Work

Crank–Nicolson scheme for stochastic differential equations driven by fractional Brownian motions

Year: 2021

Type: article

Abstract: We study the Crank–Nicolson scheme for stochastic differential equations (SDEs) driven by a multidimensional fractional Brownian motion with Hurst parameter H>1/2. It is well known that for ordinary differential equations with proper conditions on the regularity of the coefficients, the Crank–Nicolson scheme achieves a convergence rate of n−2, regardless of the dimension. In this paper we show that, due to the interactions between the driving processes, the corresponding Crank–Nicolson scheme for m-dimensional SDEs has a slower rate than for one-dimensional SDEs. Precisely, we shall prove that when the fBm is one-dimensional and when the drift term is zero, the Crank–Nicolson scheme achieves the convergence rate n−2H, and when the drift term is nonzero, the exact rate turns out to be n−12−H. In the general multidimensional case the exact rate equals n12−2H. In all these cases the asymptotic error is proved to satisfy some linear SDE. We also consider the degenerated cases when the asymptotic error equals zero.

Source: The Annals of Applied Probability

DOI: 10.1214/20-aap1582

Authors: Yaozhong Hu, Yanghui Liu, David Nualart

Institutions: University of Alberta, Baruch College, University of Kansas

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze

Funders: National Science Foundation, Natural Sciences and Engineering Research Council of Canada

Award: Stochastic Partial Differential Equations, Fractional Noises and Limit Theorems