Non-linear logit models for high-frequency data analysis

We analyze tick-by-tick data, the most high frequency data available, of yen–dollar exchange rates with focus on the direction of up or down price movement. We propose a non-linear logit model to describe a non-trivial probability structure, apparently invisible from the price change itself, shown in binarized data extracting up or down price movement. The model selected by AIC agrees well with empirical results. Additionally, the similar bias is obtained from binarized tick-by-tick data on NYSE, for example GE. Our model could be useful for a wide range of binary time series extracting their non-trivial probability structures.