e2409156121

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.

Keywords: price jumps; classification; reflexivity; cojumps; wavelets (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:122:y:2025:p:e2409156121

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