Modelling Trades-Through in a Limit Order Book Using Hawkes Processes

Toke, Ioane Muni; Pomponio, Fabrizio

doi:10.5018/economics-ejournal.ja.2012-22

Cited by 33 publications

(27 citation statements)

References 26 publications

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“…Restricting previous Large model to market order flows at best bid and best ask, Muni-Toke and Pomponio [56] studied the dynamics of trade-through orders. A market order is a trade-through if part of it is executed at the next best limit.…”

Section: Level-i Book Modelsmentioning

confidence: 99%

“…Though very rare for some assets (e.g., Euro-Bund future), trade-through can be quite frequent on other contracts. For instance on the BNP stock [56] finds an average of 400 trade-throughs per day. Parametric estimation using MLE on exponential kernels have been performed on Euronext stocks restricting intraday-time to 9h30 to 11h30 am to avoid very strong intraday seasonality effects.…”

Section: Level-i Book Modelsmentioning

confidence: 99%

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Hawkes Processes in Finance

Bacry

Mastromatteo

Muzy

2015

Mark. Microstructure Liq.

401

345

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In this paper we propose an overview of the recent academic literature devoted to the applications of Hawkes processes in finance. Hawkes processes constitute a particular class of multivariate point processes that has become very popular in empirical high frequency finance this last decade. After a reminder of the main definitions and properties that characterize Hawkes processes, we review their main empirical applications to address many different problems in high frequency finance. Because of their great flexibility and versatility, we show that they have been successfully involved in issues as diverse as estimating the volatility at the level of transaction data, estimating the market stability, accounting for systemic risk contagion, devising optimal execution strategies or capturing the dynamics of the full order book.

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Section: Level-i Book Modelsmentioning

confidence: 99%

Section: Level-i Book Modelsmentioning

confidence: 99%

Hawkes Processes in Finance

Bacry

Mastromatteo

Muzy

2015

Mark. Microstructure Liq.

401

345

View full text Add to dashboard Cite

show abstract

“…More interestingly, there is a clear evidence that the more systemic the conditioning event is, the larger the expected number of assets swinging synchronously in the immediate future will be. In order to reproduce such empirical evidences, we propose a model within the class of mutually exciting point processes, termed Hawkes processes [21] which in recent years have experienced an increasing popularity in mathematical finance and econometrics [22,23,24,25,26,27,28,29,30]. We present a multidimensional, yet parsimonious, Hawkes process which captures with remarkable realism the cross-excitation affecting over-threshold events.…”

Section: Introductionmentioning

confidence: 99%

Collective synchronization and high frequency systemic instabilities in financial markets

Calcagnile¹,

Bormetti

Treccani³

et al. 2017

Quantitative Finance

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Recent years have seen an unprecedented rise of the role that technology plays in all aspects of human activities. Unavoidably, technology has heavily entered the Capital Markets trading space, to the extent that all major exchanges are now trading exclusively using electronic platforms. The ultra fast speed of information processing, order placement, and cancelling generates new dynamics which is still not completely deciphered.Analyzing a large dataset of stocks traded on the US markets, our study evidences that since 2001 the level of synchronization of large price movements across assets has significantly increased. Even though the total number of over-threshold events has diminished 1 www.quantlab.it 1 arXiv:1505.00704v1 [q-fin.ST] 4 May 2015 in recent years, when an event occurs, the average number of assets swinging together has increased. Quite unexpectedly, only a minor fraction of these events -regularly less than 40% along all years -can be connected with the release of pre-announced macroeconomic news. We also document that the larger is the level of sistemicity of an event, the larger is the probability -and degree of sistemicity -that a new event will occur in the near future. This opens the way to the intriguing idea that systemic events emerge as an effect of a purely endogenous mechanism. Consistently, we present a high-dimensional, yet parsimonious, model based on a class of self-and cross-exciting processes, termed Hawkes processes, which reconciles the modeling effort with the empirical evidence.

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“…In both cases, the model can then be fitted by numerical maximization of the log-likelihood of the model (see e.g. Muni Toke & Pomponio (2012) and references therein).…”

Section: A Toy Model For the Signature Performancementioning

confidence: 99%

Reconstruction of Order Flows using Aggregated Data

Toke

2016

Mark. Microstructure Liq.

Self Cite

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In this work we investigate tick-by-tick data provided by the TRTH database for several stocks on three different exchanges (Paris -Euronext, London and Frankfurt -Deutsche Börse) and on a 5-year span. We use a simple algorithm that helps the synchronization of the trades and quotes data sources, providing enhancements to the basic procedure that, depending on the time period and the exchange, are shown to be significant. We show that the analysis of the performance of this algorithm turns out to be a a forensic tool assessing the quality of the aggregated database: we are able to track through the data some significant technical changes that occurred on the studied exchanges. We also illustrate the fact that the choices made when reconstructing order flows have consequences on the quantitative models that are calibrated afterwards on such data. Our study also provides elements on the trade signature, and we are able to give a more refined look at the standard Lee-Ready procedure, giving new elements on the way optimal lags should be chosen when using this method. The findings are in line with both financial reasoning and the analysis of an illustrative Poisson model of the order flow.

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Modelling Trades-Through in a Limit Order Book Using Hawkes Processes

Cited by 33 publications

References 26 publications

Hawkes Processes in Finance

Hawkes Processes in Finance

Collective synchronization and high frequency systemic instabilities in financial markets

Reconstruction of Order Flows using Aggregated Data

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