Non-linear logit models for high frequency currency exchange data

High frequency market data has become available with recent developments in computer technology. These data have some unique characteristics that do not appear in low frequency data. They are important in understanding financial markets. We present evidence of a unique property of high frequency data by proposing a new model. In this paper, we analyze tick-by-tick data, the most high frequency data available, of yen-dollar currency exchange rates. Focusing on the direction of up or down price movement, we show that a non-trivial structure exists in conditional probabilities of binarized data, which is apparently invisible from the price change itself. The probabilistic structure has a strong bias not only in the first order conditional probabilities but also in the higher order ones. Logit models are often applied to the analysis of the direction of price change. However, we found that a conventional logit model was not sufficient for our data due to its non-linear behavior. This motivated us to develop a new extended non-linear logit model to reproduce the binary probabilistic structure. This new model has overcome some of the shortcomings of the conventional analysis such as AR models and logit models. It can successfully show that the structure is such that it refers up to the previous few minutes by a model selection based on Akaike Information Criterion. The empirical result is consistent with dealers' perceptions that their strategies change slowly in the time scale of a few minutes. Our analysis here opens a possibility that this new non-linear logit model can be applied to a wide range of binary time series to extract their non-trivial probabilistic structures. Finally, in order to investigate the generality of our model, we are now analyzing the tick-by-tick GE data on NYSE, which is also one of the most active stocks.