Resumen
Bitcoin has attracted the attention of investors lately due to its significant market capitalization and high volatility. This work considers the modeling and forecasting of daily high and low Bitcoin prices using a fractionally cointegrated vector autoregressive (FCVAR) model. As a flexible framework, FCVAR is able to account for two fundamental patterns of high and low financial prices: their cointegrating relationship and the long memory of their difference (i.e., the range), which is a measure of realized volatility. The analysis comprises the period from January 2012 to February 2018. Empirical findings indicate a significant cointegration relationship between daily high and low Bitcoin prices, which are integrated on an order close to the unity, and the evidence of long memory for the range. Results also indicate that high and low Bitcoin prices are predictable, and the fractionally cointegrated approach appears as a potential forecasting tool forcryptocurrencies market practitioners.