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Intraday Trades Profile Estimation: An Intensity Approach

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  • Alessio Sancetta

Abstract

The intraday trades profile is the expected intensity of a counting process where the counts measure the number of trades over an interval. It needs to capture the salient features of the trading activity, its spikes, and periods of relative quietness. This calls for an estimator with a time varying resolution that allows us to identify jumps. The problem can be recast as a regression one, using a fused Lasso penalty. The framework allows us to identify jumps within possibly thousands different locations within a day when the number of trading days at disposal is in the order of hundreds. This can be done without imposing any conditions on the counting process except for certain regularity conditions on the expected intensity. The empirical results suggest that much of the trading activity in some liquid futures can be captured by a deterministic seasonal component in the trade arrival process.

Suggested Citation

  • Alessio Sancetta, 2023. "Intraday Trades Profile Estimation: An Intensity Approach," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 651-677.
    • Handle: RePEc:oup:jfinec:v:21:y:2023:i:3:p:651-677.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbab014
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    References listed on IDEAS

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    1. Davidson, James, 2020. "A new consistency proof for HAC variance estimators," Economics Letters, Elsevier, vol. 186(C).
    2. Mokkadem, Abdelkader, 1988. "Mixing properties of ARMA processes," Stochastic Processes and their Applications, Elsevier, vol. 29(2), pages 309-315, September.
    3. Sancetta, Alessio, 2018. "Estimation For The Prediction Of Point Processes With Many Covariates," Econometric Theory, Cambridge University Press, vol. 34(3), pages 598-627, June.
    4. 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.
    5. Luc Bauwens & Nikolaus Hautsch, 2009. "Modelling Financial High Frequency Data Using Point Processes," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 41, pages 953-979, Springer.
    6. Davidson, James, 1992. "A Central Limit Theorem for Globally Nonstationary Near-Epoch Dependent Functions of Mixing Processes," Econometric Theory, Cambridge University Press, vol. 8(3), pages 313-329, September.
    7. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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