英文标题：
《Local Control Regression: Improving the Least Squares Monte Carlo Method
for Portfolio Optimization》
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作者：
Rongju Zhang, Nicolas Langren\\\'e, Yu Tian, Zili Zhu, Fima Klebaner,
Kais Hamza
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最新提交年份：
2018
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英文摘要：
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.
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中文摘要：
最小二乘蒙特卡罗算法已成为解决投资组合优化问题的流行算法。一种简单的方法是在投资组合权重的离散网格上近似值函数，然后使用控制回归来推广离散估计。然而，经典的全局控制回归可能代价高昂且不准确。为了克服这个困难，我们引入了一种结合自适应网格的局部控制回归技术。我们表明，为局部回归选择粗网格可以产生足够精确的结果。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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