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J. Risk Financial Manag. 2021, 14, 440 3 of 15 analogy to identify any impact of COVID-19. It is found to be large and significant, producing far greater convergence between asset classes and cryptos. Section 5 provides the concluding remarks. 2. Data and Basic Characteristics The cryptocurrency data and stock market index data set consists of daily spot ex- change rates in units of US dollars are from Yahoo Finance1. The price observations of Bitcoin (BTC-USD), Ethereum (ETH-USD), S&P500 stock market index (ˆGSPC) and NAS- DAQ stock market index (ˆIXIC) range from 6 August 2015 to 1 September 2020. We divided the time period into two parts: pre-COVID (6 August 2015–31 January 2020) and COVID era (1 February 2020–1 September 2020). In each data set of crypto market and stock market indices, we have open price, intraday high price, intraday low price, close price (adjusted for splits), adjusted close price (adjusted for both dividends and splits) and volume. To better illustrate the relationship between crypto market data and stock market indexes, we calculate the daily log return using adjusted close price: Returnt = 100 ∗ [ln(Pt) − ln(Pt−1)], (1) where Pt denotes the adjusted close price in USD at a time t. We now document main statistical properties of time series for the returns of S&P500 stock market index, NASDAQ stock market index, Bitcoin and Ethereum. Figure 1 illus- trates the time evolution of prices, volumes and daily log-returns for S&P500, NASDAQ, Bitcoin and Ethereum. We notice that both Bitcoin and Ethereum arrive their period spe- cific highest price in December 2017 within our analysis period. After this period price peak, the crypro price dropped dramatically. The descriptive statistics of daily log-returns are reported in Table 1. The daily returns of crypto markets exhibit high variability and excess kurtosis, both during pre-COVID and COVID era periods. The deviations from the Normal distribution are confirmed by the Jarque–Bera test that rejects the null hypothesis of normality. We applied the Augmented Dicker–Fuller (ADF) unit-root test, which suggests sta- tionarity of the log-returns. An ADF test tests the null hypothesis that a unit root is present in a time series sample. The alternative hypothesis is different depending on which version of the test is used, but is usually stationary or trend-stationary. In our case, we use the alternative hypothesis of stationary. This shows that the null hypothesis is rejected, and the time series of returns in each markets is stationary. These observations suggest that the crypto market is not as efficient as stock or foreign exchange markets, which display a complete lack of predictability (Lahmiri and Bekiros 2018). Since early 2020, the COVID-19 wreaked unprecedented havoc on the world economies. Educational institutions, travel industry to public events, almost everything is either post- poned or in limbo, which is inevitably going to affect businesses at every turn. Thousands of cases and deaths have already been recorded globally, and with the uncertainty on development of vaccines, the stock markets began to take many hits in terms of new lows. The S&P 500 index hit a period low since 2008 when the world plunged into a financial crisis. The cryptocurrency market has even become more volatile and has also experienced one of the worst sudden declines. We also noticed from Figure 1 that both cryptos and stock market indexes became more uncertain since the COVID-19 outbreak in early 2020. The return prices and volumes of Bitcoin and Ethereum also surged since early 2020.PDF Image | Contrasting Cryptocurrencies with Other Assets
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