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A Vector Integer-Valued Moving Average Modelfor High Frequency Financial Count Data

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  • Quoreshi, Shahiduzzaman

    ()
    (Department of Economics, Umeå University)

Abstract

A vector integer-valued moving average (VINMA) model is introduced. The VINMA model allows for both positive and negative correlations between the counts. The conditional and unconditional first and second order moments are obtained. The CLS and FGLS estimators are discussed. The model is capable of capturing the covariance between and within intra-day time series of transaction frequency data due to macroeconomic news and news related to a specific stock. Empirically, it is found that the spillover effect from Ericsson B to AstraZeneca is larger than that from AstraZeneca to Ericsson B

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Bibliographic Info

Paper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 674.

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Length: 10 pages
Date of creation: 11 Apr 2006
Date of revision:
Handle: RePEc:hhs:umnees:0674

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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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Keywords: Count data; Intra-day; Time series; Estimation; Reaction;

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References

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  1. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
  2. Gourieroux Christian & Monfort Alain & Trognon A, 1982. "Pseudo maximum lilelihood methods : applications to poisson models," CEPREMAP Working Papers (Couverture Orange) 8203, CEPREMAP.
  3. Brännäs, Kurt & Quoreshi, Shahiduzzaman, 2004. "Integer-Valued Moving Average Modelling of the Number of Transactions in Stocks," UmeÃ¥ Economic Studies 637, Umeå University, Department of Economics.
  4. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  5. Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," UmeÃ¥ Economic Studies 673, Umeå University, Department of Economics.
  6. Quoreshi, Shahiduzzaman, 2005. "Bivariate Time Series Modelling of Financial Count Data," UmeÃ¥ Economic Studies 655, Umeå University, Department of Economics.
  7. Easley, David & O'Hara, Maureen, 1992. " Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
  8. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
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Cited by:
  1. Pedeli, Xanthi & Karlis, Dimitris, 2013. "Some properties of multivariate INAR(1) processes," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 213-225.

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