Beginner question. Having read a couple of papers and book chapters on high-frequency data forecasting, I'm surprised (and confused) that the same time series techniques can be applied to high-frequency/ tick data than for lower frequencies.
High-frequency data seems to clearly not follow a normal distribution given that prices change in discrete steps, have a minimal tick size, lag-1 autocorrelation, etc. Thus how can researchers apply techniques on it which have strong normality assumptions? (e.g. analysing the mid-price evolution and signed volume of transactions using vector autoregression of Hasbrouck).
For example  describes an autoregressive (VAR) quote-revision model which relates mid-price changes to signed trade size. Clearly, the difference in mid-prices are discrete and I would say ressembles count data.
 Measuring the Information of Stock Trades Joel Hasbrouck, Quantitative Finance, 1991, Journal of Finance pdf