Economics at your fingertips  

Robust multivairiate extreme value at risk allocation

A. Belhajjam, M. Belbachir and S. El Ouardirhi

Finance Research Letters, 2017, vol. 23, issue C, 1-11

Abstract: Recent research in economy, especially in finance, is using the assumption that the returns distribution is normal for determining optimal allocation for different models in finance optimization like Markowitz model. However this assumption is not already exact because the problem of the asymmetric distribution has a big impact. Our paper proposes a Multivariate Extreme Value at Risk (MEVaR) approach to find the optimal allocation of a portfolio based on the extreme value theory. A detailed procedure and implementation on two different portfolio (the first, for an emerging market as Morocco and the second on a Canadian portfolio of a very liquid market. Are given for demonstrating the consistency of the new approach? Results are compared with the Worst-Case-Value at Risk (WCVaR) proposed by El Ghaoui (2003), and Partitioned Value at Risk (PVaR) approach Joel Goh et al. (2011). They establish that the MEVaR performs well and is used in prediction model.

Keywords: Extreme value theory; Value at risk; Worst-case value at risk; Partitioned value at risk; Markowitz approach; Multivariate extreme value (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) 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:

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2018-12-08
Handle: RePEc:eee:finlet:v:23:y:2017:i:c:p:1-11