Cryptocurrency portfolio optimization: Utilizing a GARCH‐copula model within the Markowitz framework

Author:

Jeleskovic Vahidin1,Latini Claudio2,Younas Zahid I.3,Al‐Faryan Mamdouh A. S.4ORCID

Affiliation:

1. Deparment of econmics Humboldt‐Universität of Berlin Berlin Germany

2. Independent Researcher Viterbo Italy

3. Berlin School of Business and Innovation Berlin Germany

4. University of Portsmouth Portsmouth UK

Abstract

AbstractThe growing interest in cryptocurrencies has brought this new means of exchange to the attention of the financial world. This study aims to investigate the effects that a cryptocurrency can have when it is considered as a financial asset. The analysis is carried out from an ex‐post perspective, evaluating the performance achieved in a certain period by three different portfolios. These are the one composed only of equities, bonds and commodities, the second one only of cryptocurrencies, and the third one is a combination of these both ones and thus made up of all considered “traditional” assets and the most performing cryptocurrency of the second portfolio. For these purposes, the classic variance‐covariance approach is applied where the calculation of the risk structure is done via the GARCH‐Copula and GARCH‐Vine Copula approaches. The optimal weights of the assets in the optimized portfolios are determined through Markowitz optimization problem. The analysis mainly showed that the portfolio composed of cryptocurrency and traditional assets has a higher Sharpe index, from an ex‐post perspective, and more stable performances, from an ex‐ante perspective. We justify our selection of the Markowitz approach over conditional VaR and expected shortfall due to their heightened sensitivity to unsystematic extreme events in crypto markets.

Publisher

Wiley

Reference87 articles.

1. Pair‐copula constructions of multiple dependence;Aas K.;Insurance: Mathematics and economics,2009

2. Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios;Antonakakis N.;Science Direct,2019

3. Anyfantaki S. &Topaloglou N.(2018).Diversification integration and cryptocurrency market. Integration and Cryptocurrency Market (March 29 2018).

4. Mean–variance–skewness–kurtosis approach to portfolio optimization: An application in İstanbul Stock Exchange;AracioĞlu B.;Ege Akademik Bakış Dergisi,2011

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3