Crypto-assets portfolio selection and optimization: a COGARCH-Rvine approach

IF 0.7 4区经济学 Q3 ECONOMICS

Studies in Nonlinear Dynamics and Econometrics Pub Date : 2021-03-26 DOI:10.1515/snde-2020-0072

J. Mba, Sutene Mwambetania Mwambi

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引用次数: 1

Abstract

Abstract Blockchain is a new technology slowly integrating our economy with crytocurrencies such as Bitcoin and many more applications. Bitcoin and other version of it (known as Altcoins) are traded everyday at various cryptocurrency exchanges and have drawn the interest of many investors. These new type of assets are characterised by wild swings in prices and this can lead to great profit as well as large losses. To respond to these dynamics, crypto investors need adequate tools to guide them through their choice of optimal portfolio selection. This paper presents a portfolio selection based on COGARCH and regular vine copula which are able to capture features such as abrupt jumps in prices, heavy-tailed distribution and dependence structure respectively, with the optimal portfolio achieved through the stochastic heuristic algorithm differential evolution known for its global search solution ability. This method shows great performance as compared with other available models and can achieve up to 50% of total returns in some periods of optimization.

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加密资产投资组合选择与优化:COGARCH-Rvine方法

摘要区块链是一项新技术，它将我们的经济与比特币等加密货币以及更多应用程序缓慢地结合在一起。比特币和其他版本的比特币（称为Altcoins）每天在各种加密货币交易所进行交易，吸引了许多投资者的兴趣。这些新型资产的特点是价格剧烈波动，这可能导致巨大的利润和巨大的损失。为了应对这些动态，加密货币投资者需要足够的工具来指导他们选择最佳投资组合。本文提出了一种基于COGARCH和正则藤copula的投资组合选择方法，该方法能够分别捕捉价格突变、重尾分布和依赖结构等特征，并通过以全局搜索能力著称的随机启发式算法差分进化获得最优投资组合。与其他可用模型相比，该方法表现出良好的性能，并且在某些优化期内可以实现高达50%的总回报。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Studies in Nonlinear Dynamics and Econometrics Multiple-

CiteScore

1.40

自引率

12.50%

发文量

期刊介绍： Studies in Nonlinear Dynamics & Econometrics (SNDE) recognizes that advances in statistics and dynamical systems theory may increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.