no code implementations • 21 Aug 2023 • Kristoffer Andersson, Cornelis W. Oosterlee
With an incomplete market and a more involved objective function, we show that it is beneficial to add options to the portfolio.
1 code implementation • 5 Dec 2022 • Luis Antonio Souto Arias, Cornelis W. Oosterlee, Pasquale Cirillo
We combine the metrics of distance and isolation to develop the Analytic Isolation and Distance-based Anomaly (AIDA) detection algorithm.
1 code implementation • 26 May 2022 • Luis A. Souto Arias, Pasquale Cirillo, Cornelis W. Oosterlee
Furthermore, we show that by using partial integrals of the characteristic function, which are also explicitly known for the HQH process, we can reduce the dimensionality of the COS method, and so its numerical complexity.
no code implementations • 4 Mar 2022 • Fei Gao, Shuaiqiang Liu, Cornelis W. Oosterlee, Nico M. Temme
In this paper, we will evaluate integrals that define the conditional expectation, variance and characteristic function of stochastic processes with respect to fractional Brownian motion (fBm) for all relevant Hurst indices, i. e. $H \in (0, 1)$.
no code implementations • 18 Jan 2022 • 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.
no code implementations • 30 Sep 2021 • Emanuele Casamassima, Lech A. Grzelak, Frank A. Mulder, Cornelis W. Oosterlee
Here, in the setting of a Dutch mortgage provider, we propose to include non-linear financial instruments in the hedge portfolio when dealing with mortgages with the option to prepay part of the notional early.
no code implementations • 6 Sep 2021 • 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.
1 code implementation • 3 Apr 2021 • Jorino van Rhijn, Cornelis W. Oosterlee, Lech A. Grzelak, Shuaiqiang Liu
We compare the input-output map obtained with the standard GAN and supervised GAN and show experimentally that the standard GAN may fail to provide a path-wise approximation.
no code implementations • 8 Jan 2021 • Boris C. Boonstra, Cornelis W. Oosterlee
Storage of electricity has become increasingly important, due to the gradual replacement of fossil fuels by more variable and uncertain renewable energy sources.
no code implementations • 26 Oct 2020 • Kristoffer Andersson, Cornelis W. Oosterlee
In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, \textit{e. g.,} a portfolio of a mix of European- and Bermudan-type derivatives.
no code implementations • 7 Sep 2020 • Shuaiqiang Liu, Lech A. Grzelak, Cornelis W. Oosterlee
With a method variant called the compression-decompression collocation and interpolation technique, we can drastically reduce the number of neural network functions that have to be learned, so that computational speed is enhanced.
no code implementations • 25 May 2020 • Beatriz Salvador, Cornelis W. Oosterlee, Remco van der Meer
Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs).
no code implementations • 31 Jan 2020 • Shuaiqiang Liu, Álvaro Leitao, Anastasia Borovykh, Cornelis W. Oosterlee
For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield.
no code implementations • 23 Apr 2019 • Shuaiqiang Liu, Anastasia Borovykh, Lech A. Grzelak, Cornelis W. Oosterlee
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN).
no code implementations • 14 Feb 2019 • 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.
no code implementations • 25 Jan 2019 • 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 accelerating the corresponding numerical methods.
1 code implementation • 16 Jan 2018 • Ki Wai Chau, Cornelis W. Oosterlee
In this work, we apply the Stochastic Grid Bundling Method (SGBM) to numerically solve backward stochastic differential equations.
Numerical Analysis
3 code implementations • 14 Mar 2017 • Anastasia Borovykh, Sander Bohte, Cornelis W. Oosterlee
The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series.