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

Author

Listed:
  • Rongju Zhang
  • Nicolas Langren'e
  • Yu Tian
  • Zili Zhu
  • Fima Klebaner
  • Kais Hamza

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.

Suggested Citation

  • Rongju Zhang & Nicolas Langren'e & Yu Tian & Zili Zhu & Fima Klebaner & Kais Hamza, 2018. "Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization," Papers 1803.11467, arXiv.org, revised Sep 2018.
    • Handle: RePEc:arx:papers:1803.11467
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    References listed on IDEAS

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