Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288517 
Year of Publication: 
2020
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 61 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 1565-1588
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Order patterns and permutation entropy have become useful tools for studying biomedical, geophysical or climate time series. Here we study day-to-day market data, and Brownian motion which is a good model for their order patterns. A crucial point is that for small lags (1 up to 6 days), pattern frequencies in financial data remain essentially constant. The two most important order parameters of a time series are turning rate and up-down balance. For change points in EEG brain data, turning rate is excellent while for financial data, up-down balance seems the best. The fit of Brownian motion with respect to these parameters is tested, providing a new version of a forgotten test by Bienaymé.
Subjects: 
Order pattern
Time series
Permutation entropy
Stock data
JEL: 
M10
B84
G18
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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