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Found 13 papers, 3 papers with code
A mathematical framework for modelling order book dynamics
We present a general framework for modelling the dynamics of limit order books, built on the combination of two modelling ingredients: the order flow, modelled as a general spatial point process, and market clearing, modelled via a deterministic mass transport operator acting on distributions of buy and sell orders.
Asymptotic Analysis of Deep Residual Networks
We investigate the asymptotic properties of deep Residual networks (ResNets) as the number of layers increases.
A model-free approach to continuous-time finance
We present a non-probabilistic, pathwise approach to continuous-time finance based on causal functional calculus.
Fast and Slow Optimal Trading with Exogenous Information
We first derive the optimal strategy of the high-frequency trader given any admissible strategy of the institutional investor.
Convergence and Implicit Regularization Properties of Gradient Descent for Deep Residual Networks
We prove linear convergence of gradient descent to a global optimum for the training of deep residual networks with constant layer width and smooth activation function.
Rough volatility: fact or artefact?
We investigate the finite sample performance of our estimator for measuring the roughness of sample paths of stochastic processes using detailed numerical experiments based on sample paths of fractional Brownian motion and other fractional processes.
Tail-GAN: Learning to Simulate Tail Risk Scenarios
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios.
Cross-Impact of Order Flow Imbalance in Equity Markets
We investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting.
Scaling Properties of Deep Residual Networks
Residual networks (ResNets) have displayed impressive results in pattern recognition and, recently, have garnered considerable theoretical interest due to a perceived link with neural ordinary differential equations (neural ODEs).
Model-free Analysis of Dynamic Trading Strategies
We introduce a model-free approach based on excursions of trading signals for analyzing the risk and return for a broad class of dynamic trading strategies, including pairs trading and other statistical arbitrage strategies.
A stochastic partial differential equation model for limit order book dynamics
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 flow dynamics.
Universal features of price formation in financial markets: perspectives from Deep Learning
The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors.
The Price Impact of Order Book Events
We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U. S. stocks.
