发表机构
ENSTA — Institut Polytechnique de Paris; Scuola Normale Superiore（巴黎理工学院 ENSTA; 比萨高等师范学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本报告研究贝叶斯在线变点检测及其扩展，用于实时识别金融市场制度转换，并应用于纳斯达克股票订单流。
AI 中文摘要
金融市场在相对稳定和不稳定时期之间交替，结构性突变标志着这些制度之间的转换。实时识别此类突变是任何高频运行的交易或风险系统的核心要求。本报告研究了贝叶斯在线变点检测（BOCPD）及其文献中提出的两种扩展方法，并将其应用于纳斯达克上市股票的符号订单流。
英文摘要
Financial markets alternate between periods of relative stability and instability, with structural breaks marking the transitions between these regimes. Identifying such breaks in real time is a central requirement for any trading or risk system operating at high frequency. This report studies Bayesian Online Changepoint Detection (BOCPD) and two extensions proposed in the literature, and applies them to the signed order flow of NASDAQ-listed equities.
Comments59 pages. 48 figures. Summer research project of ENSTA at Scuola Normale Superiore (Pisa)
