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Irregularly Spaced AR (ISAR) Models

  • Jeffrey S. C. Pai
  • Wolfgang Polasek
  • Hideo Kozumi
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Decay Parameter High Frequency Data Reciprocal Function Autoregressive Conditional Heteroskedasticity Space Time Interval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • Jeffrey S. C. Pai
    • 1
  • Wolfgang Polasek
    • 2
  • Hideo Kozumi
    • 3
  1. 1.Faculty of ManagementUniversity of ManitobaWinnipeg, ManitobaCanada
  2. 2.Institute of Statistics and EconometricsUniversity of BaselBaselSwitzerland
  3. 3.Faculty of Economics and Business AdministrationHokkaido UniversitySapporo 060Japan

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