  We study the dynamical behavior of high-frequency data from the Korean Stock
Price Index (KOSPI) using the movement of returns in Korean financial markets.
The dynamical behavior for a binarized series of our models is not completely
random. The conditional probability is numerically estimated from a return
series of KOSPI tick data. Non-trivial probability structures can be
constituted from binary time series of autoregressive (AR), logit, and probit
models, for which the Akaike Information Criterion shows a minimum value at the
15th order. From our results, we find that the value of the correct match ratio
for the AR model is slightly larger than the findings of other models.
