Francesca Mariani, Maria Cristina Recchioni, Tai-Ho Wang, Roberto Giacalone
An empirical analysis, suggested by optimal Merton dynamics, reveals some unexpected features of asset volumes. These features are connected to traders' belief and risk aversion. This paper proposes a trading strategy model in the optimal Merton framework that is representative of the collective behavior of heterogeneous rational traders. This model allows for the estimation of the average risk aversion of traders acting on a specific risky asset, while revealing the existence of a price of risk closely related to market price of risk and volume rate. The empirical analysis, conducted on real data, confirms the validity of the proposed model.
{"title":"Can market volumes reveal traders' rationality and a new risk premium?","authors":"Francesca Mariani, Maria Cristina Recchioni, Tai-Ho Wang, Roberto Giacalone","doi":"arxiv-2406.05854","DOIUrl":"https://doi.org/arxiv-2406.05854","url":null,"abstract":"An empirical analysis, suggested by optimal Merton dynamics, reveals some\u0000unexpected features of asset volumes. These features are connected to traders'\u0000belief and risk aversion. This paper proposes a trading strategy model in the\u0000optimal Merton framework that is representative of the collective behavior of\u0000heterogeneous rational traders. This model allows for the estimation of the\u0000average risk aversion of traders acting on a specific risky asset, while\u0000revealing the existence of a price of risk closely related to market price of\u0000risk and volume rate. The empirical analysis, conducted on real data, confirms\u0000the validity of the proposed model.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are several approaches to modeling and forecasting time series as applied to prices of commodities and financial assets. One of the approaches is to model the price as a non-stationary time series process with heteroscedastic volatility (variance of price). The goal of the research is to generate probabilistic forecasts of day-ahead electricity prices in a spot marker employing stochastic volatility models. A typical stochastic volatility model - that treats the volatility as a latent stochastic process in discrete time - is explored first. Then the research focuses on enriching the baseline model by introducing several exogenous regressors. A better fitting model - as compared to the baseline model - is derived as a result of the research. Out-of-sample forecasts confirm the applicability and robustness of the enriched model. This model may be used in financial derivative instruments for hedging the risk associated with electricity trading. Keywords: Electricity spot prices forecasting, Stochastic volatility, Exogenous regressors, Autoregression, Bayesian inference, Stan
{"title":"Electricity Spot Prices Forecasting Using Stochastic Volatility Models","authors":"Andrei Renatovich Batyrov","doi":"arxiv-2406.19405","DOIUrl":"https://doi.org/arxiv-2406.19405","url":null,"abstract":"There are several approaches to modeling and forecasting time series as\u0000applied to prices of commodities and financial assets. One of the approaches is\u0000to model the price as a non-stationary time series process with heteroscedastic\u0000volatility (variance of price). The goal of the research is to generate\u0000probabilistic forecasts of day-ahead electricity prices in a spot marker\u0000employing stochastic volatility models. A typical stochastic volatility model -\u0000that treats the volatility as a latent stochastic process in discrete time - is\u0000explored first. Then the research focuses on enriching the baseline model by\u0000introducing several exogenous regressors. A better fitting model - as compared\u0000to the baseline model - is derived as a result of the research. Out-of-sample\u0000forecasts confirm the applicability and robustness of the enriched model. This\u0000model may be used in financial derivative instruments for hedging the risk\u0000associated with electricity trading. Keywords: Electricity spot prices\u0000forecasting, Stochastic volatility, Exogenous regressors, Autoregression,\u0000Bayesian inference, Stan","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In continuation of the macroscopic market making `a la Avellaneda-Stoikov as a control problem, this paper explores its stochastic game. Concerning the price competition, each agent is compared with the best quote from the others. We start with the linear case. While constructing the solution directly, the ordering property and the dimension reduction in the equilibrium are revealed. For the non-linear case, extending the decoupling approach, we introduce a multidimensional characteristic equation to study the well-posedness of forward-backward stochastic differential equations. Properties of coefficients in the characteristic equation are obtained via non-smooth analysis. In addition to novel well-posedness results, the linear price impact arises and the impact function can be further decomposed into two parts in some examples.
