We are interested in the numerical approximation of solutions of nonlinear stochastic differential equations, that appear in financial mathematics. Here, we study the Aït-Sahalia model. We propose an explicit numerical scheme where we actually approximate the Lamperti transformation of the original stochastic differential equation and then transform back. The proposed method is domain preserving and is proven to converge strongly to the solution process with order at least $ 1 $ with no extra restrictions on the step-size $ \Delta $. Numerical experiments verify the theoretical results.
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Trajectories of (9) and (35) for the approximation of (3) for different parameters with various
Difference between (9) and (35) for the approximation of (3) with various step-sizes
Convergence of proposed method (9) for the approximation of (3) with reference solution produced at
Convergence of proposed method (9) for the approximation of (3) with reference solution produced at
Convergence of implicit method (35) for the approximation of (3) with reference solution produced at
Difference between (9) and (35) for the approximation of (3) for different parameter sets