Cui Jinxin, Maghyereh Aktham
Collaborative Innovation Center of Statistical Data Engineering Technology and Application, Zhejiang Gongshang University, Hangzhou, 310018 People's Republic of China.
International Business School, Zhejiang Gongshang University, Hangzhou, 310018 People's Republic of China.
Financ Innov. 2022;8(1):90. doi: 10.1186/s40854-022-00395-w. Epub 2022 Sep 30.
Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1-7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.
分析共同变动和关联性对于为加密投资组合风险管理提供重要启示至关重要。然而,大多数现有研究都集中在低阶矩联系(即收益与波动率的相互作用)上。本研究首次在时域和频域中考察了新冠疫情之前及期间加密货币之间的高阶矩共同变动和风险关联性。我们结合了已实现矩测度和小波相干性,以及新提出的基于时变参数向量自回归的频率关联性方法(Chatziantoniou等人,《原油市场的整合与风险传导:一种时变参数频率关联性方法》。技术报告,比勒陀利亚大学经济系,2021年),使用日内高频数据。实证结果表明,比特币与其他加密货币之间已实现波动率的共同变动强于已实现偏度、已实现峰度和有符号跳跃变差的共同变动。加密货币之间的共同变动既依赖于时间,也依赖于频率。除了波动率溢出效应外,高阶矩和跳跃的风险溢出效应也很显著,尽管它们的幅度随矩而变化,这使得它们也依赖于矩,并且低于波动率关联性。频率关联性表明,风险关联性主要在短期内(1 - 7天)传导。此外,所有已实现矩的总动态关联性是随时间变化的,并且受到新冠疫情爆发的显著影响。我们为加密货币投资者、投资组合经理、监管机构和政策制定者在优化其投资和风险管理策略方面得出了一些实际启示。