A bivariate integer-valued long-memory model for high-frequency financial count data. Issue 3 (1st February 2017)

• Record Type: Journal Article
• Authors: Quoreshi, A.M.M. Shahiduzzaman
• Is Part Of: Communications in statistics. Volume 46:Issue 3(2017)
• Page Start: 1080
• Page End: 1089
• Publication Date: 2017-02-01
• Subjects: Count data -- Intra-day -- Time series -- Estimation -- Reaction time -- Finance.; C13 -- C22 -- C25 -- C51 -- G12 -- G14; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2014.997361
• Languages: English
• ISSNs: 0361-0926

Abstract

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.