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.
Study homogenizes equations on parallelizable manifolds using tensor localization and periodicity.
The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation. The bagging technique is an elegant way to form WNN estimators with weights automatically generated to the nearest neighbors; we name the resulting estimator as t…
Paper studies convergence of Mean-Field GDA dynamics for MNE of continuous games.
High frequency based estimation methods for a semiparametric pure-jump subordinated Brownian motion exposed to a small additive microstructure noise are developed building on the two-scales realized variations approach originally developed by Zhang et. al. (2005) for the estimation of the integrated variance of a conti…
Formula derived for a magnetic line invariant.
A new model for defective media using two scales.
Paper proposes a mean-field gradient descent for zero-sum games, proving convergence to Nash equilibrium.
We give a natural way to identify between two scales, potentially arbitrarily far apart, in a non-compact Ricci-flat manifold with Euclidean volume growth when a tangent cone at infinity has smooth cross section. The identification map is given as the gradient flow of a solution to an elliptic equation.
Stochastic variance-reduced gradient (SVRG) is an optimization method originally designed for tackling machine learning problems with a finite sum structure. SVRG was later shown to work for policy evaluation, a problem in reinforcement learning in which one aims to estimate the value function of a given policy. SVRG m…
We develop and analyze a procedure for gradient-based optimization that we refer to as stochastically controlled stochastic gradient (SCSG). As a member of the SVRG family of algorithms, SCSG makes use of gradient estimates at two scales, with the number of updates at the faster scale being governed by a geometric rand…
New method combines machine learning with data assimilation for model error correction.
A new measure predicts deep learning model performance.
Agents' heterogeneity is recognized as a driver mechanism for the persistence of financial volatility. We focus on the multiplicity of investment strategies' horizons, we embed this concept in a continuous time stochastic volatility framework and prove that a parsimonious, two-scale version effectively captures the lon…
The financial market is nonpredictable, as according to the Bachelier, the mathematical expectation of the speculator is zero. Nevertheless, we observe in the price fluctuations the two distinct scales, short and long time. Behaviour of a market in long terms, such as year intervals, is different from that in short ter…
We investigate the waiting-time distribution of the absolute return in the Korean stock-market index KOSPI. We define the waiting time as a time interval during which the normalized absolute return remains continuously below a threshold . Through an exponential bin plot, we observe that the waiting-time distributi…
In an earlier work we identified the types and numbers of static equilibrium points of solids arising from fine, equidistant -discretrizations of smooth, convex surfaces. We showed that such discretizations carry equilibrium points on two scales: the local scale corresponds to the discretization, the global scale to…
We consider microstructure as an arbitrary contamination of the underlying latent securities price, through a Markov kernel . Special cases include additive error, rounding and combinations thereof. Our main result is that, subject to smoothness conditions, the two scales realized volatility is robust to the form of…
Jackknife variance estimation validated for generalized U-statistics.
Bitcoin returns exhibit a distinct inverse cubic law scaling behavior.
We investigate multifractality in the Korean stock-market index KOSPI. The generalized th order height-height correlation function shows multiscaling properties. There are two scaling regimes with a crossover time around min. We consider the original data sets and the modified data sets obtained by removin…
A new method decouples set representation learning from posterior modeling for efficient amortized inference.
We introduce wavelet-based methodology for estimation of realized variance allowing its measurement in the time-frequency domain. Using smooth wavelets and Maximum Overlap Discrete Wavelet Transform, we allow for the decomposition of the realized variance into several investment horizons and jumps. Basing our estimator…
We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are it…
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of …
Graph cross network improves graph classification accuracy.
This paper explores online learning of dynamics and state using ensemble Kalman filters.
Empirical studies indicate the presence of multi-scales in the volatility of underlying assets: a fast-scale on the order of days and a slow-scale on the order of months. In our previous works, we have studied the portfolio optimization problem in a Markovian setting under each single scale, the slow one in [Fouque and…
A new method predicts non-Markovian closure terms for complex systems.
New principles needed for scaling large language models, challenging traditional regularization methods.
Microscopic (pore-scale) properties of porous media affect and often determine their macroscopic (continuum- or Darcy-scale) counterparts. Understanding the relationship between processes on these two scales is essential to both the derivation of macroscopic models of, e.g., transport phenomena in natural porous media,…
The conditional-mean barrier helps diagnose deterministic surrogates missing uncertainty.
Combines ML and DA to infer unresolved scale parametrisation from noisy data.
One gradient step improves neural network feature learning by aligning weights with the teacher model.
Proves weak convergence equals mean convergence in GGC.
This is an intuitive survey of extrinsic and intrinsic notions of convergence of manifolds complete with pictures of key examples and a discussion of the properties associated with each notion. We begin with a description of three extrinsic notions which have been applied to study sequences of submanifolds in Euclidean…
The abstract discusses convergence properties of Lipschitz functions and sets defined by equations.
Study shows intrinsic timed Hausdorff convergence leads to Gromov-Hausdorff and big bang convergence.
Studied SGD convergence under weak conditions.
Study on convergence rate of -curvature flow in 6 dimensions.
The objective of this paper is to introduce the notion of generalized almost statistical (briefly, GAS) convergence of bounded real sequences, which generalizes the notion of almost convergence as well as statistical convergence of bounded real sequences. As a special kind of Banach limit functional, we also introduce …
Establishes geometric convergence of iterative optimization algorithms.
Uniform counting formulas for orthogeodesics in Kleinian groups converge.
The paper explores null distance convergence for warped product spacetimes.
New quasi-Newton method guarantees global superlinear convergence.
We investigate finite-time decoupled convergence in nonlinear two-time-scale stochastic approximation.
The article introduces a new convergence concept for Lorentzian spaces and applies it to generalized cones.
AdaBoost's classifier and margins converge to a known value.
Study shows gap between uniform convergence and test error in random feature models.