A bivariate integer-valued long-memory model for high-frequency financial count data

Shahiduzzaman Quoreshi

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 3, 1080-1089

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.

Date: 2017
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Working Paper: Bivariate Integer-Valued Long Memory Model for High Frequency Financial Count Data (2014)
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DOI: 10.1080/03610926.2014.997361

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