arXiv research

The paper explores how market-based returns depend on past trade values.

The paper examines how market trade values and volumes affect price autocorrelation.

Market-based asset price probability depends on trade volumes and values, improving forecasts and reliability.

We select n stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fl…

New method unfolds distribution moments directly from data without binning.

The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.

Paper provides Edgeworth expansions for network moments, improving accuracy of sampling distributions.

Empower efficient representation of distributions through moment-preserving methods.

In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments. While Adam is an adaptive lower-order moment based (of the stochastic gradient) method, we propose an extension namely, HAdam, which uses higher order moments of the stochastic gradient. Our …

Maximum likelihood learning with exponential families leads to moment-matching of the sufficient statistics, a classic result. This can be generalized to conditional exponential families and/or when there are hidden data. This document gives a first-principles explanation of these generalized moment-matching conditions…

Optimizes mixture models without parametrizing distributions using tensor decomposition.

The paper explores how market trade values and volumes affect price and return statistics.

Method estimates posterior model for boundary value problems with uncertain constraints.

We study the price dynamics of stocks traded in a financial market by considering the statistical properties both of a single time series and of an ensemble of stocks traded simultaneously. We use the $n$ stocks traded in the New York Stock Exchange to form a statistical ensemble of daily stock returns. For each tradin…

Modern statistical inference tasks often require iterative optimization methods to compute the solution. Convergence analysis from an optimization viewpoint only informs us how well the solution is approximated numerically but overlooks the sampling nature of the data. In contrast, recognizing the randomness in the dat…

Huber regression assessed for robustness in statistical learning.

Betas are possibly the most frequently applied tool to analyze how securities relate to the market. While in very widespread use, betas only express dynamics derived from second moment statistics. Financial returns data often deviate from normal assumptions in the sense that they have significant third and fourth order…

New KCM tests improve specification testing via RKHS.

Enhances DP linear regression using public data moments.

Develops a robust GMM estimator for outlier-tolerant inference.

We discuss the probabilistic properties of the variation based third and fourth moments of financial returns as estimators of the actual moments of the return distributions. The moment variations are defined under non-parametric assumptions with quadratic variation method but for the computational tractability, we use …

Paper proposes a new method for density estimation using squared Hellinger distance.

DGMM improves Gaussian mixture modeling efficiency and stability.

Gradient descent with random weights in linear regression analyzed for various noise types.

A tractable pseudo-metric for non-parametric distributions via SPD geometry.

A new method of moments estimator goes beyond data reweighting.

Study on U-statistics with heavy-tailed samples, providing tail bounds and LDP.

We study the price dynamics of stocks traded in the NASDAQ market by considering the statistical properties of an ensemble of stocks traded simultaneously. For each trading day of our database, we study the ensemble return distribution by extracting its first two central moments. According to previous results obtained …

In this paper we introduce an efficient fat-tail measurement framework that is based on the conditional second moments. We construct a goodness-of-fit statistic that has a direct interpretation and can be used to assess the impact of fat-tails on central data conditional dispersion. Next, we show how to use this framew…

A new method for estimating causal parameters from observables reduces the need for finite moment conditions.

In this paper we will study the statistics of the unit geodesic flow normal to the boundary of a hyperbolic manifold with non-empty totally geodesic boundary. Viewing the time it takes this flow to hit the boundary as a random variable, we derive a formula for its moments in terms of the orthospectrum. The first moment…

New KSDs control moments in approximations, improving diagnostics and tests.

The paper explores tail diversification in financial markets using entropy and mutual information.

Learning rate needs to decrease with higher data moments for effective ICA in high dimensions.

Develops a new method for estimating models with conditional moment restrictions.

This work provides a computationally efficient and statistically consistent moment-based estimator for mixtures of spherical Gaussians. Under the condition that component means are in general position, a simple spectral decomposition technique yields consistent parameter estimates from low-order observable moments, wit…

Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learnin…

Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution ($Δ$) becomes small. For uncorrelated particle production within $Δ$, Gaussian statistics holds and factorial moments $F_q$ are equal to unity for all orders $q$. Correlations between par…

Spectral features of the empirical moment matrix constitute a resourceful tool for unveiling properties of a cloud of points, among which, density, support and latent structures. It is already well known that the empirical moment matrix encodes a great deal of subtle attributes of the underlying measure. Starting from …

This thesis studies domain adaptation under minimal distribution similarity assumptions using moments.

We describe a general framework -- compressive statistical learning -- for resource-efficient large-scale learning: the training collection is compressed in one pass into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task. A…

Method learns statistics of return distributions via neural networks and maximum mean discrepancy.

Unified framework for FDR control in knockoffs, validating Gaussian knockoffs.

A new model for generating point processes with complex geometries.

The paper provides bounds for high-dimensional U-statistics with novel order-explicit inequalities.

The article introduces inferential moments for analyzing uncertain multivariable systems.

This work develops efficient methods for computing moments of Gaussian mixtures.

The paper extends RDPG model to handle weighted graphs, enabling better analysis of network data.