In this paper, we study various new Hawkes processes. Specifically, we construct general compound Hawkes processes and investigate their properties in limit order books. With regards to these general compound Hawkes processes, we prove a Law of Large Numbers (LLN) and a Functional Central Limit Theorems (FCLT) for seve…
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48 results for “mid-price process”
Paper examines GCHP for mid-price prediction in financial data.
problem Predicting mid-price in financial data with stochastic models.
method General Compound Hawkes Process (GCHP) for mid-price prediction.
result GCHP models show potential for predicting mid-price direction and volatility.
Study applies Hawkes volatility to mid-price process for real-time risk management.
problem Lack of studies on Hawkes volatility for tick-level price dynamics.
method Derived variance formula for unmarked and marked Hawkes models, applied to mid-price process.
result Reliable results and high predictive power of intraday Hawkes volatility.
ALPE improves mid-price forecasting in HFT with real-time data.
problem Real-time mid-price forecasting in high-frequency trading.
method Adaptive Learning Policy Engine (ALPE) using RL and adaptive epsilon decay.
result ALPE outperforms other models in mid-price forecasting.
We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a p…
We derive a continuous time model for the joint evolution of the mid price and the bid-ask spread from a multiscale analysis of the whole limit order book (LOB) dynamics. We model the LOB as a multiclass queueing system and perform our asymptotic analysis using stylized features observed empirically. We argue that in t…
We propose an analytically tractable class of models for the dynamics of a limit order book, described through a stochastic partial differential equation (SPDE) with multiplicative noise for the order book centered at the mid-price, along with stochastic dynamics for the mid-price which is consistent with the order flo…
Beyond the Bid-Ask: Strategic Insights into Spread Prediction and the Global Mid-Price Phenomenonq-fin.TR
Study examines new financial metrics and their implications for trading and risk management.
problem Liquidity and price dynamics in financial markets.
method High-frequency trading data, ARMA(1,1)-GARCH(1,1) model, normal inverse Gaussian distribution, option pricing model, Rachev ratio.
result New financial metrics (TMOBBAS, GMP) have heavy-tailed distributions and significant deviations from normality.
In most illiquid markets, there is no obvious proxy for the market price of an asset. The European corporate bond market is an archetypal example of such an illiquid market where mid-prices can only be estimated with a statistical model. In this OTC market, dealers / market makers only have access, indeed, to partial i…
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Marketsecon.EM
Bayesian model predicts mid-price dynamics in financial markets.
problem Challenges in predicting financial markets using traditional methods.
method Bayesian bilinear neural network with temporal attention.
result Feasibility and advantages of Bayesian deep-learning approach.
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features …
At the ultra high frequency level, the notion of price of an asset is very ambiguous. Indeed, many different prices can be defined (last traded price, best bid price, mid price,...). Thus, in practice, market participants face the problem of choosing a price when implementing their strategies. In this work, we propose …
Deep learning predicts stock price changes in Limit Order Books.
problem Predicting high-frequency Limit Order Book mid-price changes.
method Cutting-edge deep learning methodologies applied to NASDAQ stocks.
result Deep learning methods' effectiveness varies by stock microstructure.
The paper models battery valuation in intraday electricity markets, incorporating liquidity costs.
problem Valuing batteries in intraday electricity markets considering liquidity costs.
method Stochastic model for mid-prices combined with a deterministic model for liquidity costs, using dynamic programming for optimization.
result Liquidity costs significantly impact battery valuation, especially with multiple batteries.
HLOB predicts mid-price changes in L.O.Bs using deep learning.
problem Forecasting mid-price changes in Limit Order Books.
method HLOB uses a deep learning model with an Information Filtering Network and Homological Convolutional Neural Networks.
result HLOB outperforms state-of-the-art models in real-world datasets.
We propose a framework to study optimal trading policies in a one-tick pro-rata limit order book, as typically arises in short-term interest rate futures contracts. The high-frequency trader has the choice to trade via market orders or limit orders, which are represented respectively by impulse controls and regular con…
This work's purpose is to understand the dynamics of limit order books in order-driven markets. We try to illustrate a dynamical trading mechanism attached to the microstructure of limit order markets. We capture the iterative nature of trading processes, which is critical in the dynamics of bid-ask pairs and the switc…
Maker-taker fees can prevent algorithmic cooperation in market making, but not always.
problem Unexpected cooperation among independent algorithms in market making.
method Modeling market making as a repeated game, experimental analysis of transaction costs and rebates.
result Maker-taker fee models can destabilize cooperation, but not always with a specific relationship between costs and rebates.
We study the multi-level order-flow imbalance (MLOFI), which is a vector quantity that measures the net flow of buy and sell orders at different price levels in a limit order book (LOB). Using a recent, high-quality data set for 6 liquid stocks on Nasdaq, we fit a simple, linear relationship between MLOFI and the conte…
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicatorsq-fin.ST
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We f…
This paper proposes a parametric approach for stochastic modeling of limit order markets. The models are obtained by augmenting classical perfectly liquid market models by few additional risk factors that describe liquidity properties of the order book. The resulting models are easy to calibrate and to analyze using st…
Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methodscs.CE
Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price pre…
Given a stationary point process, an intensity burst is defined as a short time period during which the number of counts is larger than the typical count rate. It might signal a local non-stationarity or the presence of an external perturbation to the system. In this paper we propose a novel procedure for the detection…
Deep learning predicts cryptocurrency price movements with 78% accuracy.
problem Predicting price formation in cryptocurrency markets with high volatility and illiquidity.
method Applied deep learning to predict mid-price changes on live tick-level cryptocurrency data.
result Achieved 78% accuracy in predicting mid-price movement of Bitcoin vs USD.
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.
