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dc.contributor.author
Zhang, Qun
dc.contributor.author
Zhang, Qunzhi
dc.contributor.author
Sornette, Didier
dc.date.accessioned
2018-08-30T11:43:26Z
dc.date.available
2017-06-12T16:01:44Z
dc.date.available
2018-08-30T11:43:26Z
dc.date.issued
2016-11-02
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0165819
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/122690
dc.identifier.doi
10.3929/ethz-b-000122690
dc.description.abstract
We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the “S&P 500 1987” bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Early warning signals of financial crises with multi-scale quantile regressions of log-periodic power law singularities
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS ONE
ethz.journal.volume
11
en_US
ethz.journal.issue
11
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e0165819
en_US
ethz.size
43 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
006206116
ethz.publication.place
San Francisco, CA, USA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03738 - Sornette, Didier / Sornette, Didier
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03738 - Sornette, Didier / Sornette, Didier
ethz.date.deposited
2017-06-12T16:06:23Z
ethz.source
ECIT
ethz.identifier.importid
imp593654df546a352917
ethz.ecitpid
pub:185017
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-15T03:30:13Z
ethz.rosetta.lastUpdated
2018-12-02T13:17:17Z
ethz.rosetta.versionExported
true
ethz.COinS
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