Konark Jain, Nick Firoozye, Jonathan Kochems, Philip Treleaven
{"title":"Limit Order Book Simulations: A Review","authors":"Konark Jain, Nick Firoozye, Jonathan Kochems, Philip Treleaven","doi":"arxiv-2402.17359","DOIUrl":null,"url":null,"abstract":"Limit Order Books (LOBs) serve as a mechanism for buyers and sellers to\ninteract with each other in the financial markets. Modelling and simulating\nLOBs is quite often necessary} for calibrating and fine-tuning the automated\ntrading strategies developed in algorithmic trading research. The recent AI\nrevolution and availability of faster and cheaper compute power has enabled the\nmodelling and simulations to grow richer and even use modern AI techniques. In\nthis review we \\highlight{examine} the various kinds of LOB simulation models\npresent in the current state of the art. We provide a classification of the\nmodels on the basis of their methodology and provide an aggregate view of the\npopular stylized facts used in the literature to test the models. We\nadditionally provide a focused study of price impact's presence in the models\nsince it is one of the more crucial phenomena to model in algorithmic trading.\nFinally, we conduct a comparative analysis of various qualities of fits of\nthese models and how they perform when tested against empirical data.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Computational Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2402.17359","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Limit Order Books (LOBs) serve as a mechanism for buyers and sellers to
interact with each other in the financial markets. Modelling and simulating
LOBs is quite often necessary} for calibrating and fine-tuning the automated
trading strategies developed in algorithmic trading research. The recent AI
revolution and availability of faster and cheaper compute power has enabled the
modelling and simulations to grow richer and even use modern AI techniques. In
this review we \highlight{examine} the various kinds of LOB simulation models
present in the current state of the art. We provide a classification of the
models on the basis of their methodology and provide an aggregate view of the
popular stylized facts used in the literature to test the models. We
additionally provide a focused study of price impact's presence in the models
since it is one of the more crucial phenomena to model in algorithmic trading.
Finally, we conduct a comparative analysis of various qualities of fits of
these models and how they perform when tested against empirical data.