375 citations · 608 across the 47 of their papers we have counts for
7 papers · 1 filter
Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications
Nikolaj T. Mücke, Benjamin Sanderse, Sander Bohté +1
In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, Markov Chain Generative Adversarial Neural Networks (MCG…
Pricing and Hedging Prepayment Risk in a Mortgage Portfolio
Emanuele Casamassima, Lech A. Grzelak, Frank A. Mulder +1
Understanding mortgage prepayment is crucial for any financial institution providing mortgages, and it is important for hedging the risk resulting from such unexpected cash flows.…
The One Step Malliavin scheme: new discretization of BSDEs implemented with deep learning regressions
Balint Negyesi, Kristoffer Andersson, Cornelis W. Oosterlee
A novel discretization is presented for forward-backward stochastic differential equations (FBSDE) with differentiable coefficients, simultaneously solving the BSDE and its Malliav…
Positive Stochastic Collocation for the Collocated Local Volatility Model
Fabien Le Floc'h, Cornelis W. Oosterlee
This paper presents how to apply the stochastic collocation technique to assets that can not move below a boundary. It shows that the polynomial collocation towards a lognormal dis…
Monte Carlo Simulation of SDEs using GANs
Jorino van Rhijn, Cornelis W. Oosterlee, Lech A. Grzelak +1
Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. We investigate if GANs can…
Energy-conserving formulation of the two-fluid model for incompressible two-phase flow in channels and pipes
J. F. H. Buist, B. Sanderse, S. Dubinkina +2
We show that the one-dimensional (1D) two-fluid model (TFM) for stratified flow in channels and pipes (in its incompressible, isothermal form) satisfies an energy conservation equa…