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dc.contributor.authorIto, R.
dc.date.accessioned2016-08-11T15:25:18Z
dc.date.available2016-08-11T15:25:18Z
dc.date.issued2016-01-24
dc.identifier.otherCWPE1606
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/257163
dc.description.abstractWe develop the spline-DCS model and apply it to trade volume prediction, which remains a highly non-trivial task in high-frequency finance. Our application illustrates that the spline-DCS is computationally practical and captures salient empirical features of the data such as the heavy-tailed distribution and intra-day periodicity very well. We produce density forecasts of volume and compare the model's predictive performance with that of the state-of-the-art volume forecasting model, named the component-MEM, of Brownlees et al. (2011). The spline-DCS significantly outperforms the component-MEM in predicting intra-day volume proportions.en
dc.publisherFaculty of Economics
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.relation.replaceshttps://www.repository.cam.ac.uk/handle/1810/255283
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectorder slicing
dc.subjectprice impact
dc.subjectrobustness
dc.subjectscore
dc.subjectVWAP trading
dc.titleSpline-DCS for Forecasting Trade Volume in High-Frequency Finance
dc.typeWorking Paper
dc.identifier.doi10.17863/CAM.1091


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