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Integer-Valued Moving Average Modelling of the Number of Transactions in Stocks

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  • Brännäs, Kurt

    ()
    (Department of Economics, Umeå University)

  • Quoreshi, Shahiduzzaman

    ()
    (Department of Economics, Umeå University)

Abstract

The integer-valued moving average model is advanced to model the number of transactions in intra-day data of stocks. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study the least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. News about prices are found to exert a symmetric and positive effect on the number of transactions.

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Bibliographic Info

Paper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 637.

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Length: 24 pages
Date of creation: 09 May 2004
Date of revision:
Handle: RePEc:hhs:umnees:0637

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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; High frequency; Time series; Estimation; Finance.;

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References

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  1. 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.
  2. Brännäs, Kurt & Simonsen, Ola, 2003. "Discretized Time and Conditional Duration Modelling for Stock Transaction Data," UmeÃ¥ Economic Studies 610, Umeå University, Department of Economics.
  3. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  4. HEINEN, Andréas, 2003. "Modelling time series count data: an autoregressive conditional Poisson model," CORE Discussion Papers 2003062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Brannas, Kurt & Hellstrom, Jorgen & Nordstrom, Jonas, 2002. "A new approach to modelling and forecasting monthly guest nights in hotels," International Journal of Forecasting, Elsevier, vol. 18(1), pages 19-30.
  6. HEINEN, Andreas & RENGIFO, Erick, 2003. "Multivariate modelling of time series count data: an autoregressive conditional Poisson model," CORE Discussion Papers 2003025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. Lobato, Ignacio & Nankervis, John C & Savin, N E, 2001. "Testing for Autocorrelation Using a Modified Box-Pierce Q Test," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 187-205, February.
  8. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
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Citations

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Cited by:
  1. Quoreshi, Shahiduzzaman, 2006. "A Vector Integer-Valued Moving Average Modelfor High Frequency Financial Count Data," UmeÃ¥ Economic Studies 674, Umeå University, Department of Economics.
  2. 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.
  3. Christian Weiß & Hee-Young Kim, 2013. "Parameter estimation for binomial AR(1) models with applications in finance and industry," Statistical Papers, Springer, vol. 54(3), pages 563-590, August.
  4. Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," UmeÃ¥ Economic Studies 673, Umeå University, Department of Economics.
  5. Drost, F.C. & Akker, R. van den & Werker, B.J.M., 2006. "Local Asymptotic Normality and Efficient Estimation for inar (P) Models," Discussion Paper 2006-45, Tilburg University, Center for Economic Research.
  6. Quoreshi, Shahiduzzaman, 2006. "Time Series Modelling Of High Frequency Stock Transaction Data," UmeÃ¥ Economic Studies 675, Umeå University, Department of Economics.
  7. Drost, F.C. & Akker, R. van den & Werker, B.J.M., 2007. "Efficient Estimation of Autoregression Parameters and Innovation Distributions for Semiparametric Integer-Valued AR(p) Models (Subsequently replaced by DP 2008-53)," Discussion Paper 2007-23, Tilburg University, Center for Economic Research.
  8. Quoreshi, A.M.M. Shahiduzzaman, 2014. "Bivariate Integer-Valued Long Memory Model for High Frequency Financial Count Data," CITR Working Paper Series 2014/03, Center for Innovation and Technology Research, Blekinge Institute of Technology.
  9. Brännäs, Kurt & Lönnbark, Carl, 2006. "Effects of Explanatory Variables in Count Data Moving Average Models," UmeÃ¥ Economic Studies 679, Umeå University, Department of Economics.

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