Pathwise Convergent Approximation for the Fractional SDEs

Fractional stochastic differential equation (FSDE)-based random processes are used in a wide spectrum of scientific disciplines. However, in the majority of cases, explicit solutions for these FSDEs do not exist and approximation schemes have to be applied. In this paper, we study one-dimensional st...

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Bibliographic Details
Published inMathematics (Basel) Vol. 10; no. 4; p. 669
Main Authors Kubilius, Kęstutis, Medžiūnas, Aidas
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
Approximation
backward approximation
Convergence
Differential equations
fractional Brownian motion
Inequality
Lamperti transformation
Mathematical analysis
Mathematics
Random processes
Random variables
stochastic differential equations
Stochastic processes
Transformations (mathematics)
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Summary:Fractional stochastic differential equation (FSDE)-based random processes are used in a wide spectrum of scientific disciplines. However, in the majority of cases, explicit solutions for these FSDEs do not exist and approximation schemes have to be applied. In this paper, we study one-dimensional stochastic differential equations (SDEs) driven by stochastic process with Hölder continuous paths of order 1/2<γ<1. Using the Lamperti transformation, we construct a backward approximation scheme for the transformed SDE. The inverse transformation provides an approximation scheme for the original SDE which converges at the rate h2γ, where h is a time step size of a uniform partition of the time interval under consideration. This approximation scheme covers wider class of FSDEs and demonstrates higher convergence rate than previous schemes by other authors in the field.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10040669