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Convergence, Non-negativity and Stability of a New Milstein Scheme with Applications to Finance
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We propose and analyse a new Milstein type scheme for simulating stochastic differential equations (SDEs) with highly nonlinear coefficients. Our work is motivated by the need to justify multi-level Monte Carlo simulations for mean-reverting financial models with polynomial growth in the diffusion term. We introduce a double implicit Milstein scheme and show that it possesses desirable properties. It converges strongly and preserves non-negativity for a rich family of financial models and can reproduce linear and nonlinear stability behaviour of the underlying SDE without severe restriction on the time step. Although the scheme is implicit, we point out examples of financial models where an explicit formula for the solution to the scheme can be found.
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Strong Stability Preservation for Stochastic Partial Differential Equations
Stochastic Runge-Kutta methods built from monotone Euler-Maruyama schemes, bounded increments, and SSP time steppers preserve nonlinear stability pathwise with mean-square order 1/2.
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