Modeling high-frequency order book data with Hawkes-Markovian process.
problem Capturing the dynamics of high-frequency order book events.
method Hawkes process with Markovian baseline intensities, LASSO regularization, and Akaike Information Criteria.
result Effective modeling of order book dynamics with reduced parameter redundancy.
This paper improves market making strategies by incorporating non-Markovian features in order book models.
problem Failure of order book models to accurately represent real market behavior.
method Identification of statistical properties, design of market making strategies, and comparison of performances.
result Incorporating non-Markovian features in order book models significantly improves market making strategies.
We develop a new framework to understand price formation in financial markets.
problem Understanding the dynamics of financial markets and the relationship between micro and macro features.
method A general Markovian framework for order book modeling, including state dependency and non-instantaneous impact of order book updates.
result The price process converges to a Brownian motion under general assumptions.
The paper proposes a time-dependent Markov model for a limit order book.
problem Understanding the convergence of a limit order book to a more complex diffusion.
method A simple time-dependent Markov model is proposed, describing the arrival of different orders.
result Empirical studies verify the validity of the modeling assumptions for certain stocks.
We propose and study a simple stochastic model for the dynamics of a limit order book, in which arrivals of market order, limit orders and order cancellations are described in terms of a Markovian queueing system. Through its analytical tractability, the model allows to obtain analytical expressions for various quantit…
A new model predicts price concavity and reversion after metaorder execution.
problem Modeling market response to exogenous trades on limit order books.
method Developed a Non-Markovian Zero Intelligence model with a time-weighted mid-price return function.
result The model predicts concave price paths and price reversion after metaorder execution.
We propose a model for the dynamics of a limit order book in a liquid market where buy and sell orders are submitted at high frequency. We derive a functional central limit theorem for the joint dynamics of the bid and ask queues and show that, when the frequency of order arrivals is large, the intraday dynamics of the…
In this paper, we develop a Markovian model that deals with the volume offered at the best quote of an electronic order book. The volume of the first limit is a stochastic process whose paths are periodically interrupted and reset to a new value, either by a new limit order submitted inside the spread or by a market or…
A new model for predicting market order book dynamics using a buffer Hawkes process.
problem Predicting the evolution of limit order books in financial markets.
method Introducing a Markovian single point process with a buffer mechanism and self-exciting effect.
result The model accurately predicts market order book dynamics and converges to Brownian motion.
Extends a model for limit order markets to more flexible distributions.
problem Modeling the dynamics of limit order markets with flexible distributions.
method Uses Markov renewal processes to model bid and ask queues, keeping analytical tractability.
result Calibrated model accurately represents market data for five stocks.
New model ranks financial agents based on trading quality.
problem Ranking financial agents based on trading quality.
method General non-linear order book model with individual agent behaviors.
result Closed-form formulas for trading metrics rank market makers.
Unified model for market dynamics, linking price and order flow.
problem Modeling market dynamics and order flow in a unified framework.
method Markovian market model driven by a hidden Brownian efficient price, signal-driven and queue-reactive models.
result Stability of mid-price around efficient price at macroscopic scale, behavior as diffusion.
Paper improves neural ODEs for forecasting non-Markovian processes.
problem Forecasting irregularly observed time series with incomplete data.
method Path-dependent Neural Jump ODEs with signature transform.
result Path-dependent NJ-ODE outperforms original framework in non-Markovian data.
A framework models order book dynamics using point processes and mass transport.
problem Capturing the complex dynamics of limit order books.
method Combines spatial point process for order flow and mass transport operator for market clearing.
result Provides insights into the interplay between order flow and price dynamics.
A new HOM model improves forecasting of Indian base metal prices.
problem Improving accuracy in predicting base metal prices in the Indian market.
method A Higher Order Markovian (HOM) model with varying order based on market delay.
result The HOM model consistently outperforms the standard Markovian model in forecasting.
Models predict order book recovery from aggressive trading follows a simple t^1/3 scaling.
problem Understanding order book dynamics in financial markets.
method Developed macroscopic models and solved numerically and asymptotically.
result Order book recovery follows a t^1/3 scaling law.
