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The least squares Monte Carlo algorithm has become popular for solvingportfolio optimization problems. A simple approach is to approximate the valuefunctions on a discrete grid of portfolio weights, then use control regressionto generalize the discrete estimates. However, the classical global controlregression can be expensive and inaccurate. To overcome this difficulty, weintroduce a local control regression technique, combined with adaptive grids.We show that choosing a coarse grid for local regression can producesufficiently accurate results.

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