Large-dimensional factor modeling based on high-frequency observations

By Markus Pelger. Journal of Econometrics, 2019

104 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/9c0efd746fa8b26f0f55b39e245f2127b71e2fbe

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Citations in the corpus

Understanding Systematic Risk: A High‐Frequency Approach (2020, unknown): “The capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965) with the market as the only common factor falls into this category.”
Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019, unknown): “, 2018), a local window or high frequency approach (Pelger, 2019; Aı̈t-Sahalia and Xiu, 2018) or a kernel projection in the time dimension (Pelger and Xiong, 2018).”
Principal Component Analysis of High-Frequency Data (2016, unknown): “…continuous-time models with a factor structure allowing for an increasing dimensionality: see Fan, Furger, and Xiu (2016) and Aït-Sahalia and Xiu (2017) for using factor analysis for covariance estimation and Pelger (2015a) and Pelger (2015b) for estimating factors and their loadings.”
State-Varying Factor Models of Large Dimensions (2018, unknown): “From the proof of Theorem 2 and Theorem 3, and similar to Pelger (2019a), we have uli1 = 1 N N ∑”
Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, background): “Independently, Pelger (2015a) and Pelger (2015b) propose an alternative estimator for the number of factors and factor loadings, with a distributional theory that is entry-wise, whereas the present paper concentrates on the matrix-wise asymptotic properties of the covariance matrix and its inverse.”
A diagnostic criterion for approximate factor structure (2016, unknown): “We see that the scaled three-factor FF model with Zt−1 = (1, divYt−1)′ passes the diagnostic criterion.”