Dynamical structures of high-frequency financial data
Seong-Min Yoon (),
Yup Kim and
Sang Hoon Kang
Physica A: Statistical Mechanics and its Applications, 2007, vol. 376, issue C, 525-531
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 of a binarized series of our models is not completely random. In addition, 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 that derived by other models.
Keywords: Correlation function; Conditional probability distribution; AR model; Logit and probit models; KOSPI (search for similar items in EconPapers)
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Working Paper: Dynamical Structures of High-Frequency Financial Data (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:376:y:2007:i:c:p:525-531
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