Nearest comoment estimation with unobserved factors

Kris Boudt; Dries Cornilly; Tim Verdonck

doi:10.1016/j.jeconom.2019.12.009

Nearest comoment estimation with unobserved factors

Kris Boudt
, Dries Cornilly^*
, Tim Verdonck

^*Corresponding author for this work

Econometrics and Data Science

Research output: Contribution to Journal › Article › Academic › peer-review

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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.

Original language	English
Pages (from-to)	381-397
Journal	Journal of Econometrics
Volume	217
Issue number	2
Early online date	7 Jan 2020
DOIs	https://doi.org/10.1016/j.jeconom.2019.12.009
Publication status	Published - Aug 2020

Funding

We thank the Editor (Jeroen Rombouts), two anonymous referees and seminar participants at CREST, ETH Zürich and Vrije Universiteit Amsterdam for their valuable comments. We also benefited from fruitful discussions with participants at the CMStatistics, JSM and R/Finance conferences. We gratefully acknowledge support from the Research Foundation — Flanders (FWO (Belgium) research grant G023815N and PhD fellowship 1114119N) and the Internal Funds KU Leuven, Belgium (project C16∕15∕068). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the FWO (Belgium) and the Flemish Government — Department EWI, Belgium. We thank the Editor (Jeroen Rombouts), two anonymous referees and seminar participants at CREST, ETH Zürich and Vrije Universiteit Amsterdam for their valuable comments. We also benefited from fruitful discussions with participants at the CMStatistics, JSM and R/Finance conferences. We gratefully acknowledge support from the Research Foundation — Flanders ( FWO (Belgium) research grant and PhD fellowship ) and the Internal Funds KU Leuven, Belgium (project ). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the FWO (Belgium) and the Flemish Government — Department EWI, Belgium .

Funders	Funder number
Flemish Government — Department EWI
Vlaams Supercomputer Centrum
Fonds Wetenschappelijk Onderzoek	1114119N, G023815N
KU Leuven	C16∕15∕068

Keywords

Higher-order multivariate moments
Latent factor model
Minimum distance estimation
Risk assessment
Structural equation modelling

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10.1016/j.jeconom.2019.12.009

Nearest comoment estimation with unobserved factorsFinal published version, 866 KBLicence: Article 25fa Dutch Copyright Act

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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.",

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KW - Higher-order multivariate moments

KW - Latent factor model

KW - Minimum distance estimation

KW - Risk assessment

KW - Structural equation modelling

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ER -

Nearest comoment estimation with unobserved factors

Abstract

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Keywords

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