Economics at your fingertips  

Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization

Rongju Zhang, Nicolas Langren\'e, Yu Tian, Zili Zhu, Fima Klebaner and Kais Hamza

Papers from

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.

New Economics Papers: this item is included in nep-cmp
Date: 2018-03, Revised 2018-09
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link) Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Papers from
Bibliographic data for series maintained by arXiv administrators ().

Page updated 2018-09-12
Handle: RePEc:arx:papers:1803.11467