Nonparametric mean-lower partial moment model and enhanced index investment
Nonparametric mean-lower partial moment model and enhanced index investment
复制标题
非参数均值下偏矩模型和增强型指数投资
DOI：
10.1016/j.cor.2022.105814
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发表时间：
2022
影响因子：
4.6
通讯作者：
姚海祥
中科院分区：
文献类型：
--
作者：
黄金波;Yong Li;姚海祥
在本研究中，我们提出了一种新的平滑非参数核 (NPK) 方法来估计通过下偏矩 (LPM) 测量的下行风险，并构建 NPK 均值 LPM 模型。我们还使用 NPK LPM 构建了一个新颖的增强指数模型来控制下行风险。我们从理论上证明，当 LPM 阶数等于或大于 1 时，NPK LPM 估计器是凸函数。数值结果表明，我们的 NPK 方法在估计精度方面优于矩方法。我们的模拟实验和实证测试表明，当使用多种绩效指标和不同的再平衡策略时，基于 NPK LPM 的增强指数模型优于基准模型和指数。
In this study, we propose a new smooth nonparametric kernel (NPK) method to estimate downside risk as measured by the lower partial moment (LPM) and build a NPK mean-LPM model. We also use the NPK LPM to construct a novel enhanced index model for controlling downside risk. We theoretically prove that the NPK LPM estimator is a convex function when the order of the LPM is equal to or greater than 1. The numerical results show that our NPK method outperforms the moment method in terms of estimation accuracy. Our simulated experiments and empirical tests show that the NPK LPM-based enhanced index model outperforms the benchmark models and indexes when using a wide variety of performance indicators and different rebalancing strategies.