Journal article
Quasi-centralized limit order books
- Abstract:
- A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in which financial institutions can only access the trading opportunities offered by counterparties with whom they possess sufficient bilateral credit. In this paper, we perform an empirical analysis of a recent, high-quality data set from a large electronic trading platform that utilizes QCLOBs to facilitate trade. We argue that the quoterelative framework often used to study other LOBs is not a sensible reference frame for QCLOBs, so we instead introduce an alternative, trade-relative framework, which we use to study the statistical properties of order flow and LOB state in our data. We also uncover an empirical universality: although the distributions that describe order flow and LOB state vary considerably across days, a simple, linear rescaling causes them to collapse onto a single curve. Motivated by this finding, we propose a semiparametric model of order flow and LOB state for a single trading day. Our model provides similar performance to that of parametric curve-fitting techniques but is simpler to compute and faster to implement.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 6.7MB, Terms of use)
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- Publisher copy:
- 10.1080/14697688.2016.1247980
Authors
- Publisher:
- Taylor and Francis
- Journal:
- Quantitative Finance More from this journal
- Volume:
- 17
- Issue:
- 6
- Pages:
- 831-853
- Publication date:
- 2017-01-09
- Acceptance date:
- 2016-09-28
- DOI:
- EISSN:
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1469-7696
- ISSN:
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1469-7688
- Keywords:
- Pubs id:
-
pubs:647848
- UUID:
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uuid:92dc26f2-3d3b-43d9-b14b-26456fcd24e3
- Local pid:
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pubs:647848
- Source identifiers:
-
647848
- Deposit date:
-
2016-10-03
- ARK identifier:
Terms of use
- Copyright holder:
- Informa UK Limited
- Copyright date:
- 2017
- Notes:
- © 2017 Informa UK Limited, trading as Taylor and Francis Group. This is the accepted manuscript version of the article. The final version is available online from Taylor and Francis at: https://doi.org/10.1080/14697688.2016.1247980
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