Computable error bounds of multidimensional Euler inversion and their financial applications

Transform inversion is an efficient approximation procedure in operations research, yet the inversion results are sometimes unstable which calls for comprehensive error analysis. This article proposes a multidimensional Euler inversion (MEI) algorithm with computable error bounds. We design mild sufficient conditions that validate the inversion formula, and provide closed-form upper bounds of the inversion errors. Numerical experiments are conducted to compute the joint probability of default and barrier option prices under complicated stochastic models, and output the associated error bounds.