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用小波分类价格跳跃与共跳传染

文章 arXiv papers · 作者: Cecilia Aubrun et al.
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总结

本研究提出一种无监督方法，通过小波系数构建表示来分析股票价格跳跃。该方法旨在区分与系统内部动态有关的跳跃模式和与外部冲击有关的模式。分析发现，波动率的时间不对称性是一个显著特征，并发现均值回归和趋势有助于定义其他跳跃类别。

研究还利用小波表示考察共跳，即多只股票在同一分钟内发生跳跃。作者认为，这些事件中有相当一部分反映了内生传染。这为研究冲击如何在股票间传播提供了一种方法，但本文未说明样本、验证流程，也未提供区分内生事件和外生事件的证据。因此，报告的结论描述的是该研究的发现，并非已经证实的通用分类规则。

核心观点

  • 小波系数为股票价格跳跃分类提供了一种无监督表示。
  • 波动率的时间不对称性是跳跃分析中的一个主要特征。
  • 均值回归和趋势是帮助区分跳跃类别的其他特征。
  • 作者认为，同一分钟内共跳事件中有相当一部分源于内生传染。

标签

股票机器学习统计学波动率均值回归动量
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# Riding Wavelets: A Method to Discover New Classes of Price Jumps


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









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。