Hanif Waqas, Ko Hee-Un, Pham Linh, Kang Sang Hoon
CEFAGE - Center for Advanced Studies in Management and Economics, University of Algarve, Faro, Portugal.
Department of Management Sciences, COMSATS University Islamabad, Attock Campus, Attock, Pakistan.
Financ Innov. 2023;9(1):84. doi: 10.1186/s40854-023-00474-6. Epub 2023 May 5.
This study examines the connectedness in high-order moments between cryptocurrency, major stock (U.S., U.K., Eurozone, and Japan), and commodity (gold and oil) markets. Using intraday data from 2020 to 2022 and the time and frequency connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57-66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271-296, 2018), we investigate spillovers among the markets in realized volatility, the jump component of realized volatility, realized skewness, and realized kurtosis. These higher-order moments allow us to identify the unique characteristics of financial returns, such as asymmetry and fat tails, thereby capturing various market risks such as downside risk and tail risk. Our results show that the cryptocurrency, stock, and commodity markets are highly connected in terms of volatility and in the jump component of volatility, while their connectedness in skewness and kurtosis is smaller. Moreover, jump and volatility connectedness are more persistent than that of skewness and kurtosis connectedness. Our rolling-window analysis of the connectedness models shows that connectedness varies over time across all moments, and tends to increase during periods of high uncertainty. Finally, we show the potential of gold and oil as hedging and safe-haven investments for other markets given that they are the least connected to other markets across all moments and investment horizons. Our findings provide useful information for designing effective portfolio management and cryptocurrency regulations.
本研究考察了加密货币市场、主要股票市场(美国、英国、欧元区和日本)以及大宗商品市场(黄金和石油)之间高阶矩的关联性。利用2020年至2022年的日内数据以及迪博尔德和伊尔马兹(《国际预测杂志》28(1):57 - 66, 2012)以及巴鲁尼克和克雷利克(《金融经济学杂志》16(2):271 - 296, 2018)的时间和频率关联性模型,我们研究了已实现波动率、已实现波动率的跳跃成分、已实现偏度和已实现峰度在各市场之间的溢出效应。这些高阶矩使我们能够识别金融回报的独特特征,如不对称性和厚尾性,从而捕捉各种市场风险,例如下行风险和尾部风险。我们的结果表明,加密货币市场、股票市场和大宗商品市场在波动率及其跳跃成分方面高度相关,而它们在偏度和峰度方面的关联性较小。此外,跳跃和波动率关联性比偏度和峰度关联性更持久。我们对关联性模型的滚动窗口分析表明,所有矩的关联性随时间变化,并且在高不确定性时期往往会增加。最后,鉴于黄金和石油在所有矩和投资期限内与其他市场的关联性最小,我们展示了它们作为其他市场套期保值和避险投资的潜力。我们的研究结果为设计有效的投资组合管理和加密货币监管提供了有用信息。