Comparison Tests of Variable-Stepsize Algorithms for Stochastic Ordinary   Differential Equations of Finance
  Since the introduction of the Black-Scholes model stochastic processes have played an increasingly important role in mathematical finance. In many cases prices, volatility and other quantities can be modeled using stochastic ordinary differential equations. Available methods for solving such equations have until recently been markedly inferior to analogous methods for deterministic ordinary differential equations. Recently, a number of methods which employ variable stepsizes to control local error have been developed which appear to offer greatly improved speed and accuracy. Here we conduct a comparative study of the performance of these algorithms for problems taken from the mathematical finance literature. 
Yin Mei Wong and Joshua Wilkie