Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization
Portfolio Management
2018-09-12 v2 Computational Finance
Abstract
The least squares Monte Carlo algorithm has become popular for solving portfolio optimization problems. A simple approach is to approximate the value functions on a discrete grid of portfolio weights, then use control regression to generalize the discrete estimates. However, the classical global control regression can be expensive and inaccurate. To overcome this difficulty, we introduce a local control regression technique, combined with adaptive grids. We show that choosing a coarse grid for local regression can produce sufficiently accurate results.
Keywords
Cite
@article{arxiv.1803.11467,
title = {Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization},
author = {Rongju Zhang and Nicolas Langrené and Yu Tian and Zili Zhu and Fima Klebaner and Kais Hamza},
journal= {arXiv preprint arXiv:1803.11467},
year = {2018}
}
Comments
10 pages, 4 tables, 2 figures