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Nonlinearity in High-Frequency Financial Data and Hierarchical Models

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Author Info
Robert McCulloch (University of Chicago)
Ruey Tsay (University of Chicago)
Abstract

This paper studies nonlinear behavior of high-frequency financial data and employs nonlinear hierarchical models for analyzing such data. We illustrate the analysis by modeling the transaction-bytransaction data of IBM stock on the New York Stock Exchange for a period of 3 months. The variables considered include time durations between trades and price changes. For a short time span of 5 trading days, a simple threshold model is found adequate for modeling time durations between trades after adjusting for the diurnal pattern of the data. When price change and time duration between price changes are considered jointly, we use a hierarchical model that consists of 6 simple conditional models to handle the dynamic structure within a trading day and the variation between trading days for the whole sample. The model shows that dynamic structure exists in the high-frequency data, but there are some special days on which the behavior of the stock seems different from the others. We use Markov chain Monte Carlo methods to estimate the hierarchical model.

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Publisher Info
Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 5 (2001)
Issue (Month): 1 ()
Pages: 1067-1067
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Handle: RePEc:bep:sndecm:5:2001:1:1067-1067

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Related research
Keywords: autoregressive conditional durational model diurnal pattern generalized gamma distribution

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Wood, Robert A, 2000. "Market Microstructure Research Databases: History and Projections," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 140-45, April.
  2. 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.
  3. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2001. "A nonlinear autoregressive conditional duration model with applications to financial transaction data," Journal of Econometrics, Elsevier, vol. 104(1), pages 179-207, August. [Downloadable!] (restricted)
  4. Joel Hasbrouck, 1999. "The Dynamics of Discrete Bid and Ask Quotes," Journal of Finance, American Finance Association, vol. 54(6), pages 2109-2142, December. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Stanislav Anatolyev & Dmitry Shakin, 2006. "Trade intensity in the Russian stock market:dynamics, distribution and determinants," Working Papers w0070, Center for Economic and Financial Research (CEFIR). [Downloadable!]
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