Category Rapports

Juil
2019

Neural networks-based backward scheme for fully nonlinear PDEs - H. Pham, X. 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 ...

Juin
2019

Efficient Volatility Estimation in a Two-factor Model - O. Féron, P. Gruet, and M. 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 ...

Avr
2019

Simulation of fuel poverty in France - Corinne Chaton, Alexandre Gouraud.

The assessment of fuel poverty in mainland France is based mainly on data provided by the French national housing survey (ENL). However, the last two surveys date from 2006 and ...

Avr
2019

Avoiding Fuel Poverty through Insurance -  Corinne Chaton

Twenty percent of French non-fuel poor households will fall into fuel poverty. The existence of energy insurance can reduce this percentage. This article focuses on non-fuel poor households that can ...

Fév
2019

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 ...

Fév
2019

Untangling systemic risk in financialized commodity markets - Julien Ling.

Systemic risk is a multifaceted concept that is of crucial importance for regulators. In order to ensure financial stability, they need to properly assess this risk, preventing financial shocks from ...

Jan
2019

Deep neural networks algorithms for stochastic control problems on finite 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 [11]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely ...

Jan
2019

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

This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming (DP). Diffrently from the classical approximate DP approach, we rst approximate the optimal policy ...

Jan
2019

Optimal electricity demand response contracting with responsiveness incentives - R. Aïd, D. Possamaï, and N. Touzi

Despite the success of demand response programs in retail electricity markets in reducing average consumption, the literature shows failure to reduce the variance of consumers’ responses. This paper aims at ...

Jan
2019

Day-ahead probabilistic forecast of solar irradiance: a Stochastic Differential Equation approach - J. Badosa, E. Gobet, M. Grangereau and D. Kim

In this work, we derive a probabilistic forecast of the solar irradiance during a day at a given location, using a stochastic differential equation (SDE for short) model. We propose ...

Page 4 of 12