Recent developments in machine learning methods for stochastic control and games
R Hu, M Lauriere - arXiv preprint arXiv:2303.10257, 2023 - arxiv.org
Stochastic optimal control and games have a wide range of applications, from finance and
economics to social sciences, robotics, and energy management. Many real-world …
economics to social sciences, robotics, and energy management. Many real-world …
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential …
High-dimensional partial differential equations (PDEs) appear in a number of models from
the financial industry, such as in derivative pricing models, credit valuation adjustment …
the financial industry, such as in derivative pricing models, credit valuation adjustment …
Monte-Carlo valuation of American options: facts and new algorithms to improve existing methods
B Bouchard, X Warin - Numerical Methods in Finance: Bordeaux, June …, 2012 - Springer
The aim of this paper is to discuss efficient algorithms for the pricing of American options by
two recently proposed Monte-Carlo type methods, namely the Malliavian calculus and the …
two recently proposed Monte-Carlo type methods, namely the Malliavian calculus and the …
[图书][B] Least-squares monte carlo for backward sdes
C Bender, J Steiner - 2012 - Springer
In this paper we first give a review of the least-squares Monte Carlo approach for
approximating the solution of backward stochastic differential equations (BSDEs) first …
approximating the solution of backward stochastic differential equations (BSDEs) first …
Time-consistent mean-variance portfolio selection in discrete and continuous time
C Czichowsky - Finance and Stochastics, 2013 - Springer
It is well known that mean-variance portfolio selection is a time-inconsistent optimal control
problem in the sense that it does not satisfy Bellman's optimality principle and therefore the …
problem in the sense that it does not satisfy Bellman's optimality principle and therefore the …
Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks
Parabolic partial differential equations (PDEs) are widely used in the mathematical modeling
of natural phenomena and man-made complex systems. In particular, parabolic PDEs are a …
of natural phenomena and man-made complex systems. In particular, parabolic PDEs are a …
Numerical methods for backward stochastic differential equations: A survey
J Chessari, R Kawai, Y Shinozaki… - Probability Surveys, 2023 - projecteuclid.org
Abstract Backward Stochastic Differential Equations (BSDEs) have been widely employed in
various areas of social and natural sciences, such as the pricing and hedging of financial …
various areas of social and natural sciences, such as the pricing and hedging of financial …
[PDF][PDF] From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs
The numerical approximation of partial differential equations (PDEs) poses formidable
challenges in high dimensions since classical grid-based methods suffer from the so-called …
challenges in high dimensions since classical grid-based methods suffer from the so-called …
A numerical method and its error estimates for the decoupled forward-backward stochastic differential equations
In this paper, a new numerical method for solving the decoupled forward-backward
stochastic differential equations (FBSDEs) is proposed based on some specially derived …
stochastic differential equations (FBSDEs) is proposed based on some specially derived …
Stochastic differential utility as the continuous-time limit of recursive utility
H Kraft, FT Seifried - Journal of Economic Theory, 2014 - Elsevier
Stochastic differential utility as the continuous-time limit of recursive utility - ScienceDirect Skip
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