Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamics

Ying Chen, Wee Song Chua and Wolfgang Härdle

Quantitative Finance, 2019, vol. 19, issue 9, 1473-1489

Abstract: We develop a dynamic model to simultaneously characterize the liquidity demand and supply in a limit order book. The joint dynamics are modeled in a unified Vector Functional AutoRegressive (VFAR) framework. We derive a closed-form maximum likelihood estimator under sieves and establish asymptotic consistency of the proposed method under mild conditions. We find the VFAR model presents strong interpretability and accurate out-of-sample forecasts. In application to limit order book records of 12 stocks in the NASDAQ, traded from 2 January 2015 to 6 March 2015, the VFAR model yields $R^2 $R2 values as high as 98.5% for in-sample estimation and 98.2% in out-of-sample forecast experiments. It produces accurate 5-, 25- and 50-min forecasts, with RMSE as low as 0.09–0.58 and MAPE as low as 0.3–4.5%. The predictive power stably reduces trading cost in the order splitting strategies and achieves excess gains of 31 basis points on average.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2019.1622290 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Forecasting limit order book liquidity supply-demand curves with functional AutoRegressive dynamics (2016)
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: https://EconPapers.repec.org/RePEc:taf:quantf:v:19:y:2019:i:9:p:1473-1489

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2019.1622290

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().