MANAGEMENT SCIENCE ·VOL. 62, NO. 8 ·AUGUST 2016
高频数据跳跃：虚假检测，动态特征与新闻
作者：Pierre Bajgrowicz (University of Geneva), Olivier Scaillet (University of Geneva and Swiss Finance Institute), Adrien Treccani (University of Geneva and Swiss Finance Institute)
摘要：把跳跃检验应用到金融数据集上导致相对高数量的虚假检测。当样本过于分散时，频繁波动常常被误为是跳跃。在更高频率上，对微观噪声稳健的方法是必需的。本文研究认为，无论跳跃检测还是采样频率，多个检测问题导致高度相关的虚假检测数量仍然存在。本文研究基于可用检测统计量中的明确阀值，提出一个正式的解决方法。研究证明，该方法渐进消除了所有剩余的虚假检测。对于2006-2008年间的道琼斯股票，虚假检测可以代表最初发现的高达90%的跳跃。关于考虑到的股票，跳跃是罕见的事件，时间上没有集中性，并没有任何联合跳跃同时影响所有股票，因而表明跳跃风险是分散的。接着研究将剩余跳跃关联到宏观新闻、预先安排的特定公司公告和包括各种不定期的、无组织的事件的新闻报道上。结果表明绝大多数的新闻不会导致跳跃，但可能会产生频繁波动形式的市场反应。
关键词：跳跃，高频数据，虚假检测，跳跃动态，新闻报道，联合跳跃
Jumps in high-frequency data: spurious detections, dynamics, and news
Pierre Bajgrowicz (University of Geneva), Olivier Scaillet (University of Geneva and Swiss Finance Institute), Adrien Treccani (University of Geneva and Swiss Finance Institute)
ABSTRACT
Applying tests for jumps to financial data sets can lead to an important number of spurious detections. Bursts of volatility are often incorrectly identified as jumps when the sampling is too sparse. At a higher frequency, methods robust to microstructure noise are required. We argue that whatever the jump detection test and the sampling frequency, a highly relevant number of spurious detections remain because of multiple testing issues. We propose a formal treatment based on an explicit thresholding on available test statistics. We prove that our method eliminates asymptotically all remaining spurious detections. In Dow Jones stocks between 2006 and 2008, spurious detections can represent up to 90% of the jumps detected initially. For the stocks considered, jumps are rare events, they do not cluster in time, and no cojump affects all stocks simultaneously, suggesting jump risk is diversifiable. We relate the remaining jumps to macroeconomic news, prescheduled company-specific announcements, and stories from news agencies which include a variety of unscheduled and uncategorized events. The vast majority of news do not cause jumps but may generate a market reaction of the form of bursts of volatility.
Keywords: jumps, high-frequency data, spurious detections, jumps dynamics, news releases, cojumps.
原文链接：http://www.scaillet.ch/pdfs/jumps.pdf
翻译：景薇
