Aspectos inovativos do bitcoin, Microestrutura de mercado e volatilidade de Preços.

Abstract

The present essay studied the conceptual, innovative, market-oriented and qualitative aspects of Bitcoin. After the exposure of the key concepts about the Bitcoin, digital currency and currency, there is a contextualization of the Bitcoin as a financial innovation. In regards to the innovative aspects, the birth of Bitcoin represented a radical and paradigmatic innovation with potential of creating a creative destruction. The most modern concepts of the Innovation Theory are also exposed and so it is perceived that the Bitcoin exhibits characteristics of Disruptive Innovation (DI) of two kinds: Low and (DI) and New Market (DI). In regards to the market-oriented and structural aspects it was investigated the existence of similarities between the credit and debit market of Bitcoin and the traditional structure of the payment card's market (two sided market - 2SM). It was verified the adequacy of Bitcoin to the 2SM structure, but only on the debit card's market. The credit system of the Bitcoin market does not fit to the one of the traditional system. This work sought to understand the volatility of determinants of the Bitcoin prices. To attain this objective two different kinds of analysis were made: univariate and multivariate analysis. To estimate the univariate volatility the following models were plotted: ARCH, GARCH, EGARCH and TARCH whose results showed the existence of persistent volatility. The multivariate quantitative analysis aimed to understand the volatilities interaction between Bitcoin the other financial variables. It was applied the DCC M-GARCH methodology which allows the achievement of the statistical parameters using the quasi-correlation of the volatility. The results showed the existence of ARCH and GARCH effects in all cases. While the explanatory variables don't indicate any significance, the multivariate analysis of the subsample realize a convergence movement of the variables. In all cases it was considered the return toward the mean. All models were estimated for a sample considering the period between September 13th of 2011 and June 23th of 2015 and for a subsample that covers the period from March 10th of 2013.