Neural Networks for Sustainable Risk Management, and Efficient Computation of Implied Volatility
In this research, we use neural networks for sustainable risk management, like for the computation of counterparty credit risk and credit value adjustments (CVA) of portfolios of financial derivatives, to solve stochastic control problems with a deep BSDE solver. Moreover, with neural networks, we aim to compute option prices and implied volatilities based on a nontrivial (“rich”) asset price model, by just using a cellphone for the computations.
Kristoffer Andersson, PhD student
prof. dr. ir. Kees Oosterlee, dr. ir. Lech Grzelak
EU funded project