Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.

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加密货币的多元分析

最近，加密货币世界的兴趣无疑增加了。自 2009 年大萧条后第一个加密货币出现以来，数字货币的受欢迎程度逐年上升。截至 2021 年 2 月，有超过 8525 种加密货币，市值约为 16760 亿美元。这些特定资产可用于分散投资组合以及投机行为。因此，调查加密货币的每日波动性和共同波动性对于投资者和投资组合经理来说至关重要。在这项工作中，从统计和经济的角度探索和评估了一组交易量最大的数字货币之间的相互依赖性。利用每月 Google 对加密货币的查询（这似乎是推动价格动态的因素），我们在动态条件相关 (DCC) 模型中采用了混合频率方法。特别是，我们在 DCC 框架中引入了双非对称 GARCH-MIDAS 模型。