In mathematics, the Milstein method is a technique for the approximate numerical solution of a stochastic differential equation. It is named after Grigori N. Milstein who first published it in 1974.

Description

Consider the autonomous Itō stochastic differential equation: with initial condition, where W_{t} denotes the Wiener process, and suppose that we wish to solve this SDE on some interval of time [0,T]. Then the Milstein approximation to the true solution X is the Markov chain Y defined as follows: Note that when b'(Y_n) = 0 (i.e. the diffusion term does not depend on X_{t}) this method is equivalent to the Euler–Maruyama method. The Milstein scheme has both weak and strong order of convergence \Delta t which is superior to the Euler–Maruyama method, which in turn has the same weak order of convergence \Delta t but inferior strong order of convergence.

Intuitive derivation

For this derivation, we will only look at geometric Brownian motion (GBM), the stochastic differential equation of which is given by: with real constants \mu and \sigma. Using Itō's lemma we get: Thus, the solution to the GBM SDE is: where The numerical solution is presented in the graphic for three different trajectories.

Computer implementation

The following Python code implements the Milstein method and uses it to solve the SDE describing geometric Brownian motion defined by