Sequential Design for Optimal Stopping Problems
From MaRDI portal
Publication:2941479
DOI10.1137/140980089zbMath1320.91154arXiv1309.3832OpenAlexW3099636734MaRDI QIDQ2941479
Michael Ludkovski, Robert B. Gramacy
Publication date: 28 August 2015
Published in: SIAM Journal on Financial Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1309.3832
Numerical methods (including Monte Carlo methods) (91G60) Stopping times; optimal stopping problems; gambling theory (60G40) Derivative securities (option pricing, hedging, etc.) (91G20) Sequential statistical design (62L05)
Related Items (11)
A Nonintrusive Stratified Resampler for Regression Monte Carlo: Application to Solving Nonlinear Equations ⋮ Mixing LSMC and PDE Methods to Price Bermudan Options ⋮ Discrete-type approximations for non-Markovian optimal stopping problems. II ⋮ Deep learning for ranking response surfaces with applications to optimal stopping problems ⋮ Optimal market-making strategies under synchronised order arrivals with deep neural networks ⋮ A Bayesian optimization approach to find Nash equilibria ⋮ Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation ⋮ Deep learning for limit order books ⋮ Simulation-based Value-at-Risk for nonlinear portfolios ⋮ American options under stochastic volatility: control variates, maturity randomization & multiscale asymptotics ⋮ Sequential Design for Ranking Response Surfaces
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Quantitative error estimates for a least-squares Monte Carlo algorithm for American option pricing
- Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates
- Monte Carlo algorithms for optimal stopping and statistical learning
- Sequential design of computer experiments for the estimation of a probability of failure
- Sequential Monte Carlo pricing of American-style options under stochastic volatility models
- Efficient global optimization of expensive black-box functions
- Valuation of the early-exercise price for options using simulations and nonparametric regression
- An analysis of a least squares regression method for American option pricing
- Number of paths versus number of basis functions in American option pricing
- A distribution-free theory of nonparametric regression
- On the Malliavin approach to Monte Carlo approximation of conditional expectations
- Snell envelope with small probability criteria
- A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options
- Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming
- Monte-Carlo Valuation of American Options: Facts and New Algorithms to Improve Existing Methods
- A Review on Regression-based Monte Carlo Methods for Pricing American Options
- PRICING OF HIGH-DIMENSIONAL AMERICAN OPTIONS BY NEURAL NETWORKS
- Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives
- ON THE CONSISTENCY OF REGRESSION‐BASED MONTE CARLO METHODS FOR PRICING BERMUDAN OPTIONS IN CASE OF ESTIMATED FINANCIAL MODELS
- Dynamic Trees for Learning and Design
- On the Robustness of the Snell Envelope
- A QUANTIZATION TREE METHOD FOR PRICING AND HEDGING MULTIDIMENSIONAL AMERICAN OPTIONS
This page was built for publication: Sequential Design for Optimal Stopping Problems
