加密货币投资组合优化:在马科维茨框架内利用 GARCH-copula模型

IF 0.9 Q3 BUSINESS, FINANCE
Vahidin Jeleskovic, Claudio Latini, Zahid I. Younas, Mamdouh A. S. Al-Faryan
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引用次数: 0

摘要

人们对加密货币的兴趣与日俱增,使这种新的交换手段引起了金融界的关注。本研究旨在调查加密货币被视为金融资产时可能产生的影响。分析从事后角度进行,对三个不同的投资组合在一定时期内取得的业绩进行评估。其中一个投资组合仅由股票、债券和商品组成,第二个投资组合仅由加密货币组成,第三个投资组合是这两个投资组合的组合,因此由所有被认为是 "传统 "的资产和第二个投资组合中表现最好的加密货币组成。为此,采用了经典的方差-协方差方法,通过 GARCH-Copula 和 GARCH-Vine Copula 方法计算风险结构。优化组合中资产的最佳权重是通过马科维茨优化问题确定的。分析主要表明,从事后角度看,由加密货币和传统资产组成的投资组合具有更高的夏普指数,从事前角度看,其表现也更加稳定。由于马科维茨方法对加密货币市场非系统性极端事件的敏感性更高,我们选择马科维茨方法而非条件风险价值和预期亏空是有道理的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cryptocurrency portfolio optimization: Utilizing a GARCH-copula model within the Markowitz framework

The 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.

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来源期刊
CiteScore
2.30
自引率
7.10%
发文量
69
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