With the arise of cryptocurrencies in 2009, started with the Bitcoin, a new dynamic of investment and technology emerged in the 21st century with a new market that has already exceed US 800 billion in 2018 and has more than 2,000 coins. Despite the high volatility, various Ponzi schemes, lack of regulation and the main use as investment than in purchases goods and services, cryptocurrencies have been gaining ground, amid controversy, due to the disruptive technology. The objective of this work is to analyze the 50 largest cryptocurrencies in the market during the period of 2015-2018 by means of three essays that seek to investigate: (i) the volatility analysis and prediction using MSGARCH (KLAASSEN, 2002), with accuracy tests (involving MSE and QLIKE loss functions, as well as MAE, MAPE, and Theil s U indicator); (ii) the analysis of the behavioral phenomena of herd effect following modifications in CSSD (CHRISTIE e HUANG, 1995), CSAD (CHANG, CHENG e KHORANA, 2000) and HS (HWANG e SALMON, 2004) methodologies, as well as the contagion effect following modifications in the methodologies of FR test (FORBES e RIGOBON, 2002) and higher order comoments tests (FRY, MARTIN e TANG, 2010; FRY-MCKIBBIN e HSIAO, 2018); (iii) the analysis of the feedback trading phenomenon through the seminal model of Sentana and Wadhwani (1992). As main findings, it was identified that: (i) there is a strong influence of two volatility states; in the cryptoassets with more probability of occurrence under the second regime, there is a greater tendency of occurrence of the second state of volatility when prices go up, where there is more the volatility - the exception that has been noted only in BTC and ETH, where the first state of volatility is strong when prices go up, with more volatility; there is more accuracy in the forecasting with two volatility states for long term prediction than in short term prediction; (ii) with respect to the herd effect, the CSAD model detected a small herd effect, with little statistical significance, and the CSSD model detected a strong herd effect statistically significant in the down movement of market; the HS model successfully captured herd behavior and revealed extreme periods of reversal in the herd effect; in relation to the contagion effect, the FR test was able to capture Bitcoin s contagion in other currencies in practically all cases except Tether Dollar, BITCNY and ECC - which typically have inflationary control and particularities of stablecoins; in the comoments models, the tests indicated contagion of Bitcoin in relation to the currencies analyzed; (iii) in relation to the feedback trading phenomenon, it was possible to capture negative feedback trading in TETHER and positive in BTC, ETH, CSC and ECC, whose adequacy of the model used was confirmed later by the signal bias test (ENGLE e NG, 1993), with the exception of TETHER - which contradicts Santana and Wadhwani (1992) and Shi, Chiang and Liang (2012) that less parsimonious models would have little influence on feedback trading.