97 citations · 329 across the 13 of their papers we have counts for
13 papers
Signed random Fourier features for fast density estimation with indefinite kernels
Xie Wang, Nicolas Langrené, Wen Chen
Kernel density estimation (KDE) is one of the most fundamental statistical estimators of density functions. Its direct implementation on a dataset of points incurs an $\mathcal…
Deep Least Squares Monte Carlo methods for the valuation of variable annuities with guarantees
Nicolas Langrené, Xiaolin Luo, Pavel V. Shevchenko +1
In general, the pricing of variable annuities with guarantees can be done by solving the corresponding optimal stochastic control problem if the contract withdrawal strategy is ass…
A deep learning approach for pricing convertible bonds with path-dependent reset and call provisions
Qinwen Zhu, Wen Chen, Nicolas Langrené
This paper develops a deep learning framework for pricing convertible bonds with path-dependent downward reset and issuer call provisions governed by rolling-window triggers. We fo…
Robust utility maximization under model uncertainty via a penalization approach
Ivan Guo, Nicolas Langrené, Grégoire Loeper +1
This paper addresses the problem of utility maximization under uncertain parameters. In contrast with the classical approach, where the parameters of the model evolve freely within…
Closed-form approximations with respect to the mixing solution for option pricing under stochastic volatility
Kaustav Das, Nicolas Langrené
We consider closed-form approximations for European put option prices within the Heston and GARCH diffusion stochastic volatility models with time-dependent parameters. Our methodo…
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications
Achref Bachouch, Côme Huré, Nicolas Langrené +1
This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18]. Numerical and comparative tests using TensorFlow illustr…
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis
Côme Huré, Huyên Pham, Achref Bachouch +1
This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming. Unlike classical approximate dynamic programming app…
Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization
Rongju Zhang, Nicolas Langrené, Yu Tian +3
The least squares Monte Carlo algorithm has become popular for solving portfolio optimization problems. A simple approach is to approximate the value functions on a discrete grid o…
Fast and stable multivariate kernel density estimation by fast sum updating
Nicolas Langrené, Xavier Warin
Kernel density estimation and kernel regression are powerful but computationally expensive techniques: a direct evaluation of kernel density estimates at evaluation points give…
Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method
Rongju Zhang, Nicolas Langrené, Yu Tian +3
In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted ra…
Switching to non-affine stochastic volatility: A closed-form expansion for the Inverse Gamma model
Nicolas Langrené, Geoffrey Lee, Zili Zhu
This paper introduces the Inverse Gamma (IGa) stochastic volatility model with time-dependent parameters, defined by the volatility dynamics $dV_{t}=κ_{t}\left(θ_{t}-V_{t}\right)dt…
A numerical algorithm for fully nonlinear HJB equations: an approach by control randomization
Idris Kharroubi, Nicolas Langrené, Huyên Pham
We propose a probabilistic numerical algorithm to solve Backward Stochastic Differential Equations (BSDEs) with nonnegative jumps, a class of BSDEs introduced in [9] for representi…
A probabilistic numerical method for optimal multiple switching problem and application to investments in electricity generation
René Aïd, Luciano Campi, Nicolas Langrené +1
In this paper, we present a probabilistic numerical algorithm combining dynamic programming, Monte Carlo simulations and local basis regressions to solve non-stationary optimal mul…