Economics at your fingertips  

Identifying events in financial time series – A new approach with bipower variation

György Andor and András Bohák

Finance Research Letters, 2017, vol. 22, issue C, 42-48

Abstract: We present a statistical test to identify significant events in financial price time series. In contrast to “jumps,” we define “events” as non-instantaneous, but nevertheless unusually fast and large, price changes. We show that non-parametric tests perform badly in detecting events so defined. We propose a new approach to explore the dependence of jump detection statistics on the sampling method used and find that our method improves the event detection rate of the standard test by a factor of three.

Keywords: Jump detection; Event detection; Realized volatility; Bipower variation (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Series data maintained by Dana Niculescu ().

Page updated 2018-01-24
Handle: RePEc:eee:finlet:v:22:y:2017:i:c:p:42-48