跳至正文
返回文库全部文档

订单流与市场冲击的贝叶斯变点检测

文章 arXiv papers · 作者: Ioanna-Yvonni Tsaknaki et al.
阅读原文

总结

本文将持续的买卖订单流视为市场状态变化的证据；大额订单被拆分并随时间执行时，就可能出现这种情况。论文提出一种贝叶斯在线变点检测方法，并结合得分驱动模型，允许序列相关性存在，也允许参数在各状态内变化。其目标是实时检测变化，并改善对订单流和价格冲击的预测。

基于NASDAQ数据的实证应用报告称，与假设观测值相互独立的状态模型相比，其样本外预测更好；残差检验表明，所提模型能够较好地捕捉分布和时间特征。作者还发现，在各状态内，价格动态与时间和成交量呈凹性关系；纳入状态信息后，在线预测优于未纳入该信息的模型。这些发现仅适用于所研究的数据和模型比较；文档未提供实施详情，也未提供其他市场的证据。

核心观点

  • 订单流持续性可能反映大额订单的逐步执行。
  • 贝叶斯在线变点检测可在订单流状态变化时识别变化。
  • 得分驱动模型能够处理时间依赖性以及状态内参数变化。
  • NASDAQ应用报告称，纳入状态信息后，订单流和市场冲击预测有所改善。

标签

市场微观结构统计学机器学习交易执行
全文
# Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods


# Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods









Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.

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。