Parameter-Related Strong Convergence Rate of the Backward Euler–Maruyama Method for Time-Changed Stochastic Differential Equations

The strong convergence of the backward Euler–Maruyama method for time-changed stochastic differential equations with additive noise is investigated. This work focuses on the detailed study of the relationship between the strong convergence rate and the parameter, [Formula: see text], of the inverse subordinator. The strong convergence rate of [Formula: see text] is proved and numerical simulations are provided to illustrate such an interesting observation.