2 citations · 6 across the 7 of their papers we have counts for
7 papers
Convergence of fictitious play for fully coupled FBSDEs in finite-player stochastic differential games
Adam Andersson, Kristoffer Andersson, Per Ljung
In this article we investigate the theoretical convergence properties of the fictitious-play approximation procedure applied to coupled FBSDE systems for finite-player non-zero-sum…
The deep multi-FBSDE method: a robust deep learning method for coupled FBSDEs
Kristoffer Andersson, Adam Andersson, Cornelis W. Oosterlee
We introduce the deep multi-FBSDE method for robust approximation of coupled forward-backward stochastic differential equations (FBSDEs), focusing on cases where the deep BSDE meth…
D-TIPO: Deep time-inconsistent portfolio optimization with stocks and options
Kristoffer Andersson, Cornelis W. Oosterlee
In this paper, we propose a machine learning algorithm for time-inconsistent portfolio optimization. The proposed algorithm builds upon neural network based trading schemes, in whi…
Convergence of a robust deep FBSDE method for stochastic control
Kristoffer Andersson, Adam Andersson, Cornelis W. Oosterlee
In this paper, we propose a deep learning based numerical scheme for strongly coupled FBSDEs, stemming from stochastic control. It is a modification of the deep BSDE method in whic…
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…
Deep learning for CVA computations of large portfolios of financial derivatives
Kristoffer Andersson, Cornelis W. Oosterlee
In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of der…
A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options
Kristoffer Andersson, Cornelis Oosterlee
In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies…