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.
Uniqueness found for elliptic equations with drift on manifolds.
We find fundamental solutions to p-Laplace equations with drift terms in the Heisenberg group and Grushin-type planes. These solutions are natural generalizations to the fundamental solutions discovered by Beals, Gaveau, and Greiner for the Laplace equation with drift term. Our results are independent of the results of…
In this paper, we study term structure movements in the spirit of Heath, Jarrow, and Morton [Econometrica 60(1), 77-105] under volatility uncertainty. We model the instantaneous forward rate as a diffusion process driven by a G-Brownian motion. The G-Brownian motion represents the uncertainty about the volatility. With…
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…
Study shows physical drift affects put-call parity enforcement, not just option payoffs.
Unified geometric framework for Brownian motion on various manifolds.
A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.
We consider the heat equation associated with a class of second order hypoelliptic Hörmander operators with constant second order term and linear drift. We describe the possible small time heat kernel expansion on the diagonal giving a geometric characterization of the coefficients in terms of the divergence of the dri…
Paper introduces a method to explain concept drift using counterfactual explanations.
CURIE uses cellular automata to detect concept drift in data streams.
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…
Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent c…
New models capture dynamic derivatives pricing with efficient simulations.
CONDA-PM framework helps analyze concept drift in business processes.
Study improves survival analysis for credit risk by accounting for data drift.
We consider a Bayesian financial market with one bond and one stock where the aim is to maximize the expected power utility from terminal wealth. The solution of this problem is known, however there are some conjectures in the literature about the long-term behavior of the optimal strategy. In this paper we prove now t…
Study explains mortgage burnout using Cox hazard models.
The paper develops a neural network method for estimating drift functions of diffusion processes from discrete observations.
In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-perf…
The existence of the pricing kernel is shown to imply the existence of an ambient information process that generates market filtration. This information process consists of a signal component concerning the value of the random variable X that can be interpreted as the timing of future cash demand, and an independent no…
A fundamental issue for statistical classification models in a streaming environment is that the joint distribution between predictor and response variables changes over time (a phenomenon also known as concept drifts), such that their classification performance deteriorates dramatically. In this paper, we first presen…
This paper solves a Bayes sequential impulse control problem for a diffusion, whose drift has an unobservable parameter with a change point. The partially-observed problem is reformulated into one with full observations, via a change of probability measure which removes the drift. The optimal impulse controls can be ex…
We present a new analysis of the problem of learning with drifting distributions in the batch setting using the notion of discrepancy. We prove learning bounds based on the Rademacher complexity of the hypothesis set and the discrepancy of distributions both for a drifting PAC scenario and a tracking scenario. Our boun…
Lower bounds for eigenvalues on manifolds with boundary conditions.
The study improves Monte Carlo simulations for long-term investments using advanced financial models.
Learning from data streams is an increasingly important topic in data mining, machine learning, and artificial intelligence in general. A major focus in the data stream literature is on designing methods that can deal with concept drift, a challenge where the generating distribution changes over time. A general assumpt…
We use drifted Brownian motion in warped product model spaces as comparison constructions to show -hyperbolicity of a large class of submanifolds for . The condition for -hyperbolicity is expressed in terms of upper support functions for the radial sectional curvatures of the ambient space and for the rad…
The paper analyzes prediction error in nonstationary settings using weighted risk minimization.
Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models. This paper …
DRIFT uses neural flows to replace distributional regression models.
The behavior of stock market returns over a period of 1-60 days has been investigated for S&P 500 and Nasdaq within the framework of nonextensive Tsallis statistics. Even for such long terms, the distributions of the returns are non-Gaussian. They have fat tails indicating that the stock returns do not follow a random …
Machine learning monitors detect motor overheating, adapting to concept drift.
New budget quantifies drift in closed-loop learning, improving reproducibility.
Estimates time-series drifts from i.i.d. data using a direct Nadaraya-Watson plug-in method.
This paper refines bounds on random walk speed in Teichmüller space.
DPS uses PINNs to estimate drift in diffusion models for sampling.
Graphs approximate semigroups for diffusion on Riemannian manifolds.
We study the heat trace for both the drifting Laplacian as well as Schrödinger operators on compact Riemannian manifolds. In the case of a finite regularity potential or weight function, we prove the existence of a partial (six term) asymptotic expansion of the heat trace for small times as well as a suitable remainder…
Motivated by empirical data, we develop a statistical description of the queue dynamics for large tick assets based on a two-dimensional Fokker-Planck (diffusion) equation, that explicitly includes state dependence, i.e. the fact that the drift and diffusion depends on the volume present on both sides of the spread. "J…
Enhanced ICM ensemble detects concept drift better with novel betting functions.
Investment strategy in uncertain markets improved by learning and risk-ambiguity preferences.
Online distributional prediction with latent cluster geometry
Study online conformal prediction for non-stationary data with optimal training-conditional regret.
A new correction term improves sample efficiency in deep reinforcement learning.
New method calculates geometric Brownian motion with affine drift and its integral.
Paper uses sparse learning to estimate quasi-potential and drift components in stochastic systems.
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance s…
This paper presents a novel one-factor stochastic volatility model where the instantaneous volatility of the asset log-return is a diffusion with a quadratic drift and a linear dispersion function. The instantaneous volatility mean reverts around a constant level, with a speed of mean reversion that is affine in the in…