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Found 18 papers, 5 papers with code
D-TIPO: Deep time-inconsistent portfolio optimization with stocks and options
With an incomplete market and a more involved objective function, we show that it is beneficial to add options to the portfolio.
AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm
We combine the metrics of distance and isolation to develop the Analytic Isolation and Distance-based Anomaly (AIDA) detection algorithm.
A new self-exciting jump-diffusion process for option pricing
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.
Solution of integrals with fractional Brownian motion for different Hurst indices
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. $H \in (0, 1)$.
Convergence of a robust deep FBSDE method for stochastic control
In this paper, we propose a deep learning based numerical scheme for strongly coupled FBSDEs, stemming from stochastic control.
Pricing and Hedging Prepayment Risk in a Mortgage Portfolio
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.
Positive Stochastic Collocation for the Collocated Local Volatility Model
This paper presents how to apply the stochastic collocation technique to assets that can not move below a boundary.
Monte Carlo Simulation of SDEs using GANs
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.
Valuation of electricity storage contracts using the COS method
Storage of electricity has become increasingly important, due to the gradual replacement of fossil fuels by more variable and uncertain renewable energy sources.
Deep learning for CVA computations of large portfolios of financial derivatives
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.
The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations
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.
Financial option valuation by unsupervised learning with artificial neural networks
Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs).
On Calibration Neural Networks for extracting implied information from American options
For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield.
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN).
Generalisation in fully-connected neural networks for time series forecasting
In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting.
Pricing options and computing implied volatilities using neural networks
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.
Stochastic grid bundling method for backward stochastic differential equations
In this work, we apply the Stochastic Grid Bundling Method (SGBM) to numerically solve backward stochastic differential equations.
Numerical Analysis
Conditional Time Series Forecasting with Convolutional Neural Networks
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.
