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.
Paper models non-linear dynamics from time series data.
SSL framework identifies non-linear systems without labeled data.
Study on dynamics of non-linear autoencoders learning principal components.
New model explains market dynamics with phase transitions and non-linear interactions.
Study confirms complex crypto market dynamics via non-linear potentials.
This work optimizes reservoir computing models by linking recurrence and non-linear dynamics.
This paper studies the dynamic generator model for spatial-temporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the video sequence is generated by a generator model, which is a non-linear transformation of a latent state vector, where the non-linear transform…
New algorithm improves dynamic mode decomposition for high-dimensional data.
DREAM model improves computational efficiency for non-linear effects in relational event models.
Wavelet analysis reveals non-linear dynamics in cryptocurrency prices.
Endogenous business cycles explain higher comovement across countries.
New method optimizes non-linear functionals over probability measures.
Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process. If such dynamics are non-linear, it can be difficult to estimate the posterior state and forcing term jointly, particularly when …
We develop algorithms to learn non-linear dynamical systems without mixing assumptions.
KalmanNet uses neural networks to improve state estimation in systems with unknown dynamics.
We introduce a prototype model in an attempt to capture some aspects of market dynamics simulating a trading mechanism. The model description starts with a discrete-space, continuous-time Markov process describing arrival and movement of orders with different prices. We then perform a re-scaling procedure leading to a …
This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.
Unified theory of -expectations derived from chaotic dynamics.
Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…
RNN-HAR model improves VaR forecasting with long-memory and non-linear dynamics.
Develops inverse EKF for non-linear systems with stability guarantees and learning unknown dynamics.
Tractable model explains market dynamics using Langevin and SUSY QM.
New numerical method for non-linear asset price model with CEV volatility.
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction func…
We investigate the joint dynamics of spot and implied volatility from an empirical perspective. We focus on the equity market with the SPX Index our underlying of choice. Using only observable quantities, we extract the instantaneous variance curves implied by the market and study their daily variations jointly with sp…
We propose a simple non-equilibrium model of a financial market as an open system with a possible exchange of money with an outside world and market frictions (trade impacts) incorporated into asset price dynamics via a feedback mechanism. Using a linear market impact model, this produces a non-linear two-parametric ex…
In this contribution we review results on the kinematics of a quantum system localized on a connected configuration manifold and compatible dynamics for the quantum system including external fields and leading to non-linear Schrödinger equations for pure states.
The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution of some real-world phenomenon over time, for example models of the climate or the functioning of the human brain. The models we are interest…
The paper analyzes how market prices respond to information processing and non-linear dynamics.
In that paper, we provide a new characterization of the solutions of specific reflected backward stochastic differential equations (or RBSDEs) whose driver is convex and has quadratic growth in its second variable: this is done by introducing the extended notion of -Snell enveloppe. Then, in a second step, we re…
Active learning selects inputs for GPSSM to learn latent states.
Neural ODEs control graph dynamics with low energy feedback.
This paper considers method of creation of an advisor and indicator based on the spectral stochastic analysis model, both with linear and non-linear approximation. The problem of entrance to one or another trade position is solved on the basis of combined analysis of dynamics of quotations of all currency pairs, what a…
Develops polynomial diffusion models for multi-factor commodity futures dynamics.
DISTANA predicts and denoises spatial wave dynamics.
This research evaluates learning models for bionic robots, focusing on transfer function identification.
This paper studies a limit order book (LOB) model, in which the order dynamics depend on both, the current best available prices and the current volume density functions. For the joint dynamics of the best bid price, the best ask price, and the standing volume densities on both sides of the LOB we derive a weak law of …
We develop a one-dimensional notion of affine processes under parameter uncertainty, which we call non-linear affine processes. This is done as follows: given a set of parameters for the process, we construct a corresponding non-linear expectation on the path space of continuous processes. By a general dynamic programm…
Study finds sales forecasters overreact to extreme news.
This study develops a multi-factor framework where not only market risk is considered but also potential changes in the investment opportunity set. Although previous studies find no clear evidence about a positive and significant relation between return and risk, favourable evidence can be obtained if a non-linear rela…
Reduced modeling of a computationally demanding dynamical system aims at approximating its trajectories, while optimizing the trade-off between accuracy and computational complexity. In this work, we propose to achieve such an approximation by first embedding the trajectories in a reproducing kernel Hilbert space (RKHS…
We propose a new least-squares Monte Carlo algorithm for the approximation of conditional expectations in the presence of stochastic derivative weights. The algorithm can serve as a building block for solving dynamic programming equations, which arise, e.g., in non-linear option pricing problems or in probabilistic dis…
Method estimates dynamic treatment effects using machine learning and g-estimation.
Learning weights in a spiking neural network with hidden neurons, using local, stable and online rules, to control non-linear body dynamics is an open problem. Here, we employ a supervised scheme, Feedback-based Online Local Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking neurons with hidden l…
In the last years efforts in econophysics have been shifted to study how network theory can facilitate understanding of complex financial markets. Main part of these efforts is the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financia…
A new method learns state and proposal dynamics in state-space models using neural networks.
We derive a system of equations governing the coupled gravitational and electromagnetic perturbations of Reissner-Nordström spacetime. The equations are derived in the context of global non-linear stability of Reissner-Nordström under axially symmetric polarized perturbations, as a generalization of the recent work on …
Deep networks prioritize easier examples over harder ones, leading to faster training.