Financial Econometric Analysis at Ultra–High Frequency: Data Handling Concerns
AbstractThe 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.
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Bibliographic InfoPaper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number wp2006_03.
Date of creation: Oct 2006
Date of revision:
Ultra-high Frequency Data; ACD models; Outliers; New York Stock Exchange;
Other versions of this item:
- Brownlees, C.T. & Gallo, G.M., 2006. "Financial econometric analysis at ultra-high frequency: Data handling concerns," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2232-2245, December.
- NEP-ALL-2007-01-28 (All new papers)
- NEP-ECM-2007-01-28 (Econometrics)
- NEP-ETS-2007-01-28 (Econometric Time Series)
- NEP-MST-2007-01-28 (Market Microstructure)
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