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Sensors 2021, 21, 6319 8 of 16 the market capitalization of BTC and ETH and less increase in the other currencies during the period of the outbreak of the pandemic. Kurtosis: −0.142 30 20 10 0 Skewness: 0.436 1500000 1000000 500000 0 0 500000 1000000 1500000 COVID−19 daily new cases 2020 Jun 2020 Dec COVID−19 daily new cases 2021 Jun 40 20 0 Kurtosis: −0.603 Skewness: 0.046 15000 10000 5000 0 0 5000 10000 15000 COVID−19 daily new deaths 2020 Jun 2020 Dec COVID−19 daily new deaths 2021 Jun 0.03 0.00 −0.03 2019 Jun 2020 Feb 2020 Oct 2021 Jun Date 100 75 50 25 0 Kurtosis: 5.197 Skewness: −0.517 −0.03 0.00 0.03 Gold log−returns 2000 1800 1600 1400 1200 2019 Jun 2020 Feb Date 2020 Oct 2021 Jun Figure 2. Probability density function and trend of daily (from 23 January 2020 to 01 July 2021) confirmed COVID-19 infections and deaths (upper and middle) and daily (from 04 December 2018 to 14 July 2021) returns (lower left), probability density function (lower middle), and prices (lower right) of the gold. The vertical dotted line represents the threshold between the pre-COVID-19 period (left) and during the COVID-19 period (right) so that the shaded zone represents the period after the pandemic begins to visualize its impact on the gold price. Almost all currencies followed a similar trend during the period of study. The graphi- cal plots also indicate that the market capitalization substantially plummeted, from mid- May 2021 until the end of the study period, when Tesla (https://www.bbc.com/news/ business-57096305, accessed on 29 August 2021) announced that it would not accept or sell the cryptocurrencies due to pollution concerns. Such bad news often impacts the cryptocurrency prices leading to a substantial increase in the volatility of the returns. Thus, the traditional time-series models might not provide accurate cryptocurrency returns fore- casting results, especially during adverse market conditions. Then, the SVM model might be a good option to achieve forecasting accuracy as we will show next. We split the gold price into four classes according to their quartiles and labeled the values as Q1, Q2, Q3, and Q4. Note that, although our approach can work for all quantiles (from 0 to 100), we consider only four quartiles for simplicity’s sake. Readers may use the supplementary source code for any subset of gold-price quantiles. We combine the quartiles of the gold prices one day in advance with the daily log-returns of the employed currencies and the COVID-19 cases and deaths. The resulting date corresponds to a time series comprised of 682 observations from 04 December 2018 to 22 January 2020 (before COVID- 19 with 297 observations) and from 23 January 2020 to 14 July 2021 (during COVID-19 with 385 observations). Figures 4a,b illustrate the daily returns, distribution, box-plot, and prices of the studied currencies. The box-plots illustrate that the cryptocurrency returns have many outliers in both periods and the volatility of the returns during the COVID-19 period is considerably higher than in the pre-COVID-19 pandemic period. Gold log−returns Density Density Density Gold price in US dollarPDF Image | Predicting Cryptocurrency Returns Based on the Gold Prices
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