Dynamical Structures of High-Frequency Financial Data

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dc.creatorKim, Kyungsik
dc.creatorYoon, Seong-Min
dc.creatorKim, Soo Yong
dc.creatorChang, Ki-Ho
dc.creatorKim, Yup
dc.date2005-12-23
dc.date.accessioned2026-07-07T12:07:41Z
dc.date.available2026-07-07T12:07:41Z
dc.descriptionWe 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.
dc.description4 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/physics/0512225
dc.identifierhttp://arxiv.org/abs/physics/0512225
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/209058
dc.subjectPhysics and Society
dc.subjectStatistical Finance
dc.titleDynamical Structures of High-Frequency Financial Data
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