{"title":"Macroscopic Market Making Games","authors":"Ivan Guo, Shijia Jin, Kihun Nam","doi":"arxiv-2406.05662","DOIUrl":"https://doi.org/arxiv-2406.05662","url":null,"abstract":"In continuation of the macroscopic market making `a la Avellaneda-Stoikov as\u0000a control problem, this paper explores its stochastic game. Concerning the\u0000price competition, each agent is compared with the best quote from the others.\u0000We start with the linear case. While constructing the solution directly, the\u0000ordering property and the dimension reduction in the equilibrium are revealed.\u0000For the non-linear case, extending the decoupling approach, we introduce a\u0000multidimensional characteristic equation to study the well-posedness of\u0000forward-backward stochastic differential equations. Properties of coefficients\u0000in the characteristic equation are obtained via non-smooth analysis. In\u0000addition to novel well-posedness results, the linear price impact arises and\u0000the impact function can be further decomposed into two parts in some examples.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introducing an algebraic framework for modeling limit order books (LOBs) with tools from physics and stochastic processes, our proposed framework captures the creation and annihilation of orders, order matching, and the time evolution of the LOB state. It also enables compositional settings, accommodating the interaction of heterogeneous traders and different market structures. We employ Dirac notation and generalized generating functions to describe the state space and dynamics of LOBs. The utility of this framework is shown through simulations of simplified market scenarios, illustrating how variations in trader behavior impact key market observables such as spread, return volatility, and liquidity. The algebraic representation allows for exact simulations using the Gillespie algorithm, providing a robust tool for exploring the implications of market design and policy changes on LOB dynamics. Future research can expand this framework to incorporate more complex order types, adaptive event rates, and multi-asset trading environments, offering deeper insights into market microstructure and trader behavior and estimation of key drivers for market microstructure dynamics.
{"title":"An Algebraic Framework for the Modeling of Limit Order Books","authors":"Johannes Bleher, Michael Bleher","doi":"arxiv-2406.04969","DOIUrl":"https://doi.org/arxiv-2406.04969","url":null,"abstract":"Introducing an algebraic framework for modeling limit order books (LOBs) with\u0000tools from physics and stochastic processes, our proposed framework captures\u0000the creation and annihilation of orders, order matching, and the time evolution\u0000of the LOB state. It also enables compositional settings, accommodating the\u0000interaction of heterogeneous traders and different market structures. We employ\u0000Dirac notation and generalized generating functions to describe the state space\u0000and dynamics of LOBs. The utility of this framework is shown through\u0000simulations of simplified market scenarios, illustrating how variations in\u0000trader behavior impact key market observables such as spread, return\u0000volatility, and liquidity. The algebraic representation allows for exact\u0000simulations using the Gillespie algorithm, providing a robust tool for\u0000exploring the implications of market design and policy changes on LOB dynamics.\u0000Future research can expand this framework to incorporate more complex order\u0000types, adaptive event rates, and multi-asset trading environments, offering\u0000deeper insights into market microstructure and trader behavior and estimation\u0000of key drivers for market microstructure dynamics.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an asset pricing model in an incomplete market involving a large number of heterogeneous agents based on the mean field game theory. In the model, we incorporate habit formation in consumption preferences, which has been widely used to explain various phenomena in financial economics. In order to characterize the market-clearing equilibrium, we derive a quadratic-growth mean field backward stochastic differential equation (BSDE) and study its well-posedness and asymptotic behavior in the large population limit. Additionally, we introduce an exponential quadratic Gaussian reformulation of the asset pricing model, in which the solution is obtained in a semi-analytic form.
{"title":"Mean field equilibrium asset pricing model with habit formation","authors":"Masaaki Fujii, Masashi Sekine","doi":"arxiv-2406.02155","DOIUrl":"https://doi.org/arxiv-2406.02155","url":null,"abstract":"This paper presents an asset pricing model in an incomplete market involving\u0000a large number of heterogeneous agents based on the mean field game theory. In\u0000the model, we incorporate habit formation in consumption preferences, which has\u0000been widely used to explain various phenomena in financial economics. In order\u0000to characterize the market-clearing equilibrium, we derive a quadratic-growth\u0000mean field backward stochastic differential equation (BSDE) and study its\u0000well-posedness and asymptotic behavior in the large population limit.\u0000Additionally, we introduce an exponential quadratic Gaussian reformulation of\u0000the asset pricing model, in which the solution is obtained in a semi-analytic\u0000form.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"11960 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141254693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Algorithmic trading refers to executing buy and sell orders for specific assets based on automatically identified trading opportunities. Strategies based on reinforcement learning (RL) have demonstrated remarkable capabilities in addressing algorithmic trading problems. However, the trading patterns differ among market conditions due to shifted distribution data. Ignoring multiple patterns in the data will undermine the performance of RL. In this paper, we propose MOT,which designs multiple actors with disentangled representation learning to model the different patterns of the market. Furthermore, we incorporate the Optimal Transport (OT) algorithm to allocate samples to the appropriate actor by introducing a regularization loss term. Additionally, we propose Pretrain Module to facilitate imitation learning by aligning the outputs of actors with expert strategy and better balance the exploration and exploitation of RL. Experimental results on real futures market data demonstrate that MOT exhibits excellent profit capabilities while balancing risks. Ablation studies validate the effectiveness of the components of MOT.
