Nearest Comoment Estimation With Unobserved Factors

Kris Boudt, Dries Cornilly, Tim Verdonck

Onderzoeksoutput: Articlepeer review

6 Citaten (Scopus)

Samenvatting

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 in uence 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.
Originele taal-2English
Pagina's (van-tot)381-397
Aantal pagina's17
TijdschriftJournal of Econometrics
Volume217
Nummer van het tijdschrift2
DOI's
StatusPublished - aug 2020

Bibliografische nota

Funding Information:
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.

Funding Information:
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 .

Publisher Copyright:
© 2019 Elsevier B.V.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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