Mean-square convergence rates of implicit Milstein type methods for SDEs with non-Lipschitz coefficients: applications to financial models

Abstract

A novel class of implicit Milstein type methods is devised and analyzed in the present work for stochastic differential equations (SDEs) with non-globally Lipschitz drift and diffusion coefficients. By incorporating a pair of method parameters \(\theta, \eta \in [0, 1]\) into both the drift and diffusion parts, the new schemes can be viewed as a kind of double implicit methods, which also work for non-commutative noise driven SDEs. Within a general framework, we offer upper mean-square error bounds for the proposed schemes, based on certain error terms only getting involved with the exact solution processes. Such error bounds help us to easily analyze mean-square convergence rates of the schemes, without relying on a priori high-order moment estimates of numerical approximations. Putting further globally polynomial growth condition, we successfully recover the expected mean-square convergence rate of order one for the considered schemes with \(\theta \in [\tfrac12, 1]\), solving general SDEs in various circumstances. As applications, some of the proposed schemes are also applied to solve two scalar SDE models arising in mathematical finance and evolving in the positive domain \((0, \infty)\). More specifically, the particular drift-diffusion implicit Milstein method (\( \theta = \eta = 1 \)) is utilized to approximate the Heston \(\tfrac32\)-volatility model and the semi-implicit Milstein method (\(\theta =1, \eta = 0\)) is used to solve the Ait-Sahalia interest rate model. With the aid of the previously obtained error bounds, we reveal a mean-square convergence rate of order one for the positivity preserving schemes under more relaxed conditions, compared with existing relevant results in the literature. Numerical examples are finally reported to confirm the previous findings.