Integer-valued moving average modelling of the number of transactions in stocks
AbstractThe Integer-valued Moving Average Model (INMA) 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 explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study a feasible 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. There is evidence of asymmetric effects of news about prices on the number of transactions.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Financial Economics.
Volume (Year): 20 (2010)
Issue (Month): 18 ()
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Web page: http://www.tandfonline.com/RAFE20
Other versions of this item:
- 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.
- 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 &bull Diffusion Processes
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Brännäs, Kurt & Simonsen, Ola, 2003.
"Discretized Time and Conditional Duration Modelling for Stock Transaction Data,"
UmeÃ¥ Economic Studies
610, Umeå University, Department of Economics.
- Kurt Brannas & Ola Simonsen, 2007. "Discretized time and conditional duration modelling for stock transaction data," Applied Financial Economics, Taylor & Francis Journals, vol. 17(8), pages 647-658.
- 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.
- 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).
- 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.
- Brännäs, Kurt & Hellström, Jörgen & Nordström, Jonas, 1999. "A New Approach to Modelling and Forecasting Monthly Guest Nights in Hotels," UmeÃ¥ Economic Studies 503, Umeå University, Department of Economics.
- Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
- 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).
- Heinen, Andreas, 2003. "Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model," MPRA Paper 8113, University Library of Munich, Germany.
- 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.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- Brännäs, Kurt & Lönnbark, Carl, 2006. "Effects of Explanatory Variables in Count Data Moving Average Models," UmeÃ¥ Economic Studies 679, Umeå University, Department of Economics.
- Quoreshi, Shahiduzzaman, 2006. "Time Series Modelling Of High Frequency Stock Transaction Data," UmeÃ¥ Economic Studies 675, Umeå University, Department of Economics.
- Feike C. Drost & Ramon van den Akker & Bas J. M. Werker, 2008.
"Local asymptotic normality and efficient estimation for INAR(p) models,"
Journal of Time Series Analysis,
Wiley Blackwell, vol. 29(5), pages 783-801, 09.
- 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.
- Quoreshi, A.M.M. Shahiduzzaman, 2008.
"A vector integer-valued moving average model for high frequency financial count data,"
Elsevier, vol. 101(3), pages 258-261, December.
- Quoreshi, Shahiduzzaman, 2006. "A Vector Integer-Valued Moving Average Modelfor High Frequency Financial Count Data," UmeÃ¥ Economic Studies 674, Umeå University, Department of Economics.
- Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," UmeÃ¥ Economic Studies 673, Umeå University, Department of Economics.
- 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.
- 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.
- 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.
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