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Found 6 papers, 0 papers with code
Primal and dual optimal stopping with signatures
We propose two signature-based methods to solve the optimal stopping problem - that is, to price American options - in non-Markovian frameworks.
Primal-dual regression approach for Markov decision processes with general state and action space
As a result, our method allows for the construction of tight upper and lower biased approximations of the value functions, and, provides tight approximations to the optimal policy.
A Reproducing Kernel Hilbert Space approach to singular local stochastic volatility McKean-Vlasov models
Motivated by the challenges related to the calibration of financial models, we consider the problem of numerically solving a singular McKean-Vlasov equation $$ d X_t= \sigma(t, X_t) X_t \frac{\sqrt v_t}{\sqrt {E[v_t|X_t]}}dW_t, $$ where $W$ is a Brownian motion and $v$ is an adapted diffusion process.
From optimal martingales to randomized dual optimal stopping
As a main feature, in a possibly large family of optimal martingales the algorithm efficiently selects a martingale that is as close as possible to the Doob martingale.
Probability Optimization and Control Computational Finance 91G60, 65C05, 60G40
Reinforced optimal control
Least squares Monte Carlo methods are a popular numerical approximation method for solving stochastic control problems.
Optimal stopping via reinforced regression
In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression based Monte Carlo algorithms.
