We investigate whether data from Google Trends can be used to forecast stock returns. Previous studies have found that high Google search volumes predict high returns for the first one to two weeks, with subsequent price reversal. By using a more recent dataset that covers the period from 2008...

We suggest a simple and general way to improve the GARCH volatility models using the intraday range between the highest and the lowest price to proxy volatility. We illustrate the method by modifying a GARCH(1,1) model to a Range-GARCH(1,1) model. Our empirical analysis conducted on stocks,...

This paper studies existence of structural breaks in the average return and volatility of the Bitcoin price. We utilize a Bayesian change point model to detect structural breaks and to partition the time series into segments. We find that structural breaks in average returns and volatility of...