Automatic Adjoint Differentiation for special functions involving expectations

Abstract

We explain how to compute gradients of functions of the form G = 1 2 m i=1 (Ey i − C i ) 2 , which often appear in the calibration of stochastic models, using Automatic Adjoint Differentiation and parallelization. We expand on the work of [7] and give faster and easier to implement approaches. We also provide an implementation of our methods and apply the technique to calibrate European options.