Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact

IF 2 Q2 BUSINESS, FINANCE
Risks Pub Date : 2024-05-11 DOI:10.3390/risks12050078
Nicolò Giunta, Giuseppe Orlando, Alessandra Carleo, Jacopo Maria Ricci
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引用次数: 0

Abstract

This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks.
探索基于熵的投资组合策略:经验分析和加密货币的影响
本研究探讨了大型企业的市场集中度,强调了相对熵对理解多样化战略的实用性。研究引入了熵风险值(EVaR)作为一种连贯的风险度量,它是条件风险值(CVaR)的上限,并探讨了其广义化,即根植于卡尼达基斯熵的相对论风险值(RLVaR)。通过对发达市场(即标准普尔 500 指数和欧洲斯托克 50 指数)和发展中市场(即 BIST 100 指数和 Bovespa 指数)进行广泛的实证分析,该研究评估了投资组合选择中基于熵的标准,调查了不同市场类型的模型行为,并评估了加密货币的引入对投资组合表现和多样化的影响。主要研究结果表明,熵指标能有效识别最佳投资组合,尤其是在风险增加和集中度提高的情况下,这对于在低回报或高周转期间减轻负净业绩至关重要。在 BIST 100 指数中,比特币主要用于分散投资和提高业绩,而在其他市场中,比特币的配置仍然很少或根本没有,这证实了在少数龙头股占主导地位的股票市场中观察到的极端集中现象。
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
Risks
Risks Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
3.80
自引率
22.70%
发文量
205
审稿时长
11 weeks
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