1 citations · 3 across the 6 of their papers we have counts for
6 papers
Parametric and Generative Forecasts of EPEX Day-Ahead Energy Market Curves
Julian Gutierrez, Redouane Silvente
We propose two methodologies for modelling aggregated supply and demand curves in the EPEX SPOT Day-Ahead market, emphasizing generative models as a way to recover distributional v…
A Co-evolutionary Approach for Heston Calibration
Julian Gutierrez
We evaluate a co-evolutionary calibration framework for the Heston model in which a genetic algorithm (GA) over parameters is coupled to an evolving neural inverse map from option…
Machine Learning architectures for price formation models with common noise
Diogo Gomes, Julian Gutierrez, Mathieu Laurière
We propose a machine learning method to solve a mean-field game price formation model with common noise. This involves determining the price of a commodity traded among rational ag…
The Potential Method For Price-Formation Models
Yuri Ashrafyan, Tigran Bakaryan, Diogo Gomes +1
We consider the mean-field game price formation model introduced by Gomes and Saúde. In this MFG model, agents trade a commodity whose supply can be deterministic or stochastic. Ag…
A Variational Approach For Price Formation Models In One Dimension
Yuri Ashrafyan, Tigran Bakaryan, Diogo Gomes +1
In this paper, we study a class of first-order mean-field games (MFGs) that model price formation. Using Poincar{é} Lemma, we eliminate one of the equations and obtain a variationa…
Machine Learning architectures for price formation models
Diogo Gomes, Julián Gutiérrez, Mathieu Laurière
Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min…