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|
|Contact details of provider:|| Postal: CITR (Center for Innovation and Technology Research), Department of Industrial Economics, Blekinge Inst of Technology, 371 79 Karlskrona, Sweden|
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- 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.
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- 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.
- Quoreshi, Shahiduzzaman, 2005. "Bivariate Time Series Modelling of Financial Count Data," Umeå Economic Studies 655, Umeå University, Department of Economics.
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