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Financial Econometric Analysis at Ultra–High Frequency: Data Handling Concerns

Christian Brownlees () and Giampiero Gallo ()

Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti"

Abstract: The financial econometrics literature on Ultra High-Frequency Data (UHFD) has been growing steadily in recent years. However, it is not always straightforward to construct time series of interest from the raw data and the consequences of data handling procedures on the subsequent statistical analysis are not fully understood. Some results could be sample or asset specific and in this paper we address some of these issues focussing on the data produced by the New York Stock Exchange, summarizing the structure of their TAQ ultra high-frequency dataset. We review and present a number of methods for the handling of UHFD, and explain the rationale and implications of using such algorithms. We then propose procedures to construct the time series of interest from the raw data. Finally, we examine the impact of data handling on statistical modeling within the context of financial durations ACD models.

Keywords: Ultra-high Frequency Data; ACD models; Outliers; New York Stock Exchange (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
Date: 2006-10
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