Limit theorems for additive functionals of some self-similar Gaussian processes

Under certain mild conditions, limit theorems for additive functionals of some d-dimensional self-similar Gaussian processes are obtained. These limit theorems work for general Gaussian processes including fractional Brownian motions, subfractional Brownian motions and bi-fractional Brownian motions. To prove these results, we use the method of moments and an enhanced chaining argument. The Gaussian processes under consideration are required to satisfy certain strong local nondeterminism property. A tractable sufficient condition for the strong local nondeterminism property is given and it only relays on the covariance functions of the Gaussian processes. Moreover, we give a sufficient condition for the distribution function of a random vector to be determined by its moments.

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Publication Details

Journal
The Annals of Applied Probability
Published
2024-12-01
DOI
https://doi.org/10.1214/24-aap2096
Citations
2
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
1.08
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article

Limit theorems for additive functionals of some self-similar Gaussian processes

Minhao Hong, Heguang Liu, Fangjun Xu
2 citations
The Annals of Applied Probability
Stochastic processes and financial applications
1.08
article

Limit theorems for additive functionals of some self-similar Gaussian processes

Minhao Hong, Heguang Liu, Fangjun Xu
article en
2 citations

Abstract

Under certain mild conditions, limit theorems for additive functionals of some d-dimensional self-similar Gaussian processes are obtained. These limit theorems work for general Gaussian processes including fractional Brownian motions, subfractional Brownian motions and bi-fractional Brownian motions. To prove these results, we use the method of moments and an enhanced chaining argument. The Gaussian processes under consideration are required to satisfy certain strong local nondeterminism property. A tractable sufficient condition for the strong local nondeterminism property is given and it only relays on the covariance functions of the Gaussian processes. Moreover, we give a sufficient condition for the distribution function of a random vector to be determined by its moments.

The Annals of Applied ProbabilityVol. 34(6)
East China Normal University (CN), Shanghai Maritime University (CN)
Openalex Percentile: Top 19%
Stochastic processes and financial applications
1.08
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