Summary
High frequency data in finance are time series which are often measured at unequally or irregularly spaced time intervals. This paper suggests a modeling approach by so-called AR response surfaces where the AR coefficients are declining functions in continuous lag time. The irregularly spaced ISAR models contain the usual AR models as a special case if the time series is equally spaced. We illustrate our methodology with two examples.
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© 1998 Springer Japan
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Pai, J.S.C., Polasek, W., Kozumi, H. (1998). Irregularly Spaced AR (ISAR) Models. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_27
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DOI: https://doi.org/10.1007/978-4-431-65950-1_27
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-70208-5
Online ISBN: 978-4-431-65950-1
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