Uncertainty propagation of large-scale discrete supply chains can be prohibitive when numerous events occur during the simulated period and when discrete-event simulations (DES) are costly. We present a time-bucket method to approximate and accelerate the DES of supply chains. Its stochastic...

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their...