Abstract

The exponential growth of digital currencies in general and cryptocurrencies, in particular, has seemingly broken every record in the book. This has generated in the process a tremendous amount of interest in both developed and developing countries from scholars, academics, politicians, decision-makers and other stakeholders. Considering an applied methodology about asymmetric volatility with Exponential General Auto-regressive Conditional heteroscedasticity (EGARCH), this research work explores the fundamentals of the behavior of cryptocurrencies comparatively to a benchmark of key assets. To achieve its goal, this study uses two classes of assets. On the one hand, the first class (Class I) includes seven ̶ Bitcoin, Ethereum, Binance, Dogecoin, Tether, Ripple, and Cardano ̶ of the top 10 cryptocurrencies, which, as of July 2021, commanded more than $1.5 trillion in market capitalization. On the other hand, the second class (Class II) is comprised of three traditionally established, well-known and “safe” assets, namely, gold, the 3-month US treasury bill and the 30-year US treasury bond. Using thousands of datapoints, empirical findings regarding volatilities, returns, clustering and leverage effects of the two asset classes do not reveal any startling contrasts to warrant an outright dismissal of crypto-assets as viable repositories of purchasing power and value. However, the pace in the move towards a full “safe haven” status will hinge upon the introduction of a clear regulatory and legislative framework in the US and other major countries to instill more confidence and certainty about crypto assets in a post-Covid era.

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