Gaussian Volterra processes with power-type kernels. Part II

Author:

Mishura YuliyaORCID,Shklyar SergiyORCID

Abstract

In this paper the study of a three-parametric class of Gaussian Volterra processes is continued. This study was started in Part I of the present paper. The class under consideration is a generalization of a fractional Brownian motion that is in fact a one-parametric process depending on Hurst index H. On the one hand, the presence of three parameters gives us a freedom to operate with the processes and we get a wider application possibilities. On the other hand, it leads to the need to apply rather subtle methods, depending on the intervals where the parameters fall. Integration with respect to the processes under consideration is defined, and it is found for which parameters the processes are differentiable. Finally, the Volterra representation is inverted, that is, the representation of the underlying Wiener process via Gaussian Volterra process is found. Therefore, it is shown that for any indices for which Gaussian Volterra process is defined, it generates the same flow of sigma-fields as the underlying Wiener process – the property that has been used many times when considering a fractional Brownian motion.

Publisher

VTeX

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Combinatorial approach to the calculation of projection coefficients for the simplest Gaussian-Volterra process;Modern Stochastics: Theory and Applications;2024

2. On the Gaussian Volterra processes with power-type kernels;Stochastic Models;2023-05-19

3. Gaussian Volterra processes: Asymptotic growth and statistical estimation;Theory of Probability and Mathematical Statistics;2023-05-02

4. Elements of fractional calculus. Fractional integrals;Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics;2022

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