Pathwise Convergent Approximation for the Fractional SDEs

Kęstutis Kubilius and Aidas Medžiūnas
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Kęstutis Kubilius: Faculty of Mathematics and Informatics, Vilnius University, Akademijos g. 4, LT-08412 Vilnius, Lithuania
Aidas Medžiūnas: Faculty of Mathematics and Informatics, Vilnius University, Akademijos g. 4, LT-08412 Vilnius, Lithuania

Mathematics, 2022, vol. 10, issue 4, 1-16

Abstract: 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 h 2 γ , 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.

Keywords: stochastic differential equations; fractional Brownian motion; backward approximation; Lamperti transformation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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