A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method for pricing options in stochastic volatility models.
Second-order estimator improves continuous-time policy evaluation.
We describe and extract time-ordered multibody interactions from complex systems.
Modeling financial markets with a novel order flow model.
The paper proposes a time-dependent Markov model for a limit order book.
Simulates financial market orders using anomalous diffusion models.
Efficient method classifies locally stationary time series based on second-order characteristics.
A novel transformer model improves classification of partially ordered sequences.
We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, by using a limit order or a market order. For a diffusive market, we characterize the optimal limit order placement policy and analyze its behavior under different market conditions. In part…
First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity. Second-order methods, while able to provide faster convergence, have been much less explored due to the high cost of computing the second-order information. In this paper we …
MarketGPT models financial time series with realistic order flow data.
The study examines order flow in financial markets using fractional Lévy stable motion.
Paper studies second order tail probabilities in risk models.
Study analyzes order transitions in high, medium, and low market cap stocks using Markov chains.
The aim of this paper is to geometrize time dependent Lagrangian mechanics in a way that the framework of second order tangent bundles plays an essential role. To this end, we first introduce the concepts of time dependent connections and time dependent semisprays on a manifold and their induced vector bundle struc…
Revisiting Trade-sign Long-memory and Square-root Law price impact
Enhances CEV model pricing with high-order scheme and adaptive time stepping.
SOR-Mamba improves Mamba for robust time series forecasting by minimizing channel order bias.
Model calculates optimal trading time for derivatives orders.
We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of L1-regularized regression for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. The main ap…
Accelerators with power-law memory are proposed in the framework of the discrete time approach. To describe discrete accelerators we use the capital stock adjustment principle, which has been suggested by Matthews.The suggested discrete accelerators with memory describe the economic processes with the power-law memory …
The aim of this paper is to provide a mathematical contribution on the semi-static hedge of timing risk associated to positions in American-style options under a multi-dimensional market model. Barrier options are considered in the paper and semi-static hedges are studied and discussed for a fairly large class of under…
Optimal stock trading strategy with market orders and limit orders in a risky market.
First-order method solves stochastic bilevel optimization with linear constraints.
We present an empirical study of the first passage time (FPT) of order book prices needed to observe a prescribed price change Delta, the time to fill (TTF) for executed limit orders and the time to cancel (TTC) for canceled ones in a double auction market. We find that the distribution of all three quantities decays a…
Enhances stock movement prediction using Higher Order Transformers for multimodal time-series data.
Novel hybrid modeling combines ML and physics for real-time diagnosis.
New framework detects directional influence in multivariate time series.
We show that multivariate Hawkes processes coupled with the nonparametric estimation procedure first proposed in Bacry and Muzy (2015) can be successfully used to study complex interactions between the time of arrival of orders and their size, observed in a limit order book market. We apply this methodology to high-fre…
We propose a new high-order alternating direction implicit (ADI) finite difference scheme for the solution of initial-boundary value problems of convection-diffusion type with mixed derivatives and non-constant coefficients, as they arise from stochastic volatility models in option pricing. Our approach combines differ…
We present high-order compact schemes for a linear second-order parabolic partial differential equation (PDE) with mixed second-order derivative terms in two spatial dimensions. The schemes are applied to option pricing PDE for a family of stochastic volatility models. We use a non-uniform grid with more grid-points ar…
We obtain universal inequalities for eigenvalues of the buckling problem of arbitrary order on bounded domains in .
Paper proposes BOCPD for real-time order flow and market impact prediction.
Let y''' = f(x, y, y', y'') be a 3rd order ODE. By Cartan equivalence method, we will study the local equivalence problem under the transformations group of time-fixed coordinates.
Continuous-time Kyle model shows privacy subsidy from noise-perturbed order flow.
LOBDIF predicts limit order book events using a diffusion model.
Two classes of methods have been proposed for escaping from saddle points with one using the second-order information carried by the Hessian and the other adding the noise into the first-order information. The existing analysis for algorithms using noise in the first-order information is quite involved and hides the es…
New conditions for circular orderability of direct products, linking to left-orderability of groups.
Unified analytic account of correlation emergence and Epps effect in coupled limit order books
We consider a simplified model of the continuous double auction where prices are integers varying from to with limit orders and market orders, but quantity per order limited to a single share. For this model, the order process is equivalent to two queues. We study the behaviour of the auction in the low…
Improved solver maintains positivity and accuracy across all time steps.
Optimal control models for limit order trading often assume that the underlying asset price is a Brownian motion since they deal with relatively short time scales. The resulting optimal bid and ask limit order prices tend to track the underlying price as one might expect. This is indeed the case with the model of Avell…
This paper is dedicated to the construction of high-order (in both space and time) finite-difference schemes for both forward and backward PDEs and PIDEs, such that option prices obtained by solving both the forward and backward equations are consistent. This approach is partly inspired by Andreasen & Huge, 2011 who re…
In a previous analysis the problem of "zero-inflated" time data (caused by high frequency trading in the electronic order book) was handled by left-truncating the inter-arrival times. We demonstrated, using rigorous statistical methods, that the Weibull distribution describes the corresponding stochastic dynamics for a…
Financial markets can be described on several time scales. We use data from the limit order book of the London Stock Exchange (LSE) to compare how the fluctuation dominated microstructure crosses over to a more systematic global behavior.
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in appli…
A new model predicts race places using changeover-times and log-normal distributions.
Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultra-high frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended …