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Bivariate Time Series Modelling of Financial Count Data

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  • Quoreshi, Shahiduzzaman

    ()
    (Department of Economics, Umeå University)

Abstract

A bivariate integer-valued moving average (BINMA) model is proposed. The BINMA model allows for both positive and negative correlation between the counts. This model can be seen as an inverse of the conditional duration model in the sense that short durations in a time interval correspond to a large count and vice versa. The conditional mean, variance and covariance of the BINMA model are given. Model extensions to include explanatory variables are suggested. Using the BINMA model for AstraZeneca and Ericsson B it is found that there is positive correlation between the stock transactions series. Empirically, we find support for the use of long-lag bivariate moving average models for the two series. have significant effects for both series.

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Bibliographic Info

Paper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 655.

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Length: 17 pages
Date of creation: 14 Apr 2005
Date of revision:
Handle: RePEc:hhs:umnees:0655

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Postal: Department of Economics, Umeå University, S-901 87 Umeå, Sweden
Phone: 090 - 786 61 42
Fax: 090 - 77 23 02
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Web page: http://www.econ.umu.se/
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Related research

Keywords: Count data; Intra-day; High frequency; Time series; Estimation; Long memory; Finance;

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Cited by:
  1. Quoreshi, Shahiduzzaman, 2006. "Time Series Modelling Of High Frequency Stock Transaction Data," Umeå Economic Studies 675, Umeå University, Department of Economics.
  2. Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," Umeå Economic Studies 673, Umeå University, Department of Economics.
  3. Quoreshi, A.M.M. Shahiduzzaman, 2008. "A vector integer-valued moving average model for high frequency financial count data," Economics Letters, Elsevier, vol. 101(3), pages 258-261, December.
  4. Quoreshi, A.M.M. Shahiduzzaman, 2014. "Bivariate Integer-Valued Long Memory Model for High Frequency Financial Count Data," CITR Working Paper Series, Center for Innovation and Technology Research, Blekinge Institute of Technology 2014/03, Center for Innovation and Technology Research, Blekinge Institute of Technology.

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