Estimation of Large Dimensional Conditional Factor Models in Finance

Gagliardini, Patrick; Ossola, Elisa; Scaillet, Olivier

doi:10.2139/ssrn.3443426

Cited by 5 publications

(3 citation statements)

References 185 publications

(127 reference statements)

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“…Suppose that U it are iid random variables over time (independent of any object as the above). In Gagliardini, Ossola, and Scaillet (2019), they assume,…”

Section: Define Rmentioning

confidence: 99%

“…is smooth over t. Furthermore the smoothness assumption on Z t−1 and Z t−1 (U it ) over t is required for us and but NOT for required for Gagliardini, Ossola, and Scaillet (2019). However, the structure in Equation (1.5) is not necessary for us, therefore there exist models where we can cover but not covered by Gagliardini, Ossola, and Scaillet (2019). For example if we set, β t is a vector of β it .…”

Section: Define Rmentioning

confidence: 99%

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Beta-Sorted Portfolios

Cattaneo¹,

Crump²,

Wang³

2023

Staff Reports (Federal Reserve Bank of New York)

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Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalizes the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process. We provide conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and general hypothesis testing for financial applications. We show that the rate of convergence of the estimator is non-uniform and depends on the beta value of interest. We also show that the widely used Fama-MacBeth variance estimator is asymptotically valid but is conservative in general and can be very conservative in empirically relevant settings. We propose a new variance estimator, which is always consistent and provide an empirical implementation which produces valid inference. In our empirical application we introduce a novel risk factor—a measure of the business credit cycle—and show that it is strongly predictive of both the cross-section and time-series behavior of U.S. stock returns.

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“…Suppose that U it are iid random variables over time (independent of any object as the above). In Gagliardini, Ossola, and Scaillet (2019), they assume,…”

Section: Define Rmentioning

confidence: 99%

Section: Define Rmentioning

confidence: 99%

Beta-Sorted Portfolios

Cattaneo¹,

Crump²,

Wang³

2023

Staff Reports (Federal Reserve Bank of New York)

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“…Several excellent reviews have been written with emphasis on these topics. We refer readers to the reviews by Stock & Watson (2016) for dynamic factor models with applications on macroeconomics, Bai & Wang (2016) for time series and panel data models, and Gagliardini, Ossola & Scaillet (2019) for a recent review on conditional factor models with applications to finance. Another class of estimation is a hybrid of the principal components analysis (PCA) method and the state space approach (for more discussions on this topic, see Giannone, Reichlin & Small 2008;Doz, Giannone & Reichlin 2011).…”

Section: Introductionmentioning

confidence: 99%

Recent Developments in Factor Models and Applications in Econometric Learning

Fan

Liao

2021

Annu. Rev. Financ. Econ.

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This article provides a selective overview of the recent developments in factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models and particularly draw attention to estimating the model from the low-rank recovery point of view. Our survey mainly consists of three parts. The first part is a review of new factor estimations based on modern techniques for recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and their applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.

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