Bivariate Integer-Valued Long Memory Model for High Frequency Financial Count Data
We develop a model to account for the long memory property in a bivariate count data framework. We propose a bivariate integer-valued fractional integrated (BINFIMA) model and apply the model to high frequency stock transaction data. The BINFIMA model allows for both positive and negative correlations between the counts. The unconditional and conditional first and second order moments are given. The CLS and FGLS estimators are discussed. The model is capable of capturing the covariance between and within intra-day time series of high frequency transaction data due to macroeconomic news and news related to a specific stock. Empirically, it is found that Ericsson B has mean recursive process while AstraZeneca has long memory property. It is also found that Ericsson B and AstraZenica react in a similar way due to macroeconomic news.
|Date of creation:||02 Apr 2014|
|Date of revision:|
|Contact details of provider:|| Postal: CITR (Center for Innovation and Technology Research), Department of Industrial Economics, Blekinge Inst of Technology, 371 79 Karlskrona, Sweden|
Phone: 0455 - 38 50 00
Fax: 0455 - 38 50 57
Web page: http://www.bth.se/csir
More information through EDIRC
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.:
- Kurt Brannas & A. M. M. Shahiduzzaman Quoreshi, 2010.
"Integer-valued moving average modelling of the number of transactions in stocks,"
Applied Financial Economics,
Taylor & Francis Journals, vol. 20(18), pages 1429-1440.
- Brännäs, Kurt & Quoreshi, Shahiduzzaman, 2004. "Integer-Valued Moving Average Modelling of the Number of Transactions in Stocks," Umeå Economic Studies 637, Umeå University, Department of Economics.
- Quoreshi, Shahiduzzaman, 2005. "Bivariate Time Series Modelling of Financial Count Data," Umeå Economic Studies 655, Umeå University, Department of Economics.
- 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.
- Robert F. Engle, 1996.
"The Econometrics of Ultra-High Frequency Data,"
NBER Working Papers
5816, National Bureau of Economic Research, Inc.
- Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
- Geetesh Bhardwaj & Norman Swanson, 2004.
"An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series,"
Departmental Working Papers
200422, Rutgers University, Department of Economics.
- Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
- Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
- 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.
- Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," Umeå Economic Studies 673, Umeå University, Department of Economics.
When requesting a correction, please mention this item's handle: RePEc:hhs:bthcsi:2014-003. 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: (Sam Tavassoli)
If references are entirely missing, you can add them using this form.