算法交易是指根据自动识别的交易机会执行特定资产的买卖指令。基于强化学习（RL）的策略在解决算法交易问题方面表现出了卓越的能力。然而，由于分布数据的变化，不同市场条件下的交易模式也不尽相同。忽略数据中的多种模式将损害 RL 的性能。此外，我们还提出了预训练模块（Pretrain Module），通过将行为者的输出与专家策略相一致来促进模仿学习，从而更好地平衡 RL 的探索与利用。在真实期货市场数据上的实验结果表明，MOT 在平衡风险的同时表现出卓越的盈利能力。消融研究验证了 MOT 组件的有效性。
{"title":"MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading","authors":"Xi Cheng, Jinghao Zhang, Yunan Zeng, Wenfang Xue","doi":"arxiv-2407.01577","DOIUrl":"https://doi.org/arxiv-2407.01577","url":null,"abstract":"Algorithmic trading refers to executing buy and sell orders for specific\u0000assets based on automatically identified trading opportunities. Strategies\u0000based on reinforcement learning (RL) have demonstrated remarkable capabilities\u0000in addressing algorithmic trading problems. However, the trading patterns\u0000differ among market conditions due to shifted distribution data. Ignoring\u0000multiple patterns in the data will undermine the performance of RL. In this\u0000paper, we propose MOT,which designs multiple actors with disentangled\u0000representation learning to model the different patterns of the market.\u0000Furthermore, we incorporate the Optimal Transport (OT) algorithm to allocate\u0000samples to the appropriate actor by introducing a regularization loss term.\u0000Additionally, we propose Pretrain Module to facilitate imitation learning by\u0000aligning the outputs of actors with expert strategy and better balance the\u0000exploration and exploitation of RL. Experimental results on real futures market\u0000data demonstrate that MOT exhibits excellent profit capabilities while\u0000balancing risks. Ablation studies validate the effectiveness of the components\u0000of MOT.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Milionis et al.(2023) studied the rate at which automated market makers leak value to arbitrageurs when block times are discrete and follow a Poisson process, and where the risky asset price follows a geometric Brownian motion. We extend their model to analyze another popular mechanism in decentralized finance for onchain trading: Dutch auctions. We compute the expected losses that a seller incurs to arbitrageurs and expected time-to-fill for Dutch auctions as a function of starting price, volatility, decay rate, and average interblock time. We also extend the analysis to gradual Dutch auctions, a variation on Dutch auctions for selling tokens over time at a continuous rate. We use these models to explore the tradeoff between speed of execution and quality of execution, which could help inform practitioners in setting parameters for starting price and decay rate on Dutch auctions, or help platform designers determine performance parameters like block times.
{"title":"Loss-Versus-Fair: Efficiency of Dutch Auctions on Blockchains","authors":"Ciamac C. Moallemi, Dan Robinson","doi":"arxiv-2406.00113","DOIUrl":"https://doi.org/arxiv-2406.00113","url":null,"abstract":"Milionis et al.(2023) studied the rate at which automated market makers leak\u0000value to arbitrageurs when block times are discrete and follow a Poisson\u0000process, and where the risky asset price follows a geometric Brownian motion.\u0000We extend their model to analyze another popular mechanism in decentralized\u0000finance for onchain trading: Dutch auctions. We compute the expected losses\u0000that a seller incurs to arbitrageurs and expected time-to-fill for Dutch\u0000auctions as a function of starting price, volatility, decay rate, and average\u0000interblock time. We also extend the analysis to gradual Dutch auctions, a\u0000variation on Dutch auctions for selling tokens over time at a continuous rate.\u0000We use these models to explore the tradeoff between speed of execution and\u0000quality of execution, which could help inform practitioners in setting\u0000parameters for starting price and decay rate on Dutch auctions, or help\u0000platform designers determine performance parameters like block times.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minimizing execution costs for large orders is a fundamental challenge in finance. Firms often depend on brokers to manage their trades due to limited internal resources for optimizing trading strategies. This paper presents a methodology for evaluating the effectiveness of broker execution algorithms using trading data. We focus on two primary cost components: a linear cost that quantifies short-term execution quality and a quadratic cost associated with the price impact of trades. Using a model with transient price impact, we derive analytical formulas for estimating these costs. Furthermore, we enhance estimation accuracy by introducing novel methods such as weighting price changes based on their expected impact content. Our results demonstrate substantial improvements in estimating both linear and impact costs, providing a robust and efficient framework for selecting the most cost-effective brokers.
