High-dimensional latent panel quantile regression with an application to asset pricing
高维潜在面板分位数回归及其资产定价应用AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确提出面板条件分位数模型，并赋予特征加潜在因子的分位数资产定价解释，直接支持这些分类。
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高维潜在面板分位数回归及其资产定价应用
- 研究问题
- 高维面板的条件分位数同时受到稀疏协变量和稠密潜在因子影响时，如何联合估计系数与低秩成分并分析资产定价？
- 方法与思路
- 作者将分位数损失与 L1、核范数正则化结合，用 ADMM 求解稀疏系数和低秩矩阵，并应用于高维金融面板。
- 材料陈述的贡献
- 摘要称在允许时间依赖和厚尾的条件下一致估计两部分；应用发现控制因子后特征预测更稀疏，尾部分位与中位不同。
- 阅读关注点
- 应核对稀疏项与低秩项的可区分条件、时间依赖和厚尾限制、正则化选择，以及资产定价解释与经验比较的依据。
- 阅读边界
- 依据版本未注明的 OpenAlex 摘要；具体一致性条件、速率与金融数据细节未给出，不能判断预测提升或因果效应。
原文摘要
We propose a generalization of the linear panel quantile regression model to accommodate both sparse and dense parts: sparse means that while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings. Such a structure poses problems for traditional sparse estimators, such as the ℓ1-penalized quantile regression, and for traditional latent factor estimators such as PCA. We propose a new estimation procedure, based on the ADMM algorithm, that consists of combining the quantile loss function with ℓ1 and nuclear norm regularization. We show, under general conditions, that our estimator can consistently estimate both the nonzero coefficients of the covariates and the latent low-rank matrix. This is done in a challenging setting that allows for temporal dependence, heavy-tail distributions and the presence of latent factors. Our proposed model has a “Characteristics + Latent Factors” Quantile Asset Pricing Model interpretation: we apply our model and estimator with a large-dimensional panel of financial data and find that (i) characteristics have sparser predictive power once latent factors were controlled and (ii) the factors and coefficients at upper and lower quantiles are different from the median.