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Nonlinearity in High-Frequency Financial Data and Hierarchical Models

Author

Listed:
  • McCulloch Robert E.

    (University of Chicago)

  • Tsay Ruey S.

    (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.

Suggested Citation

  • McCulloch Robert E. & Tsay Ruey S., 2001. "Nonlinearity in High-Frequency Financial Data and Hierarchical Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(1), pages 1-18, April.
    • Handle: RePEc:bpj:sndecm:v:5:y:2001:i:1:n:1
    DOI: 10.2202/1558-3708.1067
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    References listed on IDEAS

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    1. Wood, Robert A, 2000. "Market Microstructure Research Databases: History and Projections," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 140-145, 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. Ghysels, Eric, 2000. "Some Econometric Recipes for High-Frequency Data Cooking," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 154-163, April.
    4. 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.
    5. Joel Hasbrouck, 1999. "The Dynamics of Discrete Bid and Ask Quotes," Journal of Finance, American Finance Association, vol. 54(6), pages 2109-2142, December.
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    Cited by:

    1. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
    2. Dionne, Georges & Zhou, Xiaozhou, 2016. "The Dynamics of Ex-ante High-Frequency Liquidity: An Empirical Analysis," Working Papers 15-5, HEC Montreal, Canada Research Chair in Risk Management.
    3. Stanislav Anatolyev & Dmitry Shakin, 2007. "Trade intensity in the Russian stock market: dynamics, distribution and determinants," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 87-104.
    4. Simeon Coleman & Vitor Leone, 2015. "An investigation of regime shifts in UK commercial property returns: a time series analysis," Applied Economics, Taylor & Francis Journals, vol. 47(60), pages 6479-6492, December.
    5. Lumengo BONGA-BONGA, 2010. "Modeling Stock Returns in the South African Stock Exchange: a Nonlinear Approach," EcoMod2010 259600034, EcoMod.
    6. Georges Dionne & Xiaozhou Zhou, 2020. "The dynamics of ex-ante weighted spread: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 593-617, April.

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