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


Numerical resolution of McKean-Vlasov FBSDEs using neural networks - Maximilien Germain, Joseph Mikael, Xavier Warin

We propose several algorithms to solve McKean-Vlasov Forward Backward Stochastic Differential Equations. Our schemes rely on the approximating power of neural networks to estimate the solution or its gradient through minimization problems. As a consequence, we obtain methods able to tackle both mean field games and mean field control problems in high dimension. We...

Neural networks-based backward scheme for fully nonlinear PDEs - Huyen Pham, Xavier Warin

We propose a numerical method for solving high dimensional fully nonlinear partial differential equations (PDEs). Our algorithm estimates simultaneously by backward time induction the solution and its gradient by multi-layer neural networks, through a sequence of learning problems obtained from the minimization of suitable quadratic loss functions and training simulations. This methodology extends to...

Efficient volatility estimation in a two-factor model - Olivier Féron, Pierre Gruet, and Marc Hoffmann

We statistically analyse a multivariate HJM diffusion model with stochastic volatility. The volatility process of the first factor is left totally unspecified while the volatility of the second factor is the product of an unknown process and an exponential function of time to maturity. This exponential term includes some real parameter measuring the rate...

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