High-dimensional Statistical Learning Techniques for Time-varying Limit Order Book Networks

52 Pages Posted: 24 Jan 2021 Last revised: 17 Feb 2021

See all articles by Shi Chen

Shi Chen

Karlsruhe Institute of Technology - Department of Economics and Management

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Melanie Schienle

Karlsruhe Institute of Technology (KIT)

Date Written: January 1, 2020

Abstract

This paper provides statistical learning techniques for determining the full own-price market impact and the relevance and effect of cross-price and cross-asset spillover channels from intraday transactions data. The novel tools allow extracting comprehensive information contained in the limit order books (LOB) and quantify their impacts on the size and structure of price interdependencies across stocks. For correct empirical network determination of such dynamic liquidity price effects even in small portfolios, we require high-dimensional statistical learning methods with an integrated general bootstrap procedure. We document the importance of LOB liquidity network spillovers even for a small blue-chip NASDAQ portfolio.

Keywords: limit order book, high-dimensional statistical learning, liquidity networks, high frequency dynamics, market impact, bootstrap

JEL Classification: C02, C13, C22, C45, G12

Suggested Citation

Chen, Shi and Härdle, Wolfgang K. and Schienle, Melanie, High-dimensional Statistical Learning Techniques for Time-varying Limit Order Book Networks (January 1, 2020). Available at SSRN: https://ssrn.com/abstract=3702349 or http://dx.doi.org/10.2139/ssrn.3702349

Shi Chen (Contact Author)

Karlsruhe Institute of Technology - Department of Economics and Management ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Chiao Tung University ( email )

No. 1001 Ta Hsueh Road
Hsinchu 300
Taiwan

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Melanie Schienle

Karlsruhe Institute of Technology (KIT) ( email )

Institute of Economics (ECON)
Karlsruhe
Germany

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