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Order Book Dynamics with Liquidity Fluctuations: Asymptotic Analysis of Highly Competitive Regime Full article

Journal Mathematics
, E-ISSN: 2227-7390
Output data Year: 2023, Volume: 2023, Number: 11, Article number : 4235, Pages count : 24 DOI: 10.3390/math11204235
Tags limit order book; liquidity fluctuations; Markov process; large deviations; flash crash
Authors Rojas H 1 , Logachov A 2,3,4 , Yambartsev A 5
Affiliations
1 National Engineering University
2 Sobolev Institute of Mathematics
3 Department of Computer Science in Economics, Novosibirsk State Technical University (NSTU)
4 Department of Higher Mathematics, Siberian State University of Geosystems and Technologies (SSUGT)
5 Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo (USP)

Funding (1)

1 Sobolev Institute of Mathematics FWNF-2022-0010

Abstract: We introduce a class of Markov models to describe the bid–ask price dynamics in the presence of liquidity fluctuations. In a highly competitive regime, the spread evolution belongs to a class of Markov processes known as a population process with uniform catastrophes. Our mathematical analysis focuses on establishing the law of large numbers, the central limit theorem, and large deviations for this catastrophe-based model. Large deviation theory allows us to illustrate how huge deviations in the spread and prices can occur in the model. Moreover, our research highlights how these local trends and volatility are influenced by the typical values of the bid–ask spread. We calibrated the model parameters using available high-frequency data and conducted Monte Carlo numerical simulations to demonstrate its ability to reasonably replicate key phenomena in the presence of liquidity fluctuations.
Cite: Rojas H. , Logachov A. , Yambartsev A.
Order Book Dynamics with Liquidity Fluctuations: Asymptotic Analysis of Highly Competitive Regime
Mathematics. 2023. V.2023. N11. 4235 :1-24. DOI: 10.3390/math11204235 WOS Scopus РИНЦ OpenAlex
Dates:
Submitted: Oct 5, 2023
Accepted: Oct 8, 2023
Published print: Oct 10, 2023
Published online: Oct 10, 2023
Identifiers:
Web of science: WOS:001093512100001
Scopus: 2-s2.0-85175087424
Elibrary: 63498965
OpenAlex: W4387494375
Citing:
DB Citing
OpenAlex 1
Scopus 1
Web of science 1
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