Study compares exponential and power-law kernels in modeling high-frequency trading data.
problem Modeling high-frequency trading data with specific kernel types.
method Proposes and analyzes two bivariate Hawkes processes with exponential and power-law kernels.
result Identifies strengths and limitations of exponential and power-law kernels for high-frequency trading data.
Corrects gaps in a method for optimizing high-frequency trading strategies.
problem Optimizing bid and ask limit order strategies in high-frequency trading.
method Uses an approximation method based on Avellaneda and Stoikov's 2008 article, correcting gaps found in it.
result The main answer in Avellaneda and Stoikov's article remains unchanged despite corrections.
Model analyzes limit order book dynamics and trading strategies.
problem Understanding high-frequency trading on limit order books.
method Tractable Markov model using stochastic fluctuations and fluid limits.
result Existence of limiting distributions confined between thresholds.
Local convolutions bias neural networks towards high-frequency adversarial examples.
problem High-frequency adversarial examples in neural networks.
method Analysis of different linear and nonlinear architectures, focusing on the impact of local convolution operations.
result Local convolutions induce an implicit bias towards high frequency features, leading to high-frequency adversarial examples.
Study high-frequency trading game with price impact, finding unique equilibrium.
problem Optimal execution in a trading game with transient price impact.
method Analyzes high-frequency limit of an n-trader optimal execution game. result High-frequency limit converges to a continuous-time model with quadratic costs.
Study high-frequency Nash equilibria in market impact game with transient price impact.
problem Analyzing strategies and costs in a market impact game with exponentially decaying price impact.
method High-frequency limit analysis of Nash equilibria in a discrete-time model with quadratic transaction costs.
result Equilibrium strategies and costs converge to limits independent of θ for θ>0. Model for high-frequency trading with rough volatility.
problem High-frequency trading dynamics and rough volatility modeling.
method Stochastic partial differential equation (SPDE) with rough volatility driven by a Hawkes process.
result The volatility path of the SPDE is rougher than that driven by a standard Brownian motion.
In high-frequency financial data not only returns, but also waiting times between consecutive trades are random variables. Therefore, it is possible to apply continuous-time random walks (CTRWs) as phenomenological models of the high-frequency price dynamics. An empirical analysis performed on the 30 DJIA stocks shows …
Paper provides a benchmark dataset for mid-price forecasting in limit order book data.
problem Forecasting mid-price in high-frequency financial markets.
method Extracted and normalized time series data from NASDAQ Nordic stocks.
result Dataset of ~4,000,000 time series samples for 5 stocks.
A new high-frequency market making strategy using Deep Hawkes process.
problem Optimizing high-frequency trading in volatile markets.
method Developed a Deep Hawkes process to model order arrivals and their effects on the limit order book.
result The new strategy outperforms traditional methods in market making.
Develops robust estimators for high-frequency data with market microstructure noise.
problem Estimating prices in the presence of market microstructure noise.
method Plug-in versions of existing estimators, using raw price and limit order book data.
result Noise-robust estimators can be applied to various high-frequency data problems.
Optimizes high-frequency trading strategies in limit order books.
problem Impact of recent orders on future order submission rates.
method Discrete Markov chain model for LOB dynamics, Markov decision process for optimal order placement.
result Optimal policy using limit, cancellations, and market orders to maximize execution price.
Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most aggressive such practices, and arrive at figures that are surprisingly modest. By "aggressive" we mean any trading …
Novel approach to high frequency trading microstructure.
problem Understanding high frequency trading and its impact on market dynamics.
method Introducing a new concept of informed traders and rigorous limit order book analysis.
result Market frictions misrepresent wealth by 50% and alter trading strategies.
The paper develops a method to estimate time-varying parameters in high-frequency data.
problem Estimating time-varying parameters in high-frequency financial data.
method Local parametric estimation (LPE) for integrated parameter processes.
result Conditions for the central limit theorem to hold for LPE of integrated parameter processes.
We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronol…
High-frequency data cointegration framework developed with rigorous theory and tests.
problem Cointegration in high-frequency data with jumps and infinite activity.
method Regression-based estimation method and Dickey-Fuller type residual tests.
result Consistent and asymptotic limit theory for cointegration tests.
