375 citations · 608 across the 47 of their papers we have counts for
6 papers · 1 filter
Exploration of a Cosine Expansion Lattice Scheme
Ki Wai Chau, Cornelis W. Oosterlee
In this article, we combine a lattice sequence from Quasi-Monte Carlo rules with the philosophy of the Fourier-cosine method to design an approximation scheme for expectation compu…
Efficient Computation of Various Valuation Adjustments Under Local Lévy Models
Anastasia Borovykh, Andrea Pascucci, Cornelis W. Oosterlee
Various valuation adjustments, or XVAs, can be written in terms of non-linear PIDEs equivalent to FBSDEs. In this paper we develop a Fourier-based method for solving FBSDEs in orde…
A neural network-based framework for financial model calibration
Shuaiqiang Liu, Anastasia Borovykh, Lech A. Grzelak +1
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal…
A parametric acceleration of multilevel Monte Carlo convergence for nonlinear variably saturated flow
Prashant Kumar, Carmen Rodrigo, Francisco J. Gaspar +1
We present a multilevel Monte Carlo (MLMC) method for the uncertainty quantification of variably saturated porous media flow that are modeled using the Richards' equation. We propo…
Generalisation in fully-connected neural networks for time series forecasting
Anastasia Borovykh, Cornelis W. Oosterlee, Sander M. Bohte
In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting. Time series do not satisfy the typical…
Pricing options and computing implied volatilities using neural networks
Shuaiqiang Liu, Cornelis W. Oosterlee, Sander M. Bohte
This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of acceler…