Nearest Comoment Estimation with Unobserved Factors

35 Pages Posted: 13 Dec 2017 Last revised: 30 Mar 2019

See all articles by Kris Boudt

Kris Boudt

Ghent University; Vrije Universiteit Brussel; Vrije Universiteit Amsterdam

Dries Cornilly

Vrije Universiteit Brussel (VUB); KU Leuven

Tim Verdonck

KU Leuven

Date Written: March 27, 2019

Abstract

We propose a minimum distance estimator for the higher-order comoments of a multivariate distribution exhibiting a lower dimensional latent factor structure. We derive the influence function of the proposed estimator and prove its consistency and asymptotic normality. The simulation study confirms the large gains in accuracy compared to the traditional sample comoments. The empirical usefulness of the novel framework is shown in applications to portfolio allocation under non-Gaussian objective functions and to the extraction of factor loadings in a dataset with mental ability scores.

Supplementary appendix with code examples: https://ssrn.com/abstract=3269127.

Keywords: Higher-order multivariate moments; latent factor model; minimum distance estimation; risk assessment; structural equation modelling

JEL Classification: C10; C13; C51

Suggested Citation

Boudt, Kris and Cornilly, Dries and Verdonck, Tim, Nearest Comoment Estimation with Unobserved Factors (March 27, 2019). Available at SSRN: https://ssrn.com/abstract=3087336 or http://dx.doi.org/10.2139/ssrn.3087336

Kris Boudt

Ghent University ( email )

Sint-Pietersplein 5
Gent, 9000
Belgium

Vrije Universiteit Brussel ( email )

Pleinlaan 2
http://www.vub.ac.be/
Brussels, 1050
Belgium

Vrije Universiteit Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV
Netherlands

Dries Cornilly (Contact Author)

Vrije Universiteit Brussel (VUB) ( email )

Pleinlaan 2
Brussels, Brussels 1050
Belgium

KU Leuven

Celestijnenlaan 200B
Leuven, Vlaams-Brabant 3001
Belgium

Tim Verdonck

KU Leuven ( email )

Celestijnenlaan 200B
Leuven, 3001
Belgium

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