Modelling High Frequency Financial Count Data
This thesis comprises two papers concerning modelling of financial count data. The papers advance the integer-valued moving average model (INMA), a special case of integer-valued autoregressive moving average (INARMA) model class, and apply the models to the number of stock transactions in intra-day data. Paper  advances the INMA model to model the number of transactions in stocks in intra-day data. 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 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. Paper  introduces a bivariate integer-valued moving average model (BINMA) and applies the BINMA model to the number of stock transactions in intra-day data. The BINMA model allows for both positive and negative correlations between the count data series. The study shows that the correlation between series in the BINMA model is always smaller than 1 in an absolute sense. The conditional mean, variance and covariance 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.
|Date of creation:||20 Apr 2005|
|Date of revision:|
|Contact details of provider:|| Postal: Department of Economics, Umeå University, S-901 87 Umeå, Sweden|
Phone: 090 - 786 61 42
Fax: 090 - 77 23 02
Web page: http://www.econ.umu.se/
More information through EDIRC
References listed on IDEAS
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.:
- Richard Blundell & Rachel Griffith & Frank Windmeijer, 1999.
"Individual effects and dynamics in count data models,"
IFS Working Papers
W99/03, Institute for Fiscal Studies.
- Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
- Heinen, Andreas, 2003.
"Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model,"
8113, University Library of Munich, Germany.
- 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).
- Gourieroux Christian & Monfort Alain & Trognon A, 1982.
"Pseudo maximum lilelihood methods : applications to poisson models,"
CEPREMAP Working Papers (Couverture Orange)
- Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-20, May.
- 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).
- Nikas Rudholm, 2001. "Entry and the Number of Firms in the Swedish Pharmaceuticals Market," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 19(3), pages 351-364, November.
- Brännäs, Kurt & Hellström, Jörgen & Nordström, Jonas, 1999.
"A New Approach to Modelling and Forecasting Monthly Guest Nights in Hotels,"
Umeå Economic Studies
503, Umeå University, Department of Economics.
- 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.
- Tina Hviid Rydberg & Neil Shephard, 2000. "BIN Models for Trade-by-Trade Data. Modelling the Number of Trades in a Fixed Interval of Time," Econometric Society World Congress 2000 Contributed Papers 0740, Econometric Society.
- 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.
When requesting a correction, please mention this item's handle: RePEc:hhs:umnees:0656. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (David Skog)
If references are entirely missing, you can add them using this form.