Economics at your fingertips  

The impact of covariance misspecification in risk-based portfolios

David Ardia, Guido Bolliger (), Kris Boudt and Jean-Philippe Gagnon-Fleury ()
Additional contact information
Guido Bolliger: University of Neuchâtel
Jean-Philippe Gagnon-Fleury: Université Laval

Annals of Operations Research, 2017, vol. 254, issue 1, No 1, 16 pages

Abstract: Abstract The equal-risk-contribution, inverse-volatility weighted, maximum-diversification and minimum-variance portfolio weights are all direct functions of the estimated covariance matrix. We perform a Monte Carlo study to assess the impact of covariance matrix misspecification to these risk-based portfolios at the daily, weekly and monthly forecasting horizon. Our results show that the equal-risk-contribution and inverse-volatility weighted portfolio weights are relatively robust to covariance misspecification. In contrast, the minimum-variance portfolio weights are highly sensitive to errors in both the estimated variances and correlations, while errors in the estimated correlations can have a large effect on the weights of the maximum-diversification portfolio.

Keywords: Covariance misspecification; Monte Carlo study; Risk-based portfolios (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18) Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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

DOI: 10.1007/s10479-017-2474-7

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2023-03-26
Handle: RePEc:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2474-7