This paper develops a canonical-correlation-based method for detecting structural changes in high-dimensional transformed factor models. The proposed approach exploits the low-rank canonical-correlation structure induced by dynamically dependent common factors, while serially uncorrelated idiosyncratic components correspond to a noise subspace with zero canonical correlations. We construct an eigenvalue-ratio criterion that measures residual dynamic dependence in the estimated noise subspace and identifies the true change point under sufficient separation of the regime-specific loading spaces or dynamic canonical correlation structures. Since the change-point location and the regime-specific factor numbers are both unknown, we further propose an alternating iterative estimation procedure that updates them sequentially until convergence. Under suitable mixing and moment conditions, we establish asymptotic properties of the proposed estimators, with convergence rates depending explicitly on factor strength, cross-sectional dimension, and sample size. Monte Carlo experiments and empirical applications to intraday stock returns and U.S. temperature series demonstrate the finite-sample

翻译：本文提出了一种基于典型相关的方法，用于检测高维变换因子模型中的结构变化。所提出的方法利用了由动态依赖的共同因子诱导的低秩典型相关结构，而序列不相关的异质成分对应于具有零典型相关性的噪声子空间。我们构建了一个特征值比准则，用于在估计的噪声子空间中衡量残差动态依赖，并在区域特定载荷空间或动态典型相关结构充分分离的情况下识别真实变化点。由于变化点位置和区域特定因子数量均未知，我们进一步提出了一种交替迭代估计程序，该程序依次更新它们直至收敛。在合适的混合性和矩条件下，我们建立了所提估计量的渐近性质，其收敛速度明确依赖于因子强度、横截面维度和样本量。蒙特卡洛实验以及对日内股票收益率和美国温度序列的实证应用展示了该方法在有限样本下的性能。