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arXiv research

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.

169,051 papers · 148 categories

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163325488650 · Jun 202019922001200920182026
48 results for dispersed functions

The study examines Hawkes processes and their long-term behavior.

problem Understanding the long-term behavior of Hawkes processes.
method Proving functional limit theorems under various conditions on the dispersion of child events.
result Functional limit theorems hold for Hawkes processes with different levels of child event dispersion.

A new model relaxes constraints on exponential dispersion models.

problem Tight conditions on cumulant function limit the class of exponential dispersion models.
method Introduces K-LED model with Legendre cumulant function and Bregman divergence guidance.
result The model allows for easier computation of mean parameter and includes various distributions.

New framework controls statistical dispersion for high-stakes applications.

problem Understanding and controlling the dispersion of loss distributions in high-stakes applications.
method Simple yet flexible framework for distribution-free control of statistical dispersion measures.
result Proposed methods control statistical dispersion measures with societal implications.

Paper transforms a complex equation into simpler forms for analysis.

problem Analyzing a fourth-order dispersive flow equation on Kähler manifolds.
method Developed the generalized Hasimoto transformation to simplify the equation.
result Explicit expressions derived for three examples of compact Kähler manifolds.

Bayesian model tackles spatial count data issues with flexible non-parametric techniques.

problem Challenges in traditional parametric models for spatial count data with unbalanced distributions and complex dependencies.
method Bayesian semi-parametric spatial dispersed count model combining non-parametric techniques and adapted count models.
result Demonstrates superior performance in managing dispersion and capturing intricate spatial patterns.

Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.

problem Predicting future stock market returns using skewness dispersion.
method Cross-sectional analysis of firm-level realized skewness and stock market returns.
result Skewness dispersion is a significant predictor of future stock market returns, robust to various estimation methods.

New dispersion indices based on inaccuracy and divergence introduced for information measures.

problem Measuring variability in uncertainty measures.
method Introducing new dispersion indices based on Kerridge inaccuracy and Kullback-Leibler divergence.
result Properties, bounds, and examples of new dispersion indices presented.

A framework connects VAEs to GLMs for better model initialization and performance.

problem Understanding and optimizing loss function critical points in VAEs.
method Introducing a theoretical framework based on GLM and EDFs.
result Maximum likelihood initialization improves VAE performance.

Dropout improves regularization in flexible models for rare features.

problem Understanding theoretical properties of dropout in generalized linear models.
method Theoretical analysis and application to adaptive smoothing with B-splines.
result Dropout prefers rare features in mean and dispersion parameters.

The standard deviation and Gini mean difference order based on tail behavior.

problem Ordering between standard deviation and Gini mean difference for real-valued risks.
method Analysis of the mean excess function of the pairwise difference XX|X - X'|.
result Dominance regimes of SD and GMD are determined by tail behavior of the distribution.

Paper predicts urban dispersal events using deep survival analysis on mobility data.

problem Predicting abnormal dispersal events in urban areas to mitigate congestion and safety risks.
method Formulated as a survival analysis problem, developed a two-stage deep learning framework (DILSA).
result DILSA predicts dispersal events with F1-score of 0.7 and average time error of 18 minutes.

Paper proves vanishing terms in a second Poisson bracket for a specific system.

problem Proving polynomiality of coefficients in the dispersion parameter expansion of the second Poisson bracket.
method Bi-Hamiltonian recursion and Liu-Pandharipande relations.
result Proves vanishing terms in the second Poisson bracket expansion.

Geometric focusing affects dispersive estimates for Schrödinger and wave equations.

problem Long-time decay rate in dispersive estimates for Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones.
method Classifying the long-time decay rate in dispersive estimates for the Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones in terms of the intensity of geometric focusing.
result Each multiplicity of conjugate points within distance π on Y = ∂X0 leads to a |t|1/2-loss in the long-time decay order and a half-order shift in the regularity index in the dispersive estimate for the Schrödinger equation.

Study on stock market volatility and return dispersion during COVID-19.

problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.

In the recent years, banks have sold structured products such as worst-of options, Everest and Himalayas, resulting in a short correlation exposure. They have hence become interested in offsetting part of this exposure, namely buying back correlation. Two ways have been proposed for such a strategy : either pure correl…

2010-04-01abs ↗pdf ↗

Decomposes portfolio returns into drift and asset price distribution changes.

problem Understanding efficient markets through portfolio returns and asset price distributions.
method Continuous semimartingale price representations and accounting identity.
result Existence of an asset pricing factor emerges from an accounting identity across various economic and financial environments.

Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.

problem Ensembling neural networks struggles with dispersed, narrow peaks in likelihood surfaces.
method Uses Bayesian Quadrature to construct weighted ensembles of architectures.
result Empirically outperforms state-of-the-art baselines in test likelihood, accuracy, and expected calibration error.