Study develops advanced models to forecast complex LOB data.
problem Forecasting high-frequency data in a limit order book (LOB).
method Advanced multidimensional sequence-to-sequence models with compound multivariate embedding.
result Method outperforms other multivariate forecasting methods, achieving lowest forecasting error.
Study improves Cox model for predicting stock trading signs using Japanese market data.
problem Improving Cox model for predicting stock trading signs using Japanese market data.
method Added new covariates and used high-frequency trading data for 222 Nikkei 225 stocks.
result Cox-type model performs well in Japanese market and identifies key factors for accurate estimation.
Machine learning predicts short-term price movements from LOB features.
problem Understanding and predicting short-term price movements from LOB dynamics.
method Machine learning approach to analyze LOB features.
result Significantly superior prediction results compared to baseline.
Deep learning model improves financial return forecasting using LOBs.
problem Forecasting financial returns using Limit Order Books.
method Developed a deep learning architecture for simultaneous quantile regression of buy and sell positions.
result The model provides improved robustness and excellent performance in predicting financial returns.
Tensor-based methods improve mid-price prediction in high-frequency financial data.
problem Predicting price changes in high-frequency financial data.
method Multilinear tensor-based learning algorithms for mid-price prediction.
result Tensor-based models outperform vector-based approaches in mid-price prediction.
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…
High-frequency trading strategy boosts battery storage profits.
problem Maximizing revenue for battery energy storage systems in intraday markets.
method Adapted dynamic programming for continuous intraday markets, considering limit order book dynamics.
result Dynamic programming strategy outperforms standard re-optimization methods, increasing profits by 58% and 14% respectively.
Two-layer networks struggle with high frequencies due to numerical and computational limitations.
problem High frequency approximation and learning in shallow networks.
method Mathematical and computational analysis focusing on numerical error, computational cost, and stability.
result Explicit answers to fundamental computational issues in shallow networks' high frequency handling.
DeepVol uses high-frequency data to forecast volatility, outperforming traditional methods.
problem Improving volatility forecasting using high-frequency data.
method Dilated Causal Convolutions applied to high-frequency financial time-series.
result DeepVol outperforms traditional methods in forecasting day-ahead volatility.
High-frequency traders manage inventories to exploit price information, leading to mean-reverting inventories and excess trading.
problem Managing inventories for high-frequency traders in imperfect competition.
method Analyzes Nash equilibria for inventory-averse HFTs using nonlinear equations and asymptotic analysis.
result Optimal inventories become mean-reverting and vanish in the continuous-time limit, while HFTs' profits converge to risk-neutral counterparts.
Paper forecasts financial trading durations using a new point process model.
problem Forecasting limit order book durations in high-frequency financial data.
method Self-exciting flexible residual point process incorporating empirical distributional features.
result The model achieves strong predictive performance compared to alternative approaches.
Paper proposes ExsdHawkes to model LOBs, capturing volatility dynamics.
problem Modeling volatility signature plots in LOBs with high-frequency trading dynamics.
method Extended State-Dependent Hawkes Process (ExsdHawkes) with relaxed constraints.
result ExsdHawkes uniquely reproduces volatility signature plots, identifying MLOs as catalysts.
Extends Kelly Criterion to more complex betting scenarios.
problem Maximizing long-term growth in complex betting models.
method Generalizes Kelly Criterion to Lévy processes and high-frequency limits.
result Improved strategies for high-frequency betting.
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.
DRL agents learn to trade Intel stock with stable positive returns.
problem Active high frequency trading in the stock market.
method End-to-end DRL framework using Proximal Policy Optimization, Sequential Model Based Optimization, and LOB-based meta-features.
result DRL agents create dynamic trading strategies with stable positive returns.
Deep learning models compare performance on Limit Order Book tasks.
problem Comparing Deep Learning models for High Frequency Trading.
method Reviewed and compared state-of-the-art models on the same dataset.
result Multilayer Perceptrons perform comparably to CNN-LSTM architectures.
