Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach

Mazin A.M. Al Janabi, Román Ferrer and Syed Jawad Hussain Shahzad

Physica A: Statistical Mechanics and its Applications, 2019, vol. 536, issue C

Abstract: This paper develops a novel approach to assess liquidity-adjusted Value-at-Risk (LVaR) optimization of multi-asset portfolios based on vine copulas and LVaR models. This framework is applied to stock markets of the G-7 countries, gold, commodities and Bitcoin. The results show that our approach is superior to the classical mean–variance Markowitz portfolio technique in terms of the optimal portfolio selection under a number of realistic operational and budget constraints. We find that both Bitcoin and gold improves the risk-return performance of the G-7 stock portfolio. However, Bitcoin (gold) performs better under a scenario of only long-positions (when short-selling is allowed).

Keywords: Portfolio optimization; Multivariate dependence; Stock markets; Gold; Bitcoin (search for similar items in EconPapers)
JEL-codes: G11 G20 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119314761
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:536:y:2019:i:c:s0378437119314761

DOI: 10.1016/j.physa.2019.122579

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().