Bitcoin price formation has been the topic of many studies due to the recent rise in popularity of cryptocurrencies around the globe. The problem not only lies with attempting to find how the value of this currency is established, but finding a framework that best describes how Bitcoin is created. In this paper, a modified version of Barro's framework is used, along with other prior frameworks, in an attempt to model pricing variation and formation for Bitcoin. To find causality, a simple VAR(p) model is used as a starting point, where the lag-order is selected based on BIC. This model includes various network statistics, Bitcoin popularity measures, commodity prices, and financial markets to identify potential pricing factors which could be argued to cause changes in Bitcoin price. A multivariate GARCH approach (MGARCH) is then used to fortify this model by not only modeling these causal relationships but modeling changes in volatility, eventually using Google trends to explain volatility changes in Bitcoin prices. According to these models, Bitcoin price formation follows an AR(1) process with ARCH/GARCH effects where these ARCH/GARCH effects can be explained by Bitcoin popularity. Due to the returns of Bitcoin relying on past information, a violation of the efficient market hypothesis may be present, meaning that arbitrage may exist in the Bitcoin market.