Modeling dynamic diurnal patterns in high frequency financial data
Cambridge Working Papers in Economics
Faculty of Economics
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Ito, R. (2013). Modeling dynamic diurnal patterns in high frequency financial data. https://doi.org/10.17863/CAM.5637
A spline-DCS model is developed to forecast the conditional distribution of high-frequency financial data with periodic behavior. The dynamic cubic spline of Harvey and Koopman (1993) is applied to allow diurnal patterns to evolve stochastically over time. An empirical application illustrates the practicality and impressive predictive performance of the model.
outlier; robustness, score, calendar effect, spline, trade volume.
This record's DOI: https://doi.org/10.17863/CAM.5637
This record's URL: https://www.repository.cam.ac.uk/handle/1810/244759
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