Information-based complexity
Stochastic Calculus for Fractional Brownian Motion I. Theory
SIAM Journal on Control and Optimization
Optimal approximation of stochastic differential equations by adaptive step-size control
Mathematics of Computation
Linear vs standard information for scalar stochastic differential equations
Journal of Complexity
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We study pathwise approximation of scalar stochastic differential equations with additive fractional Brownian noise of Hurst parameter H 1/2, considering the mean square L2-error criterion. By means of the Malliavin calculus we derive the exact rate of convergence of the Euler scheme, also for nonequidistant discretizations. Moreover, we establish a sharp lower error bound that holds for arbitrary methods, which use a fixed number of bounded linear functionals of the driving fractional Brownian motion. The Euler scheme based on a discretization, which reflects the local smoothness properties of the equation, matches this lower error bound up to the factor 1.39.