Enhanced optimal tracking error portfolio via quantile regression with SSD constraints
提出一种结合分位数回归偏差度量与线性二阶随机占优约束的增强型指数跟踪模型，在控制跟踪误差尾部风险的同时提升组合收益分布，实证表明在受限投资集下风险收益表现更优。
Abstract Constructing an index-tracking portfolio involves closely replicating the performance of a benchmark index while minimizing deviations from it. In this paper, we propose a novel enhanced index tracking model that combines a quantile-regression-based deviation measure with linear second-order stochastic dominance (SSD) constraints. The objective is to control the tail risk of the tracking error while guaranteeing an enhancement of the portfolio return distribution relative to the benchmark. The proposed formulation leads to a linear optimization problem that remains computationally tractable under realistic portfolio constraints. The model is empirically evaluated using real-world data to assess the contribution of SSD constraints and to compare its performance with that of classical quantile regression. The empirical results show that both models outperform the benchmark index. However, when the investable universe is restricted through preselection, the proposed model delivers significant improvements in risk-return performance and tail-risk control.