Proposes a stochastic model for limit order book dynamics.
problem Captures the dynamics of limit order books in financial markets.
method Develops a stochastic partial differential equation (SPDE) model with multiplicative noise.
result Shows efficient estimation and computation methods for the model.
Extends a market impact model to include mean-reversion, revealing new order book dynamics.
problem Understanding market impact in a latent order book model.
method Mean-reversion added to a minimal model, analyzed with mean-field assumption.
result New order book dynamics and price impact development shown.
Proposes a model for simulating limit order books with state-dependent intensities.
problem Simulating the dynamics of limit order books with varying intensities of order submission.
method Developed a parametric model with state-dependent intensities for limit orders, market orders, and cancellations. Introduced new models for order placement and cancellation selection.
result The proposed model accurately simulates the dynamics of limit order books and outperforms standard Poisson models.
The paper models stock order books with varying price changes.
problem Modeling stock order books with variable price changes.
method Developed a general semi-Markov model with two and multiple states.
result Validated the model with real data from multiple companies.
New neural network models limit order book dynamics efficiently.
problem Efficiently modeling price movements in the limit order book.
method Developed a spatial neural network architecture.
result Spatial neural network outperforms other models in risk management.
Paper uses ML for electricity price forecasting using order book data.
problem Forecasting German electricity spot market prices.
method Developed feature extraction for order book data, used cross-validation, compared neural networks and random forests to statistical models.
result Machine learning models outperform traditional approaches.
Model predicts limit order book dynamics based on market participant interactions.
problem Understanding and predicting the dynamics of the limit order book in financial markets.
method Agent-based model with informed, noise, and market maker traders; deduces limit order book from interactions.
result Link between price dynamics, trade proportions, volume, spread, and equilibrium state.
Model uses statistical physics principles to predict financial market volatility and returns.
problem Predicting price volatility and expected returns in financial markets.
method Inspired by statistical physics, the study introduces a physical model using Level 3 order book data to measure kinetic energy and momentum.
result The model outperforms traditional and machine learning approaches in forecasting volatility and expected returns.
New model reveals latent liquidity in financial markets.
problem Understanding the connection between latent and observable order books.
method Suggests a simple mechanism for revealing latent liquidity and quantifies it from real data.
result Existence of a market instability threshold leading to liquidity crises.
Study new Hawkes processes to model price changes in limit order books.
problem Model price volatility in limit order books.
method Prove LLN and FCLTs for general compound and regime-switching general compound Hawkes processes.
result Volatilities of price changes are expressed in terms of parameters describing arrival rates and price changes.
Paper introduces new Hawkes processes to model price changes in limit order books.
problem Modeling price volatility and order flow in limit order books.
method Introduces compound and regime-switching compound Hawkes processes, proving Law of Large Numbers and FCLTs.
result Volatilities of price changes are linked to parameters of arrival rates and price changes.
A new model for limit order book dynamics with time-dependent arrival rates.
problem Modeling the dynamics of limit order books with time-dependent arrival rates.
method Proposes a stochastic model with endogenous price dynamics and shows the conditional diffusion limit is Brownian meander.
result The model's conditional diffusion limit is the Brownian meander.
We study the analytical properties of a one-side order book model in which the flows of limit and market orders are Poisson processes and the distribution of lifetimes of cancelled orders is exponential. Although simplistic, the model provides an analytical tractability that should not be overlooked. Using basic result…
Model simulates correlation emergence in two coupled limit order books.
problem Modeling correlation emergence in coupled limit order books.
method Simulated two coupled diffusive limit order books using random walks in the fluid limit, with trader interactions.
result Demonstrated the recovery of an Epps effect from the model.
The paper analyzes fill probabilities in limit order books with varying price levels.
problem Determining the likelihood of limit orders being executed in a limit order book.
method Developed a state-dependent stochastic framework to model limit order book dynamics.
result Derived semi-analytical expressions for fill probabilities and mid-price changes.
A new method predicts non-Markovian closure terms for complex systems.
problem Predicting the effect of unresolved variables on resolved dynamics in high-dimensional systems.
method Mamba-Assisted Closure (MAC) framework: sequence model trained to predict closure from resolved trajectory, coupled with reduced-order equations.
result Substantially outperforms existing methods in predictive accuracy and long-time stability.
