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Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data

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  • Ito, Ryoko

Abstract

We introduce the spline-DCS model with a dynamic cubic spline as a way of capturing periodic behavior in financial data that evolves over time. Our empirical application provides evidence for changing diurnal patterns in the high-frequency financial data we study. We illustrate that this generalization can lead to an improvement in the quality of the fit of the model to the empirical distribution of data, especially in the tail region, for an extended out-of-sample period. Moreover, it can lead to a substantial improvement in predicting intra-day volume proportions, which is useful for Volume-Weighted Average Price stratategies. Our novel approach gives new insights into regular trading behavior and how it responds to changing market conditions.

Suggested Citation

  • Ito, Ryoko, 2013. "Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1315
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    References listed on IDEAS

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    1. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Intra-daily Volume Modeling and Prediction for Algorithmic Trading," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(3), pages 489-518, Summer.
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    6. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
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    Cited by:

    1. Adam Clements & Joanne Fuller & Vasilios Papalexiou, 2015. "Public news flow in intraday component models for trading activity and volatility," NCER Working Paper Series 106, National Centre for Econometric Research.

    More about this item

    Keywords

    order slicing; price impact; spline; volume prediction; score; seasonality;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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