An Adaptive Random Bit Multilevel Algorithm for SDEs

We study the approximation of expectations \(\operatorname{E}(f(X))\) for solutions \(X\) of stochastic differential equations and functionals \(f\) on the path space by means of Monte Carlo algorithms that only use random bits instead of random numbers. We construct an adaptive random bit multilevel algorithm, which is based on the Euler scheme, the Lévy-Ciesielski representation of the Brownian motion, and asymptotically optimal random bit approximations of the standard normal distribution. We numerically compare this algorithm with the adaptive classical multilevel Euler algorithm for a geometric Brownian motion, an Ornstein-Uhlenbeck process, and a Cox-Ingersoll-Ross process.