Nearest comoment estimation with unobserved factors

Kris Boudt, Dries Cornilly*, Tim Verdonck

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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 languageEnglish
Pages (from-to)381-397
JournalJournal of Econometrics
Volume217
Issue number2
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

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

Fingerprint Dive into the research topics of 'Nearest comoment estimation with unobserved factors'. Together they form a unique fingerprint.

  • Cite this