Probabilistic modeling is cyclical: we specify a model, infer its posterior, and evaluate its performance. Evaluation drives the cycle, as we revise our model based on how it performs. This requires a metric. Traditionally, predictive accuracy prevails. Yet, predictive accuracy does not tell the whole story. We propose…

2016-05-24abs ↗pdf ↗

This paper generalizes beta divergence beyond its classical form associated with power variance functions of Tweedie models. Generalized form is represented by a compact definite integral as a function of variance function of the exponential dispersion model. This compact integral form simplifies derivations of many pr…

2013-06-14abs ↗pdf ↗

We establish several new stylised facts concerning the intra-day seasonalities of stock dynamics. Beyond the well known U-shaped pattern of the volatility, we find that the average correlation between stocks increases throughout the day, leading to a smaller relative dispersion between stocks. Somewhat paradoxically, t…

2010-09-24abs ↗pdf ↗

Study dispersive estimates for Schrödinger and wave equations on a cone with specific metric.

problem Pointwise decay estimates for Schrödinger and wave equations on a product cone.
method Modified Hadamard parametrix on YY with ε>πε > π to prove dispersive estimates.
result Threshold of conjugate radius ε>πε > π for pointwise dispersive estimates.

Proposes a new portfolio optimization method considering reward, dispersion, and asymmetry.

problem Capturing fat-tails and asymmetry in asset return distributions.
method Market model with tempered stable distribution; extended mean-variance optimization.
result Closed-form solutions for VaR and CVaR; efficient frontier extended to three dimensions.

New algorithms minimize regret in changing environments for piecewise Lipschitz functions.

problem Optimizing in unpredictable, time-varying conditions for piecewise Lipschitz functions.
method Introduced shifting regret as a metric, and provided bounds for O(sdTlogT+sT1β)O(\sqrt{sdT\log T}+sT^{1-β}).
result Improved bounds for ββ-dispersed functions, with empirical validation in online clustering.

In this paper we propose a novel index to quantify and measure the flow of information on macro and micro scales. We discuss the implications of this index for knowledge management fields and also as intellectual capital that can thus be utilized by entrepreneurs. We explore different function and human oriented metric…

2011-06-14abs ↗pdf ↗

Neural networks improve wave-equation simulation accuracy.

problem Inaccurate discretization of Laplacian in wave-equation simulation leads to numerical dispersion.
method Intersperse CNNs between low-fidelity timesteps to correct wavefield and limit numerical dispersion.
result Neural network augmentation reduces numerical dispersion artifacts in wave-equation simulation.

Method meta-classifies semantic segmentation predictions using dispersion measures.

problem Assessing the quality of semantic segmentation predictions.
method Aggregates dispersion measures (entropy) of predicted probabilities to derive metrics correlated with IoU.
result Metrics correlate well with IoU, providing reliable prediction quality ratings.

The paper examines stochastic ordering of Gini indexes for multivariate elliptical risks.

problem Stochastic ordering of Gini indexes for multivariate elliptical risks.
method Established conditions for monotonicity of Gini index in usual stochastic order.
result Suitable conditions for multivariate elliptical risks generalize those for multivariate normal risks.

We study productivity dispersions across workers, firms and industrial sectors. Empirical study of the Japanese data shows that they all obey the Pareto law, and also that the Pareto index decreases with the level of aggregation. In order to explain these two stylized facts, we propose a theoretical framework built upo…

2008-05-19abs ↗pdf ↗

End-to-end deep learning boosts IM/DD fiber communication over dispersive channels.

problem Improving data transmission over dispersive IM/DD channels with memory.
method Bidirectional recurrent neural network (BRNN) for end-to-end deep learning of the communication system.
result End-to-end SBRNN achieves significant bit-error-rate reduction compared to FFNNs.

Study shows how to reduce variational inference bias by concentrating likelihood ratio distribution.

problem Bias and variance issues in variational inference.
method Upper bound variational gap using dispersion measure of likelihood ratio, suggesting methods to reduce bias.
result Reducing bias in variational inference can be achieved by making likelihood ratio distribution more concentrated.

Study on cryptocurrency market dynamics and correlations over time.

problem Understanding the dynamics and correlations of cryptocurrency market over time.
method Evolutionary correlation analysis, turning point algorithm, inverse relationship between market size and collective dynamics, time-varying consistency of relationships, examination of volatility structure.
result Increased uniformity in volatility during market crashes, termed 'volatility dispersion'.

Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace. Following the blueprint of classical Linear Discriminant Analysis (LDA), WDA selects the projection matrix that maximizes the …

2016-08-29abs ↗pdf ↗

A new filter design improves system identification accuracy.

problem Improving system identification accuracy for various system types.
method Generalized proportionate-type normalized subband adaptive filter (GPtNSAF) using least squares on subband errors with a sparsity penalty.
result GPtNSAF benefits from increasing subbands more than sparsity for quasi-sparse or dispersive systems, and both aspects are complementary for sparse systems.