EconPapers    
Economics at your fingertips  
 

Spline-DCS for Forecasting Trade Volume in High-Frequency Finance

Ryoko Ito

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: 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
Date: 2016-01-24
Note: ri239
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1606.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:cam:camdae:1606

Access Statistics for this paper

More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Series data maintained by Jake Dyer ().

 
Page updated 2017-09-16
Handle: RePEc:cam:camdae:1606