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High-dimensional statistical learning techniques for time-varying limit order book networks

Shi Chen, Wolfgang Härdle () and Melanie Schienle

No 2021-015, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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; network (search for similar items in EconPapers)
JEL-codes: C02 C13 C22 C45 G12 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-isf, nep-mst and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021015

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