This article builds on the body of work that has depicted cryptocurrency as a model for science and higher education funding. To that end, this work examines the degree to which one or more cryptocurrencies would need to be adopted and achieve a network effect prior to implementation of such a funding model. Empirical data from three different cryptocurrencies were examined. The current work deploys generalized autoregressive conditional heteroskedasticity (GARCH) to analyze stochastic volatility. This work contends that the examined coins are likely overdistributed and too volatile, thereby limiting the wealth generation possibilities for funding science or higher education. Additionally, based on the GARCH analysis, this work highlights that cryptocurrency pricing metrics and valuation models, to this point, maybe insufficiently complex to persuade institutional investors to seriously allocate capital to this ecosphere.
Lehner, Edward; Carter, Louis; and Ziegler, John, "A call for Second-Generation Cryptocurrency Valuation Metrics" (2018). CUNY Academic Works.