A Stochastic Model for Order Book Dynamics

By Rama Cont, Sasha Stoikov, Rishi Talreja. 2010

500 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

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Citations in the corpus

Universal features of price formation in financial markets: perspectives from deep learning (2018, background): “However, as observed in queueing models (Cont et al. 2010, Cont and de Larrard 2013, FigueroaLopez and Chavez-Casillas 2017), even under simplifying assumptions, the relation between this probability and various measures of the bid/ask imbalance is not linear.”
OPTIMAL TRADE EXECUTION AND PRICE MANIPULATION IN ORDER BOOKS WITH TIME‐VARYING LIQUIDITY (2011, methodology): “We refer the reader to Cont, Stoikov, and Talreja (2010) for a more comprehensive introduction to limit order books.”
Micro-price trading in an order-driven market (2014, background): “propose a stochastic model of the order book and demonstrate how the model predictions accounting for depth imbalances may be used in a trading strategy [32].”
Statistical Modeling of High-Frequency Financial Data (2011, methodology): “Cont, Stoikov and Talreja [21] study a Markovian queueing model in which arrivals of market orders and limit orders at each price level are independent Poisson processes and compute analytically, using simple matrix computations and Laplace transform methods, the distribution of time to ﬁll…”
A one-level limit order book model with memory and variable spread (2017, background): “Although the general rules governing the order book in this setting create a relatively complex dynamics, we are still capable of computing several features of the LOB model of relevance in high-frequency trading (see [7] for this type of applications).”
Statistical inference for ergodic point processes and application to Limit Order Book (2015, methodology): “An illustration of this model can be found in [13].”
Ergodicity and scaling limit of a constrained multivariate Hawkes process (2013, unknown): “In contrast, the Markov models proposed in [17, 16, 1] use simple Poisson process and they do not take into account the cross-interaction between different events.”
Sequence classification of the limit order book using recurrent neural networks (2017, background): “for trading and intraday risk management. The complex relation between order book dynamics and price movements has been the focus of econometric and stochastic modeling (see Engle and Russell (1998); Cont et al. (2010, 2014); Cont and de Larrard (2013); Chavez-Casillas and Figueroa-Lopez (2017) and ”
Deep learning for limit order books (2016, unknown): “Cont et al. (2010), Cont and De Larrard (2013), Cont and De Larrard (2012), Abergel and Jedidi (2013), Donier et al. (2015), Moallemi and Saglam (2013), Maglaras et al. (2014), Carmona and Webster (2013), Carmona and Webster (2012a), Carmona and Webster (2012b), Avellaneda and Stoikov (2008),…”
No‐arbitrage implies power‐law market impact and rough volatility (2018, background): “is a generalization of the Poisson process which is usually considered when modeling order flows, see among others [7, 8, 28].”
The role of volume in order book dynamics: a multivariate Hawkes process analysis (2016, background): “Another stream (Challet and Stinchcombe 2001, Cont et al. 2010, Abergel and Jedidi 2015) models the order flow with a random process and explores the resulting dynamics.”
Modelling high-frequency limit order book dynamics with support vector machines (2015, methodology): “In the former approach, statistical properties of the limit order book for the target ﬁnancial asset are developed and conditional quantities are then derived and modelled (Rosu 2009, Cont et al. 2010, Shek 2011, Cont 2011, He and Kercheval 2012).”
A High Frequency Trade Execution Model for Supervised Learning (2017, unknown): “The complex relation between order book dynamics and price movements has been the focus of econometric and stochastic modeling (see Engle and Russell (1998); Cont et al. (2010b, 2014); Cont and de Larrard (2013); Chavez-Casillas and Figueroa-Lopez (2017) and references therein).”
Econophysics review: II. Agent-based models (2011, unknown): “Under certain (strong) modeling constraints, we can see the order book as a Markov chain and look for analytical results (Cont et al. 2008).”
Order Book Dynamics in Liquid Markets: Limit Theorems and Diffusion Approximations (2012, methodology): “Instead of keeping track of arrival, cancelation and execution of orders at all price levels (as in Cont et al. (2010b), Smith et al. (2003)), we treat the queue sizes after a price change as a stationary sequence of random variables whose distribution represents the depth of the order book in a…”