Finance For Energy Market Research Centre

The Finance fo Energy Market Research Centre is a joint research project between the Université Paris-Dauphine, the Centre for Research in Economics and Statistics (CREST), the Ecole Polytechnique and the R&D Division of the EDF group.

This research centre is part of  the Chair Dauphine Ecole Polytechnique EDF Credit Agricole CIB  "Finance and Sustainable Development - A Quantitative Approach". It aims to allow researchers from all academic institutions interested in working with research engineers of  EDF R&D on issues of mathematical economics and quantitative finance long-term energy sector.


New open-source stochastic optimization library

The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some stochastic optimization problems encountered in finance or in the industry. A python binding is available for some C++ objects provided permitting to easily solve an optimization problem by regression.

Different methods are available : dynamic programming methods based on Monte Carlo  with regressions (global, local and  sparse regressors), for underlying states following;  some uncontrolled Stochastic Differential Equations  ;  Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled  Stochastic Differential Equations  and Stochastic Dual Dynamic Programming methods to deal with stochastic stocks management problems in high dimension

 Last publications


Some machine learning schemes for high-dimensional nonlinear PDEs - C. HURE, H. PHAM, X. WARIN

We propose new machine learning schemes for solving high dimensional nonlinear partial differential equations (PDEs). Relying on the classical backward stochastic differential equation (BSDE) representation of PDEs, our algorithms estimate simultaneously the solution and its gradient by deep neural networks. These approximations are performed at each time step from the minimization of loss functions...

Deep neural networks algorithms for stochastic control problems on nite horizon, Part 2: numerical applications - A. Bachouch, C. Huré, N. Langrené, H. Pham

This paper presents several numerical applications of deep learning-based algorithms that have been analyzed in . Numerical and comparative tests using TensorFlow illustrate the performance of our di erent algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples...

Deep neural networks algorithms for stochastic control problems on nite horizon, part I: convergence analysis - C. Huré, H. Pham, A. Bachouch, N. Langrené

This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming (DP). Di erently from the classical approximate DP approach, we rst approximate the optimal policy by means of neural networks in the spirit of deep reinforcement learning, and then the value function by Monte Carlo regression. This is achieved...

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