Self-similar Gaussian Markov processes

Benedict Bauer; Stefan Gerhold

doi:10.1080/17442508.2025.2540533

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arXiv · 2008.03052

Self-similar Gaussian Markov processes

Benedict Bauer·Stefan Gerhold

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Abstract

We characterize all multi-dimensional real self-similar Gaussian Markov processes. Three types of covariance matrix functions occur: white-noise type functions, covariances that can be expressed by continuous matrix semigroups, and covariances based on non-continuous solutions of Cauchy's functional equation. Characterizing the latter requires us to develop some results on the representation theory of non-continuous matrix semigroups, which are presented in a companion paper. In dimension one, besides white noise, the self-similar Gaussian Markov processes reduce to a two-parameter family of time-changed Brownian motions. This observation simplifies several proofs of non-Markovianity of concrete processes found in the literature.

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Benedict Bauer, Stefan Gerhold. 2020-08-07. Self-similar Gaussian Markov processes. https://doi.org/10.1080/17442508.2025.2540533

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