Portfolio management and dependence structure between cryptocurrencies and traditional assets: evidence from FIEGARCH-EVT-Copula

The purpose of this paper is twofold. Firstly, it discusses the relationship between five cryptocurrencies, oil prices, and US indices. Secondly, it focuses on determining the best portfolio hedging strategy. Using daily data relevant to the period ranging from January 4, 2016, to November 29, 2019,...

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Bibliographic Details
Published in:Journal of asset management 2021-05, Vol.22 (3), p.224-239
Main Authors: Jeribi, Ahmed, Fakhfekh, Mohamed
Format: Article
Language:English
Subjects:
Crude oil
Currency
Digital currencies
Diversification
Economics and Finance
Finance
Financial Services
Futures
Hedging
Investments
Original Article
Portfolio management
Risk Management
Risk reduction
Securities markets
Stochastic models
Volatility
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Summary:The purpose of this paper is twofold. Firstly, it discusses the relationship between five cryptocurrencies, oil prices, and US indices. Secondly, it focuses on determining the best portfolio hedging strategy. Using daily data relevant to the period ranging from January 4, 2016, to November 29, 2019, this study applies the FIEGARCH-EVT-Copula and Hedge ratios analysis. The findings obtained have shown that the crude oil (WTI) and the US indices return highlights the persistence of a negative and significant leverage effect while the cryptocurrency markets present a positive asymmetric volatility effect. Moreover, this paper show evidence of very weak dependence between all the different pairs considered before and after the introduction of Bitcoin Futures. Based on the Hedging ratio and mean-variance approach, this article suggests that to minimize the risk while keeping the same expected returns of the digital-conventional financial asset portfolio, the investor should hold more conventional financial assets than digital assets except for WTI-Bitcoin, WTI- Dash and WTI-Ethereum pairs which the values of their hedge ratios are rather important with respect to OLS regression.
ISSN:1470-8272
1479-179X
DOI:10.1057/s41260-021-00211-7