A Robust Similarity Estimator

Ilya Archakov

Papers from arXiv.org

Abstract: We analyze a measure of statistical association based on the similarity of the outcomes of random variables, in both sign and magnitude. Motivated by its attractive properties, we propose a class of estimators for the linear correlation coefficient, a sample-average and a maximum-likelihood version, that operate directly on the Fisher scale and possess a robust sampling distribution that is invariant over the entire class of elliptical distributions. Under scale homogeneity, the finite-sample distribution of the estimator is available in exact form, facilitating robust inference for correlations even in small samples. The similarity measure extends naturally to higher dimensions, where it admits an interpretation as an indicator of joint similarity among multiple random variables. As empirical applications, we construct robust confidence intervals for financial correlations using intraday returns and develop a new specification of a multivariate GARCH model with robust correlation dynamics.

Date: 2026-01, Revised 2026-07
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2601.12198 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2601.12198

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().