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dc.contributor.authorIto, R.en
dc.date.accessioned2016-04-22T15:01:31Z
dc.date.available2016-04-22T15:01:31Z
dc.date.issued2016-01-24en
dc.identifier.otherCWPE1606
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/255283
dc.description.abstractThe Spline-DCS model is developed and applied to forecasting the high-frequency trade volume of selected equity and foreign currency exchange pairs. The cubic spline model of Harvey and Koopman (1993) is applied to capture intra-day periodic patterns. The model is robust to outliers as the dynamics of scale is driven by the score. The empirical application illustrates that Spline-DCS is a practical forecasting tool that is robust to the choice of sampling frequency or sampling period. The predictive performance of the model is compared with the state-of-the-art volume forecasting model, named the component-MEM, of Brownlees et al. (2011). The model can substantially outperform the component-MEM in minimizing common forecast error functions.en
dc.description.abstractprice impacten
dc.publisherFaculty of Economics
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.relation.isreplacedbyhttps://www.repository.cam.ac.uk/handle/1810/255272
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectrobustnessen
dc.subjectscoreen
dc.subjectvolume predictionen
dc.subjectVWAPen
dc.subjectslicing strategyen
dc.titleSpline-DCS for Forecasting Trade Volume in High-Frequency Financial Dataen
dc.typeWorking Paperen
dc.identifier.doi10.17863/CAM.5751


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