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

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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.

Date: 2018-03, Revised 2018-09
New Economics Papers: this item is included in nep-cmp
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