Regression Monte Carlo for impulse control
type: Item
instance of: scholarly article
title: Regression Monte Carlo for impulse control
author: Michael Ludkovski
published in: MathematicS In Action
publication date: 2022-11-08T00:00:00Z
full work available at URL: https://arxiv.org/abs/2203.06539
review text: Impulse control problems are difficult to be solved by the classical techniques using quasi-variational inequalities, because of the non-local term in the equation. The numerical algorithm in the present paper is of probabilistic-statistical type, inspired by Monte Carlo-techniques initially developed for stopping problems. It is based on the dynamic programming equation, which is discretized and solved backwards, the continuation function and the optimal impulse function being both statistically estimated through the simulation of state trajectories. Several implementations are discussed, and the algorithm is tested on two examples, treating forest rotation and irreversible investment. A package for R is publicly available on GitHub.
Mathematics Subject Classification ID: 93C27
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 93E20
Mathematics Subject Classification ID: 49N25
zbMATH DE Number: 7613646
zbMATH Keywords: impulse control
zbMATH Keywords: statistical surrogates
zbMATH Keywords: irreversible investment
zbMATH Keywords: regression Monte Carlo
reviewed by: Catherine Rainer
describes a project that uses: R
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
OpenAlex ID: W4297324064
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DOI: 10.5802/MSIA.18
description: scientific article; zbMATH DE number 7613646