Limit order books

By M. Gould, M. A. Porter, Stacy Williams, Mark McDonald, Daniel J. Fenn, Sam D. Howison. 2010

424 citations recorded by the source corpus.

Abstract Limit order books (LOBs) match buyers and sellers in more than half of the world’s financial markets. This survey highlights the insights that have emerged from the wealth of empirical and theoretical studies of LOBs. We examine the findings reported by statistical analyses of historical LOB data and discuss how several LOB models provide insight into certain aspects of the mechanism. We also illustrate that many such models poorly resemble real LOBs and that several well-established empirical facts have yet to be reproduced satisfactorily. Finally, we identify several key unresolved questions about LOBs.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/569339594ac5a39ca7f5fd88eee747688718f2c5

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Citations in the corpus

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “For classical references on market microstructure the reader is referred to [48], [49] and for a short review on LOBs in particular we refer to [7].”
Limit Order Books and Liquidity around Scheduled and Non-Scheduled Announcements: Empirical Evidence from NASDAQ Nordic (2017, neutral): “According to traditional asymmetric information models, liquidity reduces before announcements come and returns to normal, either quickly or gradually, after the announcements (see Graham et al., 2006, Table 1). It is important to make a distinction between scheduled and non-scheduled announcements.”
Testing Stylized Facts of Bitcoin Limit Order Books (2019, methodology): “Our notation builds upon the mathematical description of LOBs introduced by Gould et al. (2013).”
Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, background): “Characteristics of LOB are the resolution parameters [21] which are the tick size π (i.”
Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, background): “There are alternatively, certain “stylized facts” that are widely observed in market microstructure data, that are often evaluated qualitatively [56, 57].”
A one-level limit order book model with memory and variable spread (2017, neutral): “We refer to Gould et al. [9] for a nice survey of the extensive literature on the subject, which, among the most relevant to our work, includes Luckock [13], Kruk [12], Smith et al. [19], Rosu [17], Cont et al. [7], Abergel and Jedidi [1], Cont and Larrard [6], and Cont and Larrard [5].”
An estimation procedure for the Hawkes process (2015, methodology): “be modeled by a point process. In particular, our estimation method from Denition 3.6 allows to analyze the order ow in a Hawkes setup. For a detailed survey of order-book quantitative analysis; see Gould et al. (2013). Financial intraday histories are attractive for econometric research as there is”
The role of volume in order book dynamics: a multivariate Hawkes process analysis (2016, background): “Moreover, also the sequence of volumes presents nontrivial correlations (Gould et al. 2013).”