Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/241272
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2021-015
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
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.
Schlagwörter: 
limit order book
high-dimensional statistical learning
liquidity networks
high frequency dynamics
market impact
bootstrap
network
JEL: 
C02
C13
C22
C45
G12
Dokumentart: 
Working Paper

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