Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Frikha, Noufel"'
Cr\'epey, Frikha, and Louzi (2023) introduced a multilevel stochastic approximation scheme to compute the value-at-risk of a financial loss that is only simulatable by Monte Carlo. The optimal complexity of the scheme is in $O({\varepsilon}^{-5/2})$,
Externí odkaz:
http://arxiv.org/abs/2408.06531
We consider reinforcement learning (RL) methods for finding optimal policies in linear quadratic (LQ) mean field control (MFC) problems over an infinite horizon in continuous time, with common noise and entropy regularization. We study policy gradien
Externí odkaz:
http://arxiv.org/abs/2408.02489
Cr\'epey, Frikha, and Louzi (2023) introduced a nested stochastic approximation algorithm and its multilevel acceleration to compute the value-at-risk and expected shortfall of a random financial loss. We hereby establish central limit theorems for t
Externí odkaz:
http://arxiv.org/abs/2311.15333
In line with the methodology introduced in our recent article for formulating probabilistic representations of integration by parts involving killed diffusion, we establish an integration by parts formula for the first exit time of one-dimensional di
Externí odkaz:
http://arxiv.org/abs/2310.07266
We propose a multilevel stochastic approximation (MLSA) scheme for the computation of the value-at-risk (VaR) and expected shortfall (ES) of a financial loss, which can only be computed via simulations conditional on the realization of future risk fa
Externí odkaz:
http://arxiv.org/abs/2304.01207
We study policy gradient for mean-field control in continuous time in a reinforcement learning setting. By considering randomised policies with entropy regularisation, we derive a gradient expectation representation of the value function, which is am
Externí odkaz:
http://arxiv.org/abs/2303.06993
We propose a new probabilistic scheme which combines deep learning techniques with high order schemes for backward stochastic differential equations belonging to the class of Runge-Kutta methods to solve high-dimensional semi-linear parabolic partial
Externí odkaz:
http://arxiv.org/abs/2212.14372
Relying on the classical connection between Backward Stochastic Differential Equations (BSDEs) and non-linear parabolic partial differential equations (PDEs), we propose a new probabilistic learning scheme for solving high-dimensional semi-linear par
Externí odkaz:
http://arxiv.org/abs/2102.12051
In this paper, we establish a probabilistic representation as well as some integration by parts formulae for the marginal law at a given time maturity of some stochastic volatility model with unbounded drift. Relying on a perturbation technique for M
Externí odkaz:
http://arxiv.org/abs/2011.10453
Autor:
Frikha, Noufel, Li, Libo
In this article, we are interested in the strong well-posedness together with the numerical approximation of some one-dimensional stochastic differential equations with a non-linear drift, in the sense of McKean-Vlasov, driven by a spectrally-positiv
Externí odkaz:
http://arxiv.org/abs/2001.07505