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Entropy 2017, 19(12), 638;

How Successful Are Wavelets in Detecting Jumps?

Department of Economics, Bilgi University, Istanbul 34060, Turkey
Department of Economics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Author to whom correspondence should be addressed.
Received: 30 October 2017 / Revised: 22 November 2017 / Accepted: 22 November 2017 / Published: 25 November 2017
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory III)
Full-Text   |   PDF [284 KB, uploaded 25 November 2017]


We evaluate the performances of wavelet jump detection tests by using simulated high-frequency data, in which jumps and some other non-standard features are present. Wavelet-based jump detection tests have a clear advantage over the alternatives, as they are capable of stating the exact timing and number of jumps. The results indicate that, in addition to those advantages, these detection tests also preserve desirable power and size properties even in non-standard data environments, whereas their alternatives fail to sustain their desirable properties beyond standard data features. View Full-Text
Keywords: wavelet decomposition; jumps; jump tests wavelet decomposition; jumps; jump tests
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Eroğlu, B.A.; Gençay, R.; Yazgan, M.E. How Successful Are Wavelets in Detecting Jumps? Entropy 2017, 19, 638.

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