The study of stochastic differential equations (SDEs) has long been a cornerstone in the modelling of complex systems affected by randomness. In recent years, the extension to G-Brownian motion has ...

Stochastic differential equations (SDEs) provide a foundational framework for modelling systems subject to randomness, incorporating both continuous fluctuations and abrupt changes. In recent decades ...

This paper presents a novel and direct approach to solving boundary- and final-value problems, corresponding to barrier options, using forward pathwise deep learning and forward–backward stochastic ...

This is a preview. Log in through your library . Abstract In this paper, we introduce a Wasserstein-type distance on the set of the probability distributions of strong solutions to stochastic ...

Abstract This paper deals with a class of stochastic partial functional differential equations with finite delay. We give some sufficient conditions to guarantee the existence of a unique random ...

The paper presents a Bayesian framework for the calibration of financial models using neural stochastic differential equations (neural SDEs), for which we also formulate a global universal ...

This course is compulsory on the BSc in Actuarial Science. This course is available on the BSc in Business Mathematics and Statistics, BSc in Financial Mathematics and Statistics, BSc in Mathematics ...