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
problem Understanding responsive speeds of market participants in high-frequency trading.
method Multi-kernel Hawkes models with conditional Hessian analysis for optimization.
result Existence of multi-kernels (UHF, VHF, HF) in high-frequency price dynamics.
We propose a new model for the level I of a Limit Order Book (LOB), which incorporates the information about the standing orders at the opposite side of the book after each price change and the arrivals of new orders within the spread. Our main result gives a diffusion approximation for the mid-price process. To illust…
We model the arrival of mid-price changes in the E-Mini S&P futures contract as a self-exciting Hawkes process. Using several estimation methods, we find that the Hawkes kernel is power-law with a decay exponent close to -1.15 at short times, less than approximately 10^3 seconds, and crosses over to a second power-law …
The paper introduces a new price model based on entropy that better fits high-frequency market data.
problem Understanding fair prices in high-frequency markets with bid-ask imbalance.
method A parametrized family of prices derived from the Maximum Entropy Principle, minimizing bias given volume imbalance.
result The model can generate higher kurtosis and heavy-tailed distributions compared to standard models.
Optimal Liquidity Provisionq-fin.TR
A small investor provides liquidity at the best bid and ask prices of a limit order market. For small spreads and frequent orders of other market participants, we explicitly determine the investor's optimal policy and welfare. In doing so, we allow for general dynamics of the mid price, the spread, and the order flow, …
We consider an optimal trading problem over a finite period of time during which an investor has access to both a standard exchange and a dark pool. We take the exchange to be an order-driven market and propose a continuous-time setup for the best bid price and the market spread, both modelled by Lévy processes. Effect…
In this paper we extend the market-making models with inventory constraints of Avellaneda and Stoikov ("High-frequency trading in a limit-order book", Quantitative Finance Vol.8 No.3 2008) and Gueant, Lehalle and Fernandez-Tapia ("Dealing with inventory risk", Preprint 2011) to the case of a rather general class of mid…
A Deep Q-Learning framework tackles market-making by incorporating closing auctions.
problem Managing end-of-day risk in market-making models.
method Developed a Deep Q-Learning framework that anticipates closing auctions and continuously refines projected clearing prices.
result The Deep Q-Learning framework outperforms classical market-making models in simulations and real data.
Model shows phase transitions in asset pricing with market maker incentives.
problem Analyzing asset pricing with market maker profit incentives.
method Stochastic game theory, neural networks.
result Equilibrium experiences three phases: linear pricing, mid-price with spread, and metastable state.
New method calibrates crypto option prices more robustly.
problem Large bid-ask spreads and missing quotes in crypto markets.
method Designs a novel calibration procedure for crypto options.
result Calibration is more robust and accurate than standard methods.
We first investigate the evolution of opening and closing auctions volumes of US equities along the years. We then report dynamical properties of pre-auction periods: the indicative match price is strongly mean-reverting because the imbalance is; the final auction price reacts to a single auction order placement or can…
Learning the Spoofability of Limit Order Books With Interpretable Probabilistic Neural Networksq-fin.TR
Real-time detection of spoofing in cryptocurrency exchanges using neural networks.
problem Detecting and mitigating spoofing activity in limit order books.
method Novel order flow variables based on multi-scale Hawkes processes and a probabilistic market manipulation gain model.
result 31% of large orders could spoof the market, highlighting the importance of posting distance in price formation.
The electronic platform has been increasingly popular for executing large corporate bond orders by asset managers, who in turn have to assess the quality of their executions via Transaction Cost Analysis (TCA). One of the challenges in TCA is to build a realistic benchmark for the expected transaction cost and to chara…
Study reveals optimal price prediction through volume imbalance analysis.
problem Understanding the relationship between prices and volume imbalance in high-frequency trading.
method Developed a market-making model to analyze price-imbalance connection and solve optimization problems.
result Optimal quoting of predictive imbalance is confirmed, useful for financial regulation.
How and why stock prices move is a centuries-old question still not answered conclusively. More recently, attention shifted to higher frequencies, where trades are processed piecewise across different timescales. Here we reveal that price impact has a universal non-linear shape for trades aggregated on any intra-day sc…
Study optimal market making in Hawkes LOB market using impulse control and RL.
problem Optimal market making in Hawkes LOB market with queue dynamics and endogenous price impact.
method Impulse control framework, RL approximation, deep learning method.
result Strong empirical performance and solution to HJB-QVI with deep learning.
Estimation of slowly decreasing Hawkes kernels: Application to high frequency order book modellingq-fin.ST
We present a modified version of the non parametric Hawkes kernel estimation procedure studied in arXiv:1401.0903 that is adapted to slowly decreasing kernels. We show on numerical simulations involving a reasonable number of events that this method allows us to estimate faithfully a power-law decreasing kernel over at…
Study automates feature selection and clustering for HFT stock price forecasting.
problem Manual feature selection and clustering for high-frequency trading (HFT) stock price forecasting.
method Dual competitive feature importance mechanism and clustering via shallow neural network topology.
result Enhanced forecasting ability of the RBFNN regressor through automated feature selection and clustering.
A new method calculates implied volatilities without using option prices.
problem Calculating implied volatilities without option prices.
method Conic finance approach to uniquely strip volatilities from bid and ask quotes.
result Allows joint calculation of implied liquidity from bid and ask quotes.
Improved LSTM cell for high-frequency trading forecasts.
problem Precise stock price forecasting with minimal lags.
method Revised long short-term memory (LSTM) cell with optimal gate/state selection.
result Lower forecasting error compared to other recurrent neural networks.
We propose a limit order book (LOB) model with dynamics that account for both the impact of the most recent order and the shape of the LOB. We present an empirical analysis showing that the type of the last order significantly alters the submission rate of immediate future orders, even after accounting for the state of…
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and …
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features b…