arXiv research

Deep neural networks can solve optimal stopping problems without dimensionality issues.

Continuous-time optimal stopping solved with deep reinforcement learning

The study reveals optimal early stopping behaviors in deep learning models.

A new method uses deep learning for optimal stopping problems.

Neural networks optimize stopping boundaries in financial instruments.

Deep Penalty Method solves high-dimensional optimal stopping problems using deep learning.

A new method solves complex financial problems using deep learning.

New algorithms use Gaussian processes to optimize stopping times in financial markets.

Study on deep neural networks using concentration inequalities and optimal stopping.

Deep Q-Learning models optimal exercise strategies for option-type products.

New method solves optimal stopping problems using rough path signatures.

A deep model learns to stop early based on variational stopping policy.

New methods price American options in rough volatility models.

Paper optimizes aquaculture feeding and harvesting strategies for profit maximization.

A new stopping rule based on E-values helps efficiently use sampling in Bayesian Deep Ensembles.

New method uses neural networks for optimal stopping time problems.

DeepMartingale uses deep learning to solve complex optimal stopping problems efficiently.

Deep learning method for pricing and hedging American-style options.

Deep learning approximates Bermudan option exposures and future values.

Randomized neural networks improve optimal stopping problems efficiently.

Nowadays many financial derivatives, such as American or Bermudan options, are of early exercise type. Often the pricing of early exercise options gives rise to high-dimensional optimal stopping problems, since the dimension corresponds to the number of underlying assets. High-dimensional optimal stopping problems are,…

New method schedules learning rate without stopping time, outperforming existing methods.

This study analyzes AdaGrad's stability and convergence in non-convex optimization.

Gradient-flow optimization is reinterpreted as a statistical inference problem.

New algorithm solves complex stopping problems with robust optimization.

Solves optimal stopping problem with Poisson constraints using jumps.

In this work we consider optimal stopping problems with conditional convex risk measures called optimised certainty equivalents. Without assuming any kind of time-consistency for the underlying family of risk measures, we derive a novel representation for the solution of the optimal stopping problem. In particular, we …

RankNet forecasts car racing positions with improved accuracy and stability.

DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.

Solves optimal stopping for Gauss-Markov bridges using time-space transformation.

Dual martingales improve primal optimal stopping problem efficiency.

Study on randomized algorithms for optimal stopping problems.

NTK neural networks are robust to adversarial attacks in nonparametric regression.

The paper tackles optimal stopping problems using reinforcement learning and singular control.

Trailing stop is a popular stop-loss trading strategy by which the investor will sell the asset once its price experiences a pre-specified percentage drawdown. In this paper, we study the problem of timing buy and then sell an asset subject to a trailing stop. Under a general linear diffusion framework, we study an opt…

Study optimal stopping in random exploration, deriving HJB and designing a reinforcement learning algorithm.

New method finds optimal training stop point with noisy labeled data.

Study optimal stopping times under regime-switching models with constraints.

We use probabilistic methods to characterise time dependent optimal stopping boundaries in a problem of multiple optimal stopping on a finite time horizon. Motivated by financial applications we consider a payoff of immediate stopping of "put" type and the underlying dynamics follows a geometric Brownian motion. The op…

We show, under weaker assumptions than in the previous literature, that a perpetual optimal stopping game always has a value. We also show that there exists an optimal stopping time for the seller, but not necessarily for the buyer. Moreover, conditions are provided under which the existence of an optimal stopping time…

This paper analyzes the problem of starting and stopping a Cox-Ingersoll-Ross (CIR) process with fixed costs. In addition, we also study a related optimal switching problem that involves an infinite sequence of starts and stops. We establish the conditions under which the starting-stopping and switching problems admit …

MUSE provides unbiased stopping estimates for optimal problems.

Study optimal stopping problems with finite-time horizon and proves continuity and strict monotonicity of the boundary.

Develops a method for solving optimal stopping problems with multiple exercise rights.

The performance of deep neural networks (DNN) is very sensitive to the particular choice of hyper-parameters. To make it worse, the shape of the learning curve can be significantly affected when a technique like batchnorm is used. As a result, hyperparameter optimization of deep networks can be much more challenging th…

Study optimal stopping times for multi-dimensional processes with non-exponential discounting.

We consider the optimal double stopping time problem defined for each stopping time $S$ by $v(S)=\esssup\{E[ψ(τ_1, τ_2) | \F_S], τ_1, τ_2 \geq S \}$. Following the optimal one stopping time problem, we study the existence of optimal stopping times and give a method to compute them. The key point is the construction of …

This work bounds the run-time of nonconvex optimization with early stopping.