CRYPTOCURRENCY, PROFITABILITY, AND TWEETER: A MGARCH FRAMEWORK

Abstract

This paper used two frames based on the Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) model, namely the Dynamic Conditional Correlation (DCC) and the Baba, Engle, Kraft, and Kroner (BEKK) models. DCC parameters confirmed the significant results to assess the spillover effects for return volatilities of five cryptocurrencies (Bitcoin, Dogecoin, Ethereum, Monero, and Peercoin). It indicated that cryptocurrency market returns would be volatile, connected with the time-varying pattern. Most ARCH and GARCH effects were significant in estimating the three pairs of return-mining profitability, return-Tweet, and mining profitability-Tweet. For the cryptocurrency return and profitability pair, returns depended on future price returns and cross-volatility spillover and were greater than their own volatility spillover effect. Moreover, the BEKK diagonal model was found to be the best model for return-mining profitability. The research community can also gain valuable insights into cryptocurrency investment models, offering wider future areas of research.