大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data

Yacine Ait-Sahalia (Princeton University)

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4. Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data

大阪大学 数理・データ科学セミナー 金融・保険セミナーシリーズ 第76回

Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data

Yacine Ait-Sahalia (Princeton University)

This paper constructs an estimator for the number of common factors in a setting where both the sampling frequency and the number of variables increase. Empirically, we document that the covariance matrix of a large portfolio of US equities is well represented by a low rank common structure with sparse residual matrix. When employed for out-of-sample portfolio allocation, the proposed estimator largely outperforms the sample covariance estimator.

Keywords: High-dimensional data, high-frequency, latent factor model, principal components, portfolio optimization