A bivariate integer-valued long-memory model for high-frequency financial count data
2017 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 46, no 3, 1080-1089 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Taylor & Francis, 2017. Vol. 46, no 3, 1080-1089 p.
Count data, Estimation, Finance, Intra-day, Reaction time, Time series, Human reaction time, Count datum, High frequency HF, Long-memory property, Negative correlation, Recursive process, Second order moment, Stock transaction, Bins
IdentifiersURN: urn:nbn:se:bth-13482DOI: 10.1080/03610926.2014.997361ISI: 000387274200004ScopusID: 2-s2.0-84994034965OAI: oai:DiVA.org:bth-13482DiVA: diva2:1049430