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# On some method on model construction for strictly φ-sub-Gaussian generalized fractional Brownian motion

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## Abstract

In the paper, we consider the problem of simulation of a strictly φ-sub-Gaussian generalized fracti-onal Brownian motion. Simulation of random processes and fields is used in many areas of natural and social sciences. A special place is occupied by methods of simulation of the Wiener process and fractional Brownian motion, as these processes are widely used in financial and actuarial mathematics, queueing theory etc. We study some specific class of processes of generalized fractional Brownian motion and derive conditions, under which the model based on a series representation approximates a strictly φ-sub-Gaussian generalized fractional Brownian motion with given reliability and accuracy in the space C([0; 1]) in the case, when φ(x) = exp{|x|} − |x| − 1, x ∈ R. In order to obtain these results, we use some results from the theory of φ-sub-Gaussian random processes. Necessary simulation parameters are calculated and models of sample pathes of corresponding processes are constructed for various values of the Hurst parameter H and for given reliability and accuracy using the R programming environment.

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In the paper, we consider random variables and stochastic processes from the space Fψ(Ω) and study approximation problems for such processes. The method of series decomposition of stochastic processes from Fψ(Ω) is used to find an approximating process called a model. The rate ofconvergence of the model to the process in the uniform norm is investigated. We develop an approach for estimating the cutting-offlevel of the model under the given accuracy and reliability of the simulation. MSC Classification: 60G07, 60G15, 65C20, 68U20
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We consider simulation of $${\text{Sub}}_{\varphi } {\left( \Omega \right)}$$-processes that are weakly selfsimilar with stationary increments in the sense that they have the covariance function $$R{\left( {t,s} \right)} = \frac{1}{2}{\left( {t^{{2H}} + s^{{2H}} - {\left| {t - s} \right|}^{{2H}} } \right)}$$for some H ∈ (0, 1). This means that the second order structure of the processes is that of the fractional Brownian motion. Also, if $$H >\frac{1} {2}$$ then the process is long-range dependent. The simulation is based on a series expansion of the fractional Brownian motion due to Dzhaparidze and van Zanten. We prove an estimate of the accuracy of the simulation in the space C([0, 1]) of continuous functions equipped with the usual sup-norm. The result holds also for the fractional Brownian motion which may be considered as a special case of a $${\text{Sub}}_{{{x^{2} } \mathord{\left/ {\vphantom {{x^{2} } 2}} \right. \kern-\nulldelimiterspace} 2}} {\left( \Omega \right)}$$-process.
Article
In the paper, we consider the problem of simulation of a strictly φ-sub-Gaussian generalized fractional Brownian motion. Simulation of random processes and fields is used in many areas of natural and social sciences. A special place is occupied by methods of simulation of the Wiener process and fractional Brownian motion, as these processes are widely used in financial and actuarial mathematics, queueing theory etc. We study some specific class of processes of generalized fractional Brownian motion and derive conditions, under which the model based on a series representation approximates a strictly φ-sub-Gaussian generalized fractional Brownian motion with given reliability and accuracy in the space C([0; 1]) in the case, when φ(x) = (|x|^p)/p, |x| ≥ 1, p > 1. In order to obtain these results, we use some results from the theory of φ-sub-Gaussian random processes. Necessary simulation parameters are calculated and models of sample pathes of corresponding processes are constructed for various values of the Hurst parameter H and for given reliability and accuracy using the R programming environment.
A series expansion of fractional Brownian motion. Probability Theory and Related Fields 130
• O Dzhaparidze K
• J H Zanten
Space of φ-sub-Gaussian random variables
• R Giuliano Antonini
• Yu V Kozachenko
• T Nikitina
Banach spaces of random variables of sub-Gaussian type
• Yu V Kozachenko
• E I Ostrovskii
) φ-sub-Gaussian random process, Kyiv: Vydavnycho-Poligrafichnyi Tsentr "Kyivskyi Universytet
• O I Vasylyk
• Yu V Kozachenko
• R E Yamnenko