论文标题
通过EPP效应检测离散过程
Detecting discrete processes with the Epps effect
论文作者
论文摘要
EPP效应是与金融市场高频相关动态有关的关键现象学。我们认为，它可以用来提供有关tick数据是否最好表示为来自布朗扩散的样本的洞察力，或者是来自表示为连接点过程的真正离散事件的样本。我们得出了由异步产生的EPP效应，并提供了一种校正效果的精制方法。然后，我们提出了三个实验，这些实验表明如何区分可能的基础表示。这些反过来又表明了简单的鹰派表示如何恢复文献中报道的现象学，而没有其他临时模型复杂性，无法使用布朗代表恢复。但是，相对于霍克斯表示，通常不能歧视基于布朗动作的复杂临时噪声模型。然而，我们认为，当将刻度数据表示为互连离散事件的网络时，高频相关动力学是最忠实地恢复的，而不是即使与噪声结合在一起，也不是连续布朗尼扩散的样本。
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.
