Economics at your fingertips  

Correlation matrices with average constraints

Jan Tuitman, Steven Vanduffel and Jing Yao

Statistics & Probability Letters, 2020, vol. 165, issue C

Abstract: We develop an algorithm that makes it possible to generate all correlation matrices satisfying a constraint on their average value. We extend the results to the case of multiple constraints. These results can be used to assess the extent to which methodologies driven by correlation matrices are robust to misspecification thereof.

Keywords: Correlation matrix; Random correlation matrices; Average correlation; Simulation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

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:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2020.108868

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-10-17
Handle: RePEc:eee:stapro:v:165:y:2020:i:c:s0167715220301711