Error estimates for multinomial approximations of American options in a class of jump diffusion models

We derive error estimates for multinomial approximations of American options in a class of multidimensional jump diffusion models. We assume that the pay-offs are Markovian and satisfy Lipschitz-type conditions. Error estimates for such type of approximations were not obtained before. Our main tool is the strong approximation theorems for i.i.d. random vectors which were obtained in Sakhanenko (A new way to obtain estimates in the invariance principle, High Dimen. Probab. II (2000), pp. 221-243). For the multidimensional Black-Scholes model, our results can also be extended to a general path-dependent pay-offs which satisfy Lipschitz-type conditions. For the case of multinomial approximations of American options for the Black-Scholes model, our estimates are a significant improvement of those which were obtained in Kifer (Optimal stopping and strong approximation theorems, Stochastics 79 (2007), pp. 253-273; for game options in a more general set-up).