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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.

Suggested Citation

  • Quoreshi, Shahiduzzaman, 2005. "Bivariate Time Series Modelling of Financial Count Data," Umeå Economic Studies 655, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0655
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    Citations

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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, A.M.M. Shahiduzzaman, 2014. "Bivariate Integer-Valued Long Memory Model for High Frequency Financial Count Data," Working Papers 2014/03, Blekinge Institute of Technology, Department of Industrial Economics.
    3. Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," Umeå Economic Studies 673, Umeå University, Department of Economics.
    4. 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.
    5. Scotto, Manuel G. & Weiß, Christian H. & Silva, Maria Eduarda & Pereira, Isabel, 2014. "Bivariate binomial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 233-251.

    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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