The Endo-Exo Problem in High Frequency Financial Price Fluctuations and Rejecting Criticality

Spencer Wheatley, Alexander Wehrli, D. Sornette
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引用次数: 7

Abstract

The endo-exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization (EM) algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.
金融价格高频波动中的Endo-Exo问题及拒绝临界性
内-外问题是许多科学领域统计识别的核心问题,由于对数据的趋势、冲击和变化处理不当,经常受到虚假的强而长记忆的困扰。霍克斯过程(Hawkes process)是一类在区分外源性和内源性活动方面很有用的模型。这类点过程最近在定量金融文献中得到了很大的普及和快速发展,特别关注市场微观结构和高频价格波动的研究。我们表明,时间序列和计量经济学等旧领域的重要经验教训也应该应用于金融点过程建模。特别是,我们强调了适当处理趋势和冲击对于识别系统中记忆的强度和长度的重要性。我们利用强大的期望最大化(EM)算法和客观统计准则(BIC)来选择确定性背景强度的灵活性。通过这些方法,我们强烈反对认为金融市场在单变量和双变量微观结构水平上至关重要的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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