发表机构
Tiangong University; Fujian Normal University（天津工业大学; 福建师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高维因子定价模型中岭参数选择的不确定性，提出基于柯西规则聚合岭特定p值的检验方法，保留最小二乘估计，理论推导渐近性质，模拟显示功效优于固定岭基准。
AI 中文摘要
在高维因子定价模型中，正则化alpha检验的功效取决于岭参数，而该参数的最优值随定价误差的未知方向而变化。我们通过使用柯西规则结合特定岭的p值来解决这一调参不确定性。所得检验保留了最小二乘alpha估计量和残差样本协方差，并能容纳比观测值更多的资产。我们在原假设和局部备择假设下建立了分量统计量的联合高斯极限，并推导了跨岭参数的显式协方差公式。这些结果刻画了组合检验的渐近分布和局部功效，并证明了其尾部校准的合理性。模拟显示经验拒绝率接近名义水平，且相对于固定岭基准有功效提升，在模拟设置中功效接近信号知情的岭基准。
英文摘要
In high-dimensional factor pricing models, the power of regularized alpha tests depends on the ridge parameter, whose optimal value varies with the unknown direction of pricing errors. We address this tuning uncertainty by combining ridge-specific p-values using the Cauchy rule. The resulting test retains the least-squares alpha estimator and residual sample covariance and accommodates more assets than observations. We establish the joint Gaussian limits of the component statistics under the null and local alternatives and derive an explicit covariance formula across ridge parameters. These results characterize the combined test's asymptotic distribution and local power and justify its tail calibration. Simulations show empirical rejection rates close to the nominal levels and power gains over the fixed-ridge benchmark, with power approaching that of signal-informed ridge benchmarks across the simulated settings.
