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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
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