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

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.

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Paper 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.

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Length: 32
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:fir:econom:wp2006_03
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  1. Boehmer, Ekkehart & Grammig, Joachim & Theissen, Erik, 2007. "Estimating the probability of informed trading--does trade misclassification matter?," Journal of Financial Markets, Elsevier, vol. 10(1), pages 26-47, February.
  2. Oomen, Roel C.A., 2006. "Properties of Realized Variance Under Alternative Sampling Schemes," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 219-237, April.
  3. Bauwens, Luc & Giot, Pierre & Grammig, Joachim & Veredas, David, 2004. "A comparison of financial duration models via density forecasts," International Journal of Forecasting, Elsevier, vol. 20(4), pages 589-609.
  4. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
  5. Bollerslev, Tim, 2001. "Financial econometrics: Past developments and future challenges," Journal of Econometrics, Elsevier, vol. 100(1), pages 41-51, January.
  6. BAUWENS, Luc & ROMBOUTS, Jeroen V.K., . "Econometrics," CORE Discussion Papers RP -1713, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Rombouts, Jeroen V. K. & Bauwens, Luc, 2004. "Econometrics," Papers 2004,33, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
  7. Blume, Marshall E & Goldstein, Michael A, 1997. " Quotes, Order Flow, and Price Discovery," Journal of Finance, American Finance Association, vol. 52(1), pages 221-44, March.
  8. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-46, June.
  9. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, Elsevier.
  10. Sofianos, George & Werner, Ingrid M., 2000. "The trades of NYSE floor brokers," Journal of Financial Markets, Elsevier, vol. 3(2), pages 139-176, May.
  11. W. Breymann & A. Dias & P. Embrechts, 2003. "Dependence structures for multivariate high-frequency data in finance," Quantitative Finance, Taylor & Francis Journals, vol. 3(1), pages 1-14.
  12. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
  13. Madhavan, Ananth & Sofianos, George, 1998. "An empirical analysis of NYSE specialist trading," Journal of Financial Economics, Elsevier, vol. 48(2), pages 189-210, May.
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