In the present work we demonstrate the application of different physical methods to high-frequency or tick-by-tick financial time series data. In particular, we calculate the Hurst exponent and inverse statistics for the price time series taken from a range of futures indices. Additionally, we show that in a limit orde…
A flexible nonparametric online changepoint detection algorithm for high-frequency data.
problem Detecting changes in real-time in high-frequency data streams with limited computational resources.
method NP-FOCuS, a sequential likelihood ratio test for a change in the empirical cumulative density function, using functional pruning.
result NP-FOCuS outperforms current nonparametric online changepoint techniques in various settings.
The model analyzes order flows in financial markets using Cox-type intensities.
problem Analyzing order dynamics in limit order books for market insights.
method Cox-type model for relative intensities, parameter estimation by quasi likelihood maximization, model selection with information criteria.
result The model provides excellent agreement with empirical data and identifies important factors in order book dynamics.
High Frequency Trading (HFT) represents an ever growing proportion of all financial transactions as most markets have now switched to electronic order book systems. The main goal of the paper is to propose continuous time equations which generalize the self-financing relationships of frictionless markets to electronic …
Estimates high-frequency Hawkes process parameters with bias correction.
problem Estimating time-varying parameters in a self-exciting process.
method Chop data into blocks, compute local MLE, apply bias reduction, and use central limit theorem.
result Non-naïve estimator reduces asymptotic bias and performs well in finite samples.
Study uses reinforcement learning to optimize trading strategies.
problem Developing an optimal execution strategy for traders.
method Reinforcement learning model using ABIDES simulator.
result Reinforcement learning model outperforms standard strategies.
High-frequency trading models fail due to overfitting and survivor bias.
problem Failure of hybrid DRL-EC trading systems in high-frequency environments.
method Deployed a population of 500 agents in a high-frequency cryptocurrency environment, analyzing failure modes through multi-disciplinary lens.
result Increasing model complexity without information asymmetry exacerbates systemic fragility.
Trains a neural network to predict high-frequency trading outcomes.
problem Predicting the fill probability function for high-frequency trading.
method High-quality high-frequency data and neural network training with a weighted loss function.
result Strong state dependence properties of the fill probability function.
Modeling high-frequency speculative markets as auction search processes.
problem Understanding trading dynamics in high-frequency order-driven markets.
method Total order book model with diffusion-drift-reaction model, inspired by foraging and chemotaxis.
result Analytic and numerical analysis of trading performance in various search mechanisms.
Study optimal liquidation strategies under partial information in high-frequency trading.
problem Optimal liquidation strategies in high-frequency trading with incomplete information.
method Modeling price formation through Hawkes processes, incorporating liquidity as a hidden Markov process, and formulating as an impulse control problem.
result Development of an algorithm to approximate optimal liquidation strategies.
T-KAN improves HFT LOB forecasting with learnable splines.
problem Alpha decay in HFT LOB forecasting models.
method T-KAN uses learnable B-spline activation functions to model market signals.
result 19.1% relative improvement in F1-score at k = 100 horizon.
Model for cross-border markets with limited transmission capacities.
problem Limited transmission capacities between two countries' markets.
method Developed a regime-switching process model with high-frequency approximation.
result Analytic tractability allows computation of key market quantities.
A new bootstrap method improves hypothesis testing for roughness of time series data.
problem Improving hypothesis testing for roughness of time series data.
method Local fractional bootstrap method for high-frequency statistics of Brownian semistationary processes.
result The bootstrap method provides considerable finite-sample improvements over existing methods.
Game-theoretic models predict asset prices in financial markets.
problem Understanding price formation in financial markets with limited liquidity.
method Developed game-theoretic models for many-person and mean-field games, derived analytical formulas, and numerically assessed results.
result The derived price converges to the mean-field counterpart under specific conditions.
New method improves Gaussian kernel approximations for high-frequency data.
problem Limited scalability of kernel-based models to large data sets.
method Local random feature approximations using Maclaurin expansions and polynomial sketches.
result Significant improvement in kernel approximations and downstream performance for high-frequency data.