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Intraday Seasonality in Analysis of UHF Financial Data: Models and Their Empirical Verification

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  • Roman Huptas

    (Cracow University of Economics)

Abstract

The aim of this paper is to outline the typical characteristics of the ultra-high-frequency financial data and to present estimation methods of intraday seasonality of trading activity. Ultra-high-frequency financial data (transactions data or tick-by-tick data) is defined to be a full record of transactions and their associated characteristics. We consider two nonparametric estimation methods: cubic splines and a Nadaraya-Watson kernel estimator of regression. Both approaches are compared empirically and applied to financial data of stocks traded at the Warsaw Stock Exchange.

Suggested Citation

  • Roman Huptas, 2009. "Intraday Seasonality in Analysis of UHF Financial Data: Models and Their Empirical Verification," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 128-138.
    • Handle: RePEc:cpn:umkdem:v:9:y:2009:p:128-138
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    References listed on IDEAS

    as
    1. Bauwens, Luc & Veredas, David, 2004. "The stochastic conditional duration model: a latent variable model for the analysis of financial durations," Journal of Econometrics, Elsevier, vol. 119(2), pages 381-412, April.
    2. Luc Bauwens & Pierre Giot, 2000. "The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks," Annals of Economics and Statistics, GENES, issue 60, pages 117-149.
    3. repec:adr:anecst:y:2000:i:60:p:05 is not listed on IDEAS
    4. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," LIDAM Discussion Papers CORE 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
    2. Hugh Christensen & Simon Godsill & Richard E Turner, 2020. "Hidden Markov Models Applied To Intraday Momentum Trading With Side Information," Papers 2006.08307, arXiv.org.
    3. Roman Huptas, 2019. "Point forecasting of intraday volume using Bayesian autoregressive conditional volume models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 293-310, July.
    4. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.

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