Lahmiri Salim, Bekiros Stelios
Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3H 0A1, Canada.
Department of Economics, European University Institute, 50014 Florence, Italy.
Entropy (Basel). 2020 Jul 30;22(8):833. doi: 10.3390/e22080833.
The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor's 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that () randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while () randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, () we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and () the S&P500 volatility represents a unique cluster, while () the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, () the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and () the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that () investors may diversify their portfolios across single constituents of clusters, () investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that () constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.
我们论文的主要目的是评估新冠疫情对世界主要市场波动率序列随机性的影响，并研究其对市场间相互联系的作用。数据集包括股票（比特币和标准普尔500指数）、贵金属（黄金和白银）以及能源市场（西德克萨斯中质原油、布伦特原油和天然气）。将广义自回归条件异方差模型应用于收益序列。根据估计的波动率序列计算小波包香农熵以评估随机性。采用层次聚类法研究波动率之间的相互联系。我们发现，（1）标准普尔500指数波动率和贵金属波动率受新冠疫情影响最大，而（2）能源市场波动率受疫情影响程度低于股票和贵金属市场。此外，（3）我们发现明显出现了三个波动率集群：贵金属（黄金和白银）、能源（布伦特原油和天然气）以及比特币和西德克萨斯中质原油，且（4）标准普尔500指数波动率代表一个独特集群，同时（5）在疫情之前，标准普尔500指数市场波动率与比特币、能源和贵金属市场的波动率没有关联。而且，（6）在疫情期间，标准普尔500指数市场波动率与能源市场波动率以及比特币波动率产生了关联，并且（7）在疫情期间，贵金属波动率与能源市场波动率以及比特币市场波动率的关联度较低。研究得出结论：（1）投资者可以在各集群的单一成分中实现投资组合多样化，（2）由于疫情期间能源市场各自波动率的随机性较低，投资能源市场颇具吸引力，并且（3）由于聚类结构在不同时期相当稳定，构建多元化投资组合并非难事。
