Large-Dimensional Factor Modeling Based on High-Frequency Observations
Large-Dimensional Factor Modeling Based on High-Frequency Observations
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DOI：
10.2139/ssrn.2584172
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发表时间：
2018-05
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影响因子：
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通讯作者：
Markus Pelger
中科院分区：
文献类型：
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作者：
Markus Pelger
本文发展了一种基于金融高频数据的未知因子结构估计的统计理论。我们推导出一个估计的因素和一致的和渐近混合正态估计的负载和因素的假设下，大量的横截面和高频观测的数量。该估计方法可以分离连续和罕见跳跃风险的因素。载荷和因子的估计是基于二次协变矩阵的主成分分析。因子数的估计量使用扰动特征值比统计量。在对标准普尔500指数公司的实证分析中，我们估计了四个稳定的连续系统性因素，这些因素可以很好地近似于市场和行业投资组合。跳跃因子不同于连续因子。
This paper develops a statistical theory to estimate an unknown factor structure based on financial high-frequency data. We derive an estimator for the number of factors and consistent and asymptotically mixed-normal estimators of the loadings and factors under the assumption of a large number of cross-sectional and high-frequency observations. The estimation approach can separate factors for continuous and rare jump risk. The estimators for the loadings and factors are based on the principal component analysis of the quadratic covariation matrix. The estimator for the number of factors uses a perturbed eigenvalue ratio statistic. In an empirical analysis of the S&P 500 firms we estimate four stable continuous systematic factors, which can be approximated very well by a market and industry portfolios. Jump factors are different from the continuous factors.