no code implementations • 30 Mar 2024 • Zhaofeng Zhang, Banghao Chen, Shengxin Zhu, Nicolas Langrené
In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge.
no code implementations • 23 Oct 2023 • Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu
This comprehensive review explores the transformative potential of prompt engineering within the realm of large language models (LLMs) and multimodal language models (MMLMs).
1 code implementation • 24 Feb 2023 • Ivan Guo, Nicolas Langrené, Jiahao Wu
In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks.
no code implementations • 20 Feb 2021 • Seyedali Meghdadi, Guido Tack, Ariel Liebman, Nicolas Langrené, Christoph Bergmeir
To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics.
no code implementations • 20 Jul 2020 • Wen Chen, Nicolas Langrené
In this paper, we develop a deep neural network approach to solve a lifetime expected mortality-weighted utility-based model for optimal consumption in the decumulation phase of a defined contribution pension system.
no code implementations • 2 Jun 2020 • Kaustav Das, Nicolas Langrené
We obtain an explicit approximation formula for European put option prices within a general stochastic volatility model with time-dependent parameters.
no code implementations • 19 Dec 2018 • 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.
no code implementations • 13 Dec 2018 • Achref Bachouch, Côme Huré, Nicolas Langrené, Huyen Pham
This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18].
no code implementations • 11 Dec 2018 • Côme Huré, Huyên Pham, Achref Bachouch, Nicolas Langrené
This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming.