 Identifying the underlying components of high-frequency data: Pure vs jump diffusion processesRodrigo Hizmeri, Marwan Izzeldin and Giovanni Urga Journal of Empirical Finance, 2025, vol. 81, issue C Abstract: In this paper, we examine the finite sample properties of test statistics designed to identify distinct underlying components of high-frequency financial data, specifically the Brownian component and infinite vs. finite activity jumps. We conduct a comprehensive set of Monte Carlo simulations to evaluate the tests under various types of microstructure noise, price staleness, and different levels of jump activity. We apply these tests to a dataset comprising 100 individual S&P 500 constituents from diverse business sectors and the SPY (S&P 500 ETF) to empirically assess the relative magnitude of these components. Our findings strongly support the presence of both Brownian and jump components. Furthermore, we investigate the time-varying nature of rejection rates and we find that periods with more jumps days are usually associated with an increase in infinite jumps and a decrease in finite jumps. This suggests a dynamic interplay between jump components over time. Keywords: High-frequency data; Infinite jumps; Finite jumps; Brownian motion; Price staleness; Microstructure noise (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:81:y:2025:i:c:s0927539825000167 DOI: 10.1016/j.jempfin.2025.101594 Access Statistics for this article Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff More articles in Journal of Empirical Finance from Elsevier |
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