Bivariate Time Series Modelling of Financial Count Data
AbstractA 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 655.
Length: 17 pages
Date of creation: 14 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
Count data; Intra-day; High frequency; Time series; Estimation; Long memory; Finance;
Find related papers by 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 &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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-04-16 (All new papers)
- NEP-ECM-2005-04-16 (Econometrics)
- NEP-ETS-2005-04-16 (Econometric Time Series)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," UmeÃ¥ Economic Studies 673, Umeå University, Department of Economics.
- 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.
- 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. "Time Series Modelling Of High Frequency Stock Transaction Data," UmeÃ¥ Economic Studies 675, Umeå University, Department of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Kjell-Göran Holmberg).
If references are entirely missing, you can add them using this form.