Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/197439 
Year of Publication: 
2016
Citation: 
[Journal:] e-Finanse: Financial Internet Quarterly [ISSN:] 1734-039X [Volume:] 12 [Issue:] 3 [Publisher:] University of Information Technology and Management [Place:] Rzeszów [Year:] 2016 [Pages:] 49-58
Publisher: 
University of Information Technology and Management, Rzeszów
Abstract: 
We closely examine and compare two promising techniques helpful in estimating the moment an asset bubble bursts. Namely, the Log-Periodic Power Law model and Generalized Hurst Exponent approaches are considered. Sequential LPPL fitting to empirical financial time series exhibiting evident bubble behavior is presented. Estimating the critical crash-time works satisfactorily well also in the case of GHE, when substantial 'decorrelation' prior to the event is visible. An extensive simulation study carried out on empirical data: stock indices and commodities, confirms very good performance of the two approaches.
Subjects: 
asset bubble
crash
Log-Periodic Power Law
Generalized Hurst Exponent
multiractality
forecasting
bursting time estimation
JEL: 
C22
C53
C61
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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