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Found 7 papers, 1 papers with code
Deep Generators on Commodity Markets; application to Deep Hedging
Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the financial industry.
On the challenges of using D-Wave computers to sample Boltzmann Random Variables
Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of \textit{Boltzmann machines}, a specific kind of neural network.
Expert Aggregation for Financial Forecasting
Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest.
Conditional Loss and Deep Euler Scheme for Time Series Generation
We introduce three new generative models for time series that are based on Euler discretization of Stochastic Differential Equations (SDEs) and Wasserstein metrics.
Deep combinatorial optimisation for optimal stopping time problems : application to swing options pricing
A new method for stochastic control based on neural networks and using randomisation of discrete random variables is proposed and applied to optimal stopping time problems.
Risk management with machine-learning-based algorithms
We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets.
Machine Learning for semi linear PDEs
Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations.
