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Found 8 papers, 1 papers with code
Improved model-free bounds for multi-asset options using option-implied information and deep learning
We consider the computation of model-free bounds for multi-asset options in a setting that combines dependence uncertainty with additional information on the dependence structure.
Optimal Market Making in the Chinese Stock Market: A Stochastic Control and Scenario Analysis
Furthermore, we find that the stamp duty rate is a critical factor in market making, with a negative impact on both the profit of the market maker and the liquidity of the market.
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. $H \in (0, 1)$.
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.
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.
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).
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.
