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February 2011 Integral representations and properties of operator fractional Brownian motions
Gustavo Didier, Vladas Pipiras
Bernoulli 17(1): 1-33 (February 2011). DOI: 10.3150/10-BEJ259

Abstract

Operator fractional Brownian motions (OFBMs) are (i) Gaussian, (ii) operator self-similar and (iii) stationary increment processes. They are the natural multivariate generalizations of the well-studied fractional Brownian motions. Because of the possible lack of time-reversibility, the defining properties (i)–(iii) do not, in general, characterize the covariance structure of OFBMs. To circumvent this problem, the class of OFBMs is characterized here by means of their integral representations in the spectral and time domains. For the spectral domain representations, this involves showing how the operator self-similarity shapes the spectral density in the general representation of stationary increment processes. The time domain representations are derived by using primary matrix functions and taking the Fourier transforms of the deterministic spectral domain kernels. Necessary and sufficient conditions for OFBMs to be time-reversible are established in terms of their spectral and time domain representations. It is also shown that the spectral density of the stationary increments of an OFBM has a rigid structure, here called the dichotomy principle. The notion of operator Brownian motions is also explored.

Citation

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Gustavo Didier. Vladas Pipiras. "Integral representations and properties of operator fractional Brownian motions." Bernoulli 17 (1) 1 - 33, February 2011. https://doi.org/10.3150/10-BEJ259

Information

Published: February 2011
First available in Project Euclid: 8 February 2011

zbMATH: 1284.60079
MathSciNet: MR2797980
Digital Object Identifier: 10.3150/10-BEJ259

Keywords: dichotomy principle , integral representations , long-range dependence , multivariate Brownian motion , operator fractional Brownian motion , operator self-similarity , Time-reversibility

Rights: Copyright © 2011 Bernoulli Society for Mathematical Statistics and Probability

Vol.17 • No. 1 • February 2011
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