Arbeitspapier
Time-varying Limit Order Book Networks
This paper analyzes the market impact of limit order books (LOB) taking crossstock effects into account. Based on penalized vector autoregressive approach, we aim to identify significance and magnitude of the directed network channels within and between LOBs by bootstrapped impulse response functions. Moreover, information on asymmetries and imbalances within the LOB over time would be derived. For the sample of a NASDAQ blue-chip portfolio during 06-07/2016 we find that LOB network effects crucially determine prices and bid-ask asymmetries are prevalent.
- Sprache
-
Englisch
- Erschienen in
-
Series: IRTG 1792 Discussion Paper ; No. 2018-016
- Klassifikation
-
Wirtschaft
Mathematical Methods
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Neural Networks and Related Topics
Asset Pricing; Trading Volume; Bond Interest Rates
- Thema
-
limit order book
high dimension
generalized impulse response
high frequency
market risk
market impact
network
bootstrap
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Härdle, Wolfgang Karl
Chen, Shi
Liang, Chong
Schienle, Melanie
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (wo)
-
Berlin
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Härdle, Wolfgang Karl
- Chen, Shi
- Liang, Chong
- Schienle, Melanie
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2018
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