Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 015

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

Shi Chen
Wolfgang Karl Härdle
Melanie Schienle

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 e ects 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.

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

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