美国股市波动的模式是否因COVID-19而发生变化,或者尽管受到大流行冲击,美国股市的波动性是否有所下降?至于科技股,它们的波动性比整体市场还要小吗?在本文中,我们提供了支持股市“平静”的证据,被COVID-19打断,通过分析分散性,包括FATANG个股在内的九个收益序列的经验分布的偏度和峰度特征(FAANG:Facebook,亚马逊,苹果,Netflix和Google;加上特斯拉)和美国指数(标准普尔500指数,道琼斯工业平均指数和纳斯达克指数)。与前几年相比,在COVID-19之后,特斯拉的日平均回报率分别是6.48、2.58和2.34倍,苹果和纳斯达克,分别。就波动性而言,与FATANG个股相比,这三个股指的涨幅更为明显。本文还提出了一种基于半方差和半峰度的新方法。虽然美国三大股指的半峰度与峰度的比值始终高于70%,在股票的情况下,情况正好相反,这凸显了与负回报相比,大的正回报的重要性。还通过考虑传统的对称和非对称GARCH模型,分析了结构断裂和条件异方差。我们表明,在最近的过去,尽管COVID-19大流行,法唐科技股的特点主要是条件同时性,而美国股指的收益率主要表现为条件异方差。
Did the pattern of US stock market volatility change due to COVID-19 or have the US stock markets been less volatile despite the pandemic shock? And as for tech stocks, are they even less volatile than the market overall? In this paper, we provide evidence in favor of a \"quietness\" in the stock markets, interrupted by COVID-19, by analyzing dispersion, skewness and kurtosis characteristics of the empirical distribution of nine returns series that include individual FATANG stocks (FAANG: Facebook, Amazon, Apple, Netflix and Google; plus Tesla) and US indices (S&P 500, DJIA and NASDAQ). In comparison with the years before, the daily average return after COVID-19 was 6.48, 2.58 and 2.34 times higher for Tesla, Apple and NASDAQ, respectively. In terms of volatility, the increase was more pronounced in the three stock indices when compared to the individual FATANG stocks. This paper also puts forward a new methodology based on semi-variance and semi-kurtosis. While the value of the ratio between semi-kurtosis and kurtosis is always higher than 70% for the three US stock indices, in the
case of stocks the opposite is true, which highlights the importance of large positive returns when compared to negative ones. Structural breaks and conditional heteroskedasticity are also analyzed by considering the traditional symmetrical and asymmetrical GARCH models. We show that in the most recent past, despite the COVID-19 pandemic, the FATANG tech stocks are characterized mostly by conditional homoskedasticity, while the returns of US stock indices are characterized mainly by conditional heteroskedasticity.