{"title":"Optimizing Broker Performance Evaluation through Intraday Modeling of Execution Cost","authors":"Zoltan Eisler, Johannes Muhle-Karbe","doi":"arxiv-2405.18936","DOIUrl":"https://doi.org/arxiv-2405.18936","url":null,"abstract":"Minimizing execution costs for large orders is a fundamental challenge in\u0000finance. Firms often depend on brokers to manage their trades due to limited\u0000internal resources for optimizing trading strategies. This paper presents a\u0000methodology for evaluating the effectiveness of broker execution algorithms\u0000using trading data. We focus on two primary cost components: a linear cost that\u0000quantifies short-term execution quality and a quadratic cost associated with\u0000the price impact of trades. Using a model with transient price impact, we\u0000derive analytical formulas for estimating these costs. Furthermore, we enhance\u0000estimation accuracy by introducing novel methods such as weighting price\u0000changes based on their expected impact content. Our results demonstrate\u0000substantial improvements in estimating both linear and impact costs, providing\u0000a robust and efficient framework for selecting the most cost-effective brokers.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"117 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it `HLOB'. This architecture (i) exploits the information encoded by an Information Filtering Network, namely the Triangulated Maximally Filtered Graph, to unveil deeper and non-trivial dependency structures among volume levels; and (ii) guarantees deterministic design choices to handle the complexity of the underlying system by drawing inspiration from the groundbreaking class of Homological Convolutional Neural Networks. We test our model against 9 state-of-the-art deep learning alternatives on 3 real-world Limit Order Book datasets, each including 15 stocks traded on the NASDAQ exchange, and we systematically characterize the scenarios where HLOB outperforms state-of-the-art architectures. Our approach sheds new light on the spatial distribution of information in Limit Order Books and on its degradation over increasing prediction horizons, narrowing the gap between microstructural modeling and deep learning-based forecasting in high-frequency financial markets.
{"title":"HLOB -- Information Persistence and Structure in Limit Order Books","authors":"Antonio Briola, Silvia Bartolucci, Tomaso Aste","doi":"arxiv-2405.18938","DOIUrl":"https://doi.org/arxiv-2405.18938","url":null,"abstract":"We introduce a novel large-scale deep learning model for Limit Order Book\u0000mid-price changes forecasting, and we name it `HLOB'. This architecture (i)\u0000exploits the information encoded by an Information Filtering Network, namely\u0000the Triangulated Maximally Filtered Graph, to unveil deeper and non-trivial\u0000dependency structures among volume levels; and (ii) guarantees deterministic\u0000design choices to handle the complexity of the underlying system by drawing\u0000inspiration from the groundbreaking class of Homological Convolutional Neural\u0000Networks. We test our model against 9 state-of-the-art deep learning\u0000alternatives on 3 real-world Limit Order Book datasets, each including 15\u0000stocks traded on the NASDAQ exchange, and we systematically characterize the\u0000scenarios where HLOB outperforms state-of-the-art architectures. Our approach\u0000sheds new light on the spatial distribution of information in Limit Order Books\u0000and on its degradation over increasing prediction horizons, narrowing the gap\u0000between microstructural modeling and deep learning-based forecasting in\u0000high-frequency financial markets.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we delve into the applications and extensions of the queue-reactive model for the simulation of limit order books. Our approach emphasizes the importance of order sizes, in conjunction with their type and arrival rate, by integrating the current state of the order book to determine, not only the intensity of order arrivals and their type, but also their sizes. These extensions generate simulated markets that are in line with numerous stylized facts of the market. Our empirical calibration, using futures on German bonds, reveals that the extended queue-reactive model significantly improves the description of order flow properties and the shape of queue distributions. Moreover, our findings demonstrate that the extended model produces simulated markets with a volatility comparable to historical real data, utilizing only endogenous information from the limit order book. This research underscores the potential of the queue-reactive model and its extensions in accurately simulating market dynamics and providing valuable insights into the complex nature of limit order book modeling.
{"title":"A Novel Approach to Queue-Reactive Models: The Importance of Order Sizes","authors":"Hamza Bodor, Laurent Carlier","doi":"arxiv-2405.18594","DOIUrl":"https://doi.org/arxiv-2405.18594","url":null,"abstract":"In this article, we delve into the applications and extensions of the\u0000queue-reactive model for the simulation of limit order books. Our approach\u0000emphasizes the importance of order sizes, in conjunction with their type and\u0000arrival rate, by integrating the current state of the order book to determine,\u0000not only the intensity of order arrivals and their type, but also their sizes.\u0000These extensions generate simulated markets that are in line with numerous\u0000stylized facts of the market. Our empirical calibration, using futures on\u0000German bonds, reveals that the extended queue-reactive model significantly\u0000improves the description of order flow properties and the shape of queue\u0000distributions. Moreover, our findings demonstrate that the extended model\u0000produces simulated markets with a volatility comparable to historical real\u0000data, utilizing only endogenous information from the limit order book. This\u0000research underscores the potential of the queue-reactive model and its\u0000extensions in accurately simulating market dynamics and providing valuable\u0000insights into the complex nature of limit order book modeling.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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