Riding Wavelets: A Method to Discover New Classes of Price Jumps

TL;DR

This paper introduces an unsupervised wavelet-based method to identify and analyze new classes of price jumps in financial markets, distinguishing endogenous from exogenous events and exploring co-jump contagion.

Contribution

The paper presents a novel wavelet coefficient representation for jump time-series, enabling the discovery of new jump classes and endogenous contagion mechanisms in stock prices.

Findings

Wavelet-based features reveal time asymmetry, mean-reversion, and trend in jumps.

Identification of new classes of price jumps based on key features.

Evidence of endogenous contagion in co-jumps across stocks.

Abstract

Cascades of events and extreme occurrences have garnered significant attention across diverse domains such as financial markets, seismology, and social physics. Such events can stem either from the internal dynamics inherent to the system (endogenous), or from external shocks (exogenous). The possibility of separating these two classes of events has critical implications for professionals in those fields. We introduce an unsupervised framework leveraging a representation of jump time-series based on wavelet coefficients and apply it to stock price jumps. In line with previous work, we recover the fact that the time-asymmetry of volatility is a major feature. Mean-reversion and trend are found to be two additional key features, allowing us to identify new classes of jumps. Furthermore, thanks to our wavelet-based representation, we investigate the reflexive properties of co-jumps, which occur when multiple stocks experience price jumps within the same minute. We argue that a significant fraction of co-jumps results from an endogenous contagion mechanism.