"Market making" behaviour in an order book model and its impact on the bid-ask spread

Ioane Muni Toke

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"Market making" behaviour in an order book model and its impact on the bid-ask spread

Ioane Muni Toke

Trading and Market Microstructure · Submitted 19 Mar 2010

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show that this simple feature enables a much more realistic treatment of the bid-ask spread of the simulated order book.

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What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.11 · human participants 0.02 · controlled comparison 0.46 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.61 · mentions code or data availability 0.03.

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arXiv listing abstract v2. Results and the main claim are underlined; show every role.

It has been suggested that marked point processes might be good candidates for the modelling of financial high-frequency data. A special class of point processes, Hawkes processes, has been the subject of various investigations in the financial community. In this paper, we propose to enhance a basic zero-intelligence order book simulator with arrival times of limit and market orders following mutually (asymmetrically) exciting Hawkes processes. Modelling is based on empirical observations on time intervals between orders that we verify on several markets (equity, bond futures, index futures). We show that this simple feature enables a much more realistic treatment of the bid-ask spread of the simulated order book.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	It has been suggested that marked point processes might be good candidates for the modelling of financial high-frequency data.	background 1.00	0.04	0.02	hedged possibility 0.53
s1	A special class of point processes, Hawkes processes, has been the subject of various investigations in the financial community.	background 1.00	0.04	0.02	asserted 0.96
s2	In this paper, we propose to enhance a basic zero-intelligence order book simulator with arrival times of limit and market orders following mutually (asymmetrically) exciting Hawkes processes.	contribution 0.69	0.89	0.07	asserted 0.70
s3	Modelling is based on empirical observations on time intervals between orders that we verify on several markets (equity, bond futures, index futures).	method 0.99	0.92	0.04	asserted 0.99
s4	We show that this simple feature enables a much more realistic treatment of the bid-ask spread of the simulated order book.	result 0.99	0.95	0.84	asserted 1.00

What kind of paper the classifier reports this is: simulation study 0.90 · evidence from simulation or computation 0.84.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

It has been suggested that marked point processes might be good candidates for the modelling of financial high-frequency data. A special class of point processes, Hawkes processes, has been the subject of various investigations in the financial community. In this paper, we propose to enhance a basic zero-intelligence order book simulator with arrival times of limit and market orders following mutually (asymmetrically) exciting Hawkes processes. Modelling is based on empirical observations on time intervals between orders that we verify on several markets (equity, bond futures, index futures). We show that this simple feature enables a much more realistic treatment of the bid-ask spread of the simulated order book.

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4 arXiv papers cite this in 7 sentences

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Cites it as background 1 paper

Lately more complex dynamics have been proposed, including Markovian models [ref] , Hawkes process driven bid-ask prices [ref] , or even Hawkes processes-based LOB models [ref] , the latter being reputed to efficiently capture the time clustering property of orders, and the cross-dependences in the order flow.

Statistical Inference for Ergodic Point Processes and Application to Limit Order Book · Introduction · 0.84

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