New neural network predicts stock price jumps using limit order book data.
problem Predicting short-term price movements in stock markets.
method Attention-based Convolutional Long Short-Term Memory network architecture.
result Attention mechanism improves jump prediction performance.
When modelling stock market dynamics, the price formation is often based on an equilbrium mechanism. In real stock exchanges, however, the price formation is goverend by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more g…
Deep learning predicts Bitcoin spot price movements from order books.
problem Predicting cryptocurrency spot price movements from order book data.
method Temporal CNNs trained on 2-second prediction time horizon.
result 71% walk-forward accuracy on coinbase data.
Models interactions among market participants in a financial asset's order book.
problem Understanding dynamic interactions among market participants in a financial asset's order book.
method Derives variational partial differential equations for MM and HFT strategies, and explains almost optimal control.
result Illustrates interactions between market participants through simulations of an order book.
The distribution of returns in financial time series exhibits heavy tails. In empirical studies, it has been found that gaps between the orders in the order book lead to large price shifts and thereby to these heavy tails. We set up an agent based model to study this issue and, in particular, how the gaps in the order …
Paper models limit order book with informed traders and market makers.
problem Modeling the limit order book with heterogeneous market participants.
method Agent-based model with four types of participants: informed traders, noise traders, informed market makers, and noise market makers. Based on Glosten-Milgrom and Huang-Rosenbaum-Saliba approaches.
result Derived the static limit order book characteristics and compared them with existing models.
We develop a second-order model for limit order books in a single scaling regime.
problem Modeling price and volume dynamics in a limit order book with market and limit orders at a common time scale.
method Established a first- and second-order approximation for an infinite dimensional limit order book model.
result Proved the existence and uniqueness of a solution for the second-order approximation.
This paper consists of two parts. The first part is devoted to empirical analysis of consolidated order book (COB) for the index RTS futures. In the second part we consider Poissonian multi--agent model of the COB. By varying parameters of different groups of agents submitting orders to the book we are able to model va…
Improved queue-reactive model considers order sizes for better market simulation.
problem Accurately modeling market dynamics and order flow properties.
method Integrates order sizes, type, and arrival rate into queue-reactive model.
result Extended model produces markets with volatility matching historical data.
Simulates financial market orders using anomalous diffusion models.
problem Anomalous diffusion in financial market order dynamics.
method Discrete Time Random Walk with Sibuya waiting times, non-uniform sampling, and cubic spline interpolation.
result Demonstrates price impact for different forcing functions and model parameters.
Proposes a model combining order book data and herd behavior to replicate long-range memory in financial returns.
problem Replicating long-range memory in financial returns and trading activity.
method Combines empirical order book data and financial herd behavior model.
result Model successfully replicates long-range memory in absolute returns and trading activity.
In financial markets, liquidity is not constant over time but exhibits strong seasonal patterns. In this article we consider a limit order book model that allows for time-dependent, deterministic depth and resilience of the book and determine optimal portfolio liquidation strategies. In a first model variant, we propos…
The paper analyzes order positions and queues in limit order books.
problem Understanding the dynamics of order positions and queues in limit order books.
method Fluid and diffusion limits, fluctuations analysis, explicit expressions derivation.
result Explicit analytical expressions for various quantities in limit order books.
Model simulates sparse order books in illiquid markets.
problem Inaccurate LOB models in illiquid markets.
method Inhomogeneous Poisson process for order arrivals and cancellations.
result Enhanced understanding of LOB dynamics in illiquid markets.
Two price regimes identified in limit order books: close and far from quotes.
problem Understanding the distribution and behavior of limit orders in limit order books.
method Analysis of limit order book data in dimensions of price, time, lifetime, and volume.
result Identification of two distinct regimes in the limit order book: close and far from quotes.
Study state-dependent Hawkes processes for limit order book modeling.
problem Modeling feedback loop between order flow and limit order book shape.
method Existence and uniqueness of state-dependent Hawkes processes, simulation, maximum likelihood estimation.
result Excitation effects in order flow are strongly state-dependent.