MANAGEMENT SCIENCE ·VOL. 62, NO. 8 ·AUGUST 2016
高頻數據跳躍：虛假檢測，動态特征與新聞
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：把跳躍檢驗應用到金融數據集上導緻相對高數量的虛假檢測。當樣本過于分散時，頻繁波動常常被誤為是跳躍。在更高頻率上，對微觀噪聲穩健的方法是必需的。本文研究認為，無論跳躍檢測還是采樣頻率，多個檢測問題導緻高度相關的虛假檢測數量仍然存在。本文研究基于可用檢測統計量中的明确閥值，提出一個正式的解決方法。研究證明，該方法漸進消除了所有剩餘的虛假檢測。對于2006-2008年間的道瓊斯股票，虛假檢測可以代表最初發現的高達90%的跳躍。關于考慮到的股票，跳躍是罕見的事件，時間上沒有集中性，并沒有任何聯合跳躍同時影響所有股票，因而表明跳躍風險是分散的。接着研究将剩餘跳躍關聯到宏觀新聞、預先安排的特定公司公告和包括各種不定期的、無組織的事件的新聞報道上。結果表明絕大多數的新聞不會導緻跳躍，但可能會産生頻繁波動形式的市場反應。
關鍵詞：跳躍，高頻數據，虛假檢測，跳躍動态，新聞報道，聯合跳躍
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.
原文鍊接：http://www.scaillet.ch/pdfs/jumps.pdf
翻譯：景薇
