Spline-DCS for Forecasting Trade Volume in High-Frequency Finance
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
We 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.
Keywords: order slicing; price impact; robustness; score; VWAP trading (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 C58 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:1606
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