Abstract
This paper investigates the volatility processes of stablecoins and their potential stochastic interdependencies with Bitcoin volatility. We employ a novel approach to choose the optimal combination for the power law exponent and the minimum value for the volatilities bending the power law. Our results indicate that Bitcoin volatility is well-behaved in a statistical sense with a finite theoretical variance. Surprisingly, the volatilities of stablecoins are statistically unstable and contemporaneously respond to Bitcoin volatility. Also, whereas the volatilities of stablecoins are not Granger-causal for Bitcoin volatility, lagged Bitcoin volatility exhibits Granger-causal effects on the volatilities of stablecoins. We conclude that Bitcoin volatility is a fundamental factor that drives the volatilities of stablecoins. • We explore the stability of stablecoins. • We use power laws to estimate theoretical distribution moments. • We analyze volatility spillovers using log–log transformations. • Our results show that stablecoins are statistically unstable. • Bitcoin volatility is Granger-causal for stablecoin volatilities.
Highlights
Bitcoin has a number of unique advantages over traditional payment methods, such as user autonomy, discretion, peer-topeer focus, elimination of banking fees, low transaction fees for international payments, mobile payments, and 24/7 accessibility
While empirically our paper is closest to Baur and Hoang (2021a), unlike their study, we focus on: (i) uncertainty in stablecoin markets using realized volatilities, and (ii) stochastic interdependencies between the volatility processes of stablecoins and Bitcoin from a Granger-causal perspective
Our findings indicated that Bitcoin volatility is stable in the statistical sense that a theoretical variance exists
Summary
Bitcoin has a number of unique advantages over traditional payment methods, such as user autonomy, discretion, peer-topeer focus, elimination of banking fees, low transaction fees for international payments, mobile payments, and 24/7 accessibility. A recent study by Griffin and Shams (2020) employed algorithms to explore blockchain data and found that purchases of Tether were timed following market downturns and resulted in large increases in Bitcoin prices. In another recent paper, Baur and Hoang (2021a) proposed a framework to test for absolute and relative stability of stablecoins. In a recent study, Caporale and Zekokh (2019) fitted more than 1000 GARCH-type models to the log-returns of Bitcoin, Ethereum, Ripple and Litecoin Their results suggested that using standard GARCH models may yield incorrect Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts because cryptocurrency data exhibits a high level of asymmetries and regime switches.
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