In this paper we present an efficient implementation of automatic differentiations of random variables (see 'https://ssrn.com/abstract=2995695' https://ssrn.com/abstract=2995695).Using this implementation can increase the speed of the calculation of the automatic differentiation and reduce the...

This first part of this presentation gives an introduction to stochastic automatic differentiation and its application.The second part of the presentation introduces a simple "static hedge" approximation for an SIMM based MVA and compares it with an exact solution (where the exact solution was...

In this paper we discuss how to incorporate analytic boundary conditions into a Monte-Carlo simulation framework and discuss their applications. The method introduced can dramatically improve the stability, robustness and accuracy of the valuation, calculation of sensitivities and stress...

We derive representations for forward sensitivities (also known as future sensitivities) in a Monte-Carlo simulation suitable for backward and forward differentiation. We compare the performance of the two approaches.The calculation of all forward sensitivities of a Monte-Carlo simulation with n...

In this note we apply the stochastic (backward) automatic differentiation to calculate stochastic forward sensitivities. A forward sensitivity is a sensitivity at a future point in time, conditional to the future states (i.e., it is a random variable). A typical application of stochastic forward...