A bivariate integer-valued long-memory model for high-frequency financial count data |
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Authors: | A.M.M. Shahiduzzaman Quoreshi |
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Affiliation: | Department of Industrial Economics and Management, Blekinge Institute of Technology, Karlskrona, Sweden |
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Abstract: | We propose a bivariate integer-valued fractional integrated (BINFIMA) model to account for the long-memory property 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 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. |
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Keywords: | Count data Intra-day Time series Estimation Reaction time Finance. |
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