Runge-Kutta methods for third order weak approximation of SDEs with multidimensional additive noise
Provides a more efficient third-order weak approximation scheme for SDEs with additive noise, benefiting computational finance and stochastic simulation.
Introduced a new class of third-order Runge-Kutta methods for SDEs with additive noise that requires fewer random variable evaluations than Platen's method and works for multidimensional noise. Numerical tests show promising results compared to existing methods.
A new class of third order Runge-Kutta methods for stochastic differential equations with additive noise is introduced. In contrast to Platen's method, which to the knowledge of the author has been up to now the only known third order Runge-Kutta scheme for weak approximation, the new class of methods affords less random variable evaluations and is also applicable to SDEs with multidimensional noise. Order conditions up to order three are calculated and coefficients of a four stage third order method are given. This method has deterministic order four and minimized error constants, and needs in addition less function evaluations than the method of Platen. Applied to some examples, the new method is compared numerically with Platen's method and some well known second order methods and yields very promising results.