I have seen a plenty of articles/books regarding volatility forecasting applied to high frequency data, but none of them were dedicated to forecasting the actual prices (for example bid/ask of currency pairs, stock prices).
Forecasting Volatility using High Frequency Data (P. R. HANSEN, A. LUNDE)
I realize that forecasting hft data must be hard because of several problems:
- How long model will be valid?
- How to check such validity?
- Will a new model be calculated on time? (NN and most of subtypes of NN need a lot of time to be recalculated)
Given the above mentioned problems/restrictions I would like to ask you:
- What's the best way of forecasting high-frequency data?
- What kind of scientific papers/books should I follow in order to be up-to-date in this topic?
From my own research based on hft data I can write that generally only few models are better than naive forecasting (but only in some sub time series - few minutes etc.)
- Kalman Filtering (calibrated accordingly)
- ARIMA, ARMA models
Generally worse than naive forecasting are:
- Holts linear model (ARIMA (0,2,2)
- Linear Regression
- Exponential Smoothing
- Moving averages (different types)