标题： 一种使用组合分位数方法的数据自适应因子模型 显示英文标题

标题： A Data-Adaptive Factor Model Using Composite Quantile Approach

摘要： 本文提出了一种数据自适应因子模型（DAFM），这是一种新颖的框架，用于提取解释高维数据结构的共同因子。 DAFM采用复合分位数策略，以自适应方式捕捉数据的完整分布结构，从而提高估计精度并揭示传统因子模型无法察觉的潜在模式。 在本文中，我们通过最小化基于分位数上检查函数加权平均的目标函数，开发了一个用于估计DAFM的实际算法。 我们还建立了估计量的理论性质，包括其一致性及收敛速度。 此外，我们通过引入核平滑目标函数的近似估计量，推导了它们的渐近分布，并提出了两种确定因子数量的一致性方法。 模拟研究显示，在不同数据分布下，DAFM均优于现有的因子模型，对波动率和预测的真实数据分析进一步验证了其有效性。 显示英文摘要

摘要： This paper proposes a data-adaptive factor model (DAFM), a novel framework for extracting common factors that explain the structures of high-dimensional data. DAFM adopts a composite quantile strategy to adaptively capture the full distributional structure of the data, thereby enhancing estimation accuracy and revealing latent patterns that are invisible to conventional factor models. In this paper, we develop a practical algorithm for estimating DAFM by minimizing an objective function based on a weighted average of check functions across quantiles. We also establish the theoretical properties of the estimators, including their consistency and convergence rates. Furthermore, we derive their asymptotic distributions by introducing approximated estimators from a kernel-smoothed objective function, and propose two consistent methods for determining the number of factors. Simulation studies demonstrate that DAFM outperforms existing factor models across different data distributions, and real data analyses on volatility and forecasting further validate its effectiveness.