Nonlinear Autocorrelograms: an Application to Inter‐Trade Durations

Christian Gouriéroux and Joann Jasiak

Journal of Time Series Analysis, 2002, vol. 23, issue 2, 127-154

Abstract: The paper presents a study of temporal dependence in nonlinear transformations of time series. We examine the effects of parametric transformations on autocorrelation values and the persistence range with special emphasis on long memory processes. We derive an invariance property for the order of fractional integration of transformed normal processes and propose a related specification test. Within the class of nonlinear time series transforms, we identify those which maximize autocorrelations at selected lags. This procedure is based on nonlinear canonical correlations analysis adapted to serially correlated data. The methods proposed in this paper may be applied to various financial time series that usually are transformed prior to estimation, like returns, volumes or inter‐trade durations. In examples illustrating our approach, we use series of durations between trades of the Alcatel stock on the Paris Bourse.

Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.1111/1467-9892.00259

Related works:
Working Paper: Nonlinear Autocorrelograms: An Application to Intra-Trade Durations (1998)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:23:y:2002:i:2:p:127-154

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().