利用Epps效应检测离散过程
Detecting discrete processes with the Epps effect
AI总结：
本文提出利用Epps效应区分金融交易数据的潜在表示，推导异步性产生的Epps效应并改进校正方法，通过实验表明Hawkes离散事件表示比布朗扩散表示更能复现高频相关动力学现象。
摘要：
Epps效应是金融市场高频相关动力学的关键现象。我们认为，它可用于深入了解交易数据（tick data）应被最佳表示为布朗扩散的样本，还是真正离散事件的样本（表现为关联点过程）。我们推导了由异步性产生的Epps效应，并提出一种改进方法来校正该效应。随后，我们设计了三个实验，展示如何区分不同的潜在表示。这些实验进而表明，简单的Hawkes表示能复现文献中报道的现象，而若不增加额外的特设模型复杂度，布朗表示无法复现这些现象。然而，基于布朗运动构建的复杂特设噪声模型通常无法与Hawkes表示区分。尽管如此，我们认为，即使结合噪声，将交易数据表示为相互关联的离散事件网络，而非连续布朗扩散的样本，也能最忠实地复现高频相关动力学。
英文摘要：
The Epps effect is key phenomenology relating to high frequency correlation dynamics in financial markets. We argue that it can be used to provide insight into whether tick data is best represented as samples from Brownian diffusions, or as samples from truly discrete events represented as connected point processes. We derive the Epps effect arising from asynchrony and provide a refined method to correct for the effect. We then propose three experiments which show how to discriminate between possible underlying representations. These in turn demonstrate how a simple Hawkes representation recovers phenomenology reported in the literature that cannot be recovered using a Brownian representation without additional ad hoc model complexity. However, complex ad hoc noise models built on Brownian motions cannot in general be discriminated relative to a Hawkes representation. Nevertheless, we argue that high frequency correlation dynamics are most faithfully recovered when tick data is represented as a web of interconnected discrete events rather than being samples from continuous Brownian diffusions even when combined with noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
