Wavelet eigenvalue regression for n-variate operator fractional Brownian motion
Patrice Abry and
Journal of Multivariate Analysis, 2018, vol. 168, issue C, 75-104
In this paper, we extend the methodology proposed in Abry and Didier (2018) to obtain the first joint estimator of the real parts of the Hurst eigenvalues of n-variate operator fractional Brownian motion (OFBM). The procedure consists of a weighted regression on the log-eigenvalues of the sample wavelet spectrum. The estimator is shown to be consistent for any time reversible OFBM and, under stronger assumptions, also asymptotically normal starting from either continuous or discrete time measurements. Simulation studies establish the finite-sample effectiveness of the methodology and illustrate its benefits compared to univariate-like (entry-wise) analysis. As an application, we revisit the well-known self-similar character of Internet traffic by applying the proposed methodology to 4-variate time series of modern, high quality Internet traffic data. The analysis reveals the presence of a rich multivariate self-similarity structure.
Keywords: Eigenvalues; Operator fractional Brownian motion; Operator self-similarity; Wavelets (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:168:y:2018:i:c:p:75-104
Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Journal of Multivariate Analysis is currently edited by de Leeuw, J.
More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().