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.
Investigates gradient descent dynamics and introduces new regularisation methods.
Finite difference approximations to multi-asset American put option price are considered. The assets are modelled as a multi-dimensional diffusion process with variable drift and volatility. Approximation error of order one quarter with respect to the time discretisation parameter and one half with respect to the space…
Discrete time analogues of ergodic stochastic differential equations (SDEs) are one of the most popular and flexible tools for sampling high-dimensional probability measures. Non-asymptotic analysis in the Wasserstein distance of sampling algorithms based on Euler discretisations of SDEs has been recently develop…
We consider the approximation of stochastic differential equations (SDEs) with non-Lipschitz drift or diffusion coefficients. We present a modified explicit Euler-Maruyama discretisation scheme that allows us to prove strong convergence, with a rate. Under some regularity and integrability conditions, we obtain the opt…
Study uses BNs to predict cryptocurrency prices, improving accuracy with discretisation.
We derive a general multivariate theory for realised characteristics of `model-free discretisation-invariant swaps', so-called because the standard no-arbitrage assumption of martingale forward prices is sufficient to derive fair-value swap rates for such characteristics which have no jump or discretisation errors. Thi…
A new discretisation of a doubled, i.e. BF, version of the pure abelian Chern-Simons theory is presented. It reproduces the continuum expressions for the topological quantities of interest in the theory, namely the partition function and correlation function of Wilson loops. Similarities with free spinor field theory a…
In this article, we propose a Milstein finite difference scheme for a stochastic partial differential equation (SPDE) describing a large particle system. We show, by means of Fourier analysis, that the discretisation on an unbounded domain is convergent of first order in the timestep and second order in the spatial gri…
GRAND treats GNNs as PDE discretizations, addressing graph learning issues.
New RL method learns from state transitions without actions.
New method for pricing options in stochastic volatility models.
This paper conditions non-linear infinite-dimensional diffusion processes.
The multilevel Monte Carlo path simulation method introduced by Giles ({\it Operations Research}, 56(3):607-617, 2008) exploits strong convergence properties to improve the computational complexity by combining simulations with different levels of resolution. In this paper we analyse its efficiency when using the Milst…
A new neural approach for generating origin-destination matrices in ABMs.
New method accelerates Bayesian imaging using Langevin sampling.
Quantum algorithm reduces CVA risk-neutral expectation estimation costs.
This paper optimizes diffusion schedules for better sampling from data distributions.
Study simulates Heston-type local stochastic volatility model using particle method.
This review covers learning under concept drift, including detection, understanding, and adaptation.
Discretizes special surfaces using Koenigs nets.
Identifies features most relevant to concept drift in data.
We establish numerical methods for solving the martingale optimal transport problem (MOT) - a version of the classical optimal transport with an additional martingale constraint on transport's dynamics. We prove that the MOT value can be approximated using linear programming (LP) problems which result from a discretisa…
New method detects when models influence their own drift in real-time data streams.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. Albeit many attempts were made to deal with drift, formal notions of drift are application-dependent and formulated in various degrees of abstraction and mathematical coherence. In this contribu…
Realised pay-offs for discretisation-invariant swaps are those which satisfy a restricted `aggregation property' of Neuberger [2012] for twice continuously differentiable deterministic functions of a multivariate martingale. They are initially characterised as solutions to a second-order system of PDEs, then those pay-…
A new drift detection method based on autoregressive models.
Adaptive sampling detects local concept drift with limited labels.
Algorithm detects concept drift and adapts models in streaming data.
Classifiers operating in a dynamic, real world environment, are vulnerable to adversarial activity, which causes the data distribution to change over time. These changes are traditionally referred to as concept drift, and several approaches have been developed in literature to deal with the problem of drift handling an…
Systems of partial differential equations lie at the heart of physics. Despite this, the general theory of these systems has remained rather obscure in comparison to numerical approaches such as finite element models and various other discretisation schemes. There are, however, several theoretical approaches to systems…
Classifies polynomial growth solutions to drift-harmonic equations on asymptotically paraboloidal manifolds.
Neural networks learn discrete tasks on continuous data via emergent geometry.
This paper investigates a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at the jump times of a homogeneous Poisson process. Dr…
New method detects drift in high-dimensional data.
Detects drifts in data for classification tasks using constrained embeddings.
Concept drift is formally defined as the change in joint distribution of a set of input variables X and a target variable y. The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distributio…
PDD detects concept drift using explainable AI, improving model performance in dynamic environments.
This paper studies concept drift detectors for financial time series.
Paper proposes a framework to detect adversarial concept drifts under poisoning attacks.
Kernel-Gradient Drifting improves generative modeling for non-Euclidean data.
A framework for evaluating and benchmarking concept drift detection methods
A new method detects concept drift in streaming data using k-means space partitioning.
Visual analytics tool detects and corrects concept drift in data streams.
In this paper we outline methodology to efficiently simulate (jump) diffusion bridge sample paths without discretisation error. We achieve this by considering the simulation of conditioned (jump) diffusion bridge sample paths in light of recent work developing a mathematical framework for simulating finite dimensional …
CURIE uses cellular automata to detect concept drift in data streams.
Circular nets with spherical parameter lines have geometric properties related to Darboux cyclides and terminating Laplace sequences.
We present the first steps of a procedure which discretises surface theory in classical projective differential geometry in such a manner that underlying integrable structure is preserved. We propose a canonical frame in terms of which the associated projective Gauss-Weingarten and Gauss-Mainardi-Codazzi equations adop…