Extracting Statistical Factors When Betas are Time-Varying

68 Pages Posted: 30 Jul 2019

See all articles by Patrick Gagliardini

Patrick Gagliardini

USI Università della Svizzera italiana; Swiss Finance Institute

Hao Ma

USI Università della Svizzera italiana; Swiss Finance Institute, Students

Date Written: July 25, 2019

Abstract

This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the way the loadings vary in time as functions of common shocks and individual characteristics. The number of active factors can also be time-varying as an effect of the changing macroeconomic environment. The method deploys Instrumental Variables (IV) which have full-rank covariation with the factor betas in the cross-section. It allows for a large dimension of the vector generating the conditioning information by machine learning techniques. In an empirical application, we infer the conditional factor space in the panel of monthly returns of individual stocks in the CRSP dataset between January 1971 and December 2017.

Keywords: Large Panel, Unobservable Factors, Conditioning Information, Instrumental Variables, Machine Learning, Post-Lasso, Artificial Neural Networks

JEL Classification: G12

Suggested Citation

Gagliardini, Patrick and Ma, Hao, Extracting Statistical Factors When Betas are Time-Varying (July 25, 2019). Available at SSRN: https://ssrn.com/abstract=3426534 or http://dx.doi.org/10.2139/ssrn.3426534

Patrick Gagliardini

USI Università della Svizzera italiana ( email )

Via Buffi 13
Lugano, TN 6900
Switzerland

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Hao Ma (Contact Author)

USI Università della Svizzera italiana ( email )

Via Buffi 13
Lugano, TN 6900
Switzerland

Swiss Finance Institute, Students ( email )

c/o University of Geneva
42, Bd du Pont d'Arve
Geneva, CH-1211
Switzerland

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