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.
Develops correlation number for specific potentials and Hitchin representations.
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single trea…
Improved portfolio optimization using Kendall-like correlation coefficients.
The paper studies the correlation of Hilbert lengths for convex projective surfaces.
Bootstrapping regularizes singular correlation matrices, reducing the need for complex regularization.
We propose a correlated stochastic process of which the novel non-Gaussian probability mass function is constructed by exactly solving moment generating function. The calculation of cumulants and auto-correlation shows that the process is convergent and scale invariant in the large but finite number limit. We demonstra…
Method predicts which high-dimensional correlation signs will change in the future.
Study examines NFT market dynamics using correlation and noise analysis.
We describe a method to determine the eigenvalue density of empirical covariance matrix in the presence of correlations between samples. This is a straightforward generalization of the method developed earlier by the authors for uncorrelated samples. The method allows for exact determination of the experimental spectru…
This paper sets thresholds for recovering vertex correspondences in partially correlated graphs.
We provide an explicit formula giving the optimal number of paths needed to simulate two correlated Brownian motions.
A new method for Gaussian Processes handles mixed continuous and categorical inputs.
We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corr…
Universal functions derived for topological correlators in Yang-Mills theory.
Financial markets analyzed by reducing correlation matrix complexity.
TCGPN improves stock forecasting by capturing temporal correlation patterns.
The paper tackles fair correlation clustering with fairness constraints.
Correlation matrices are omnipresent in multivariate data analysis. When the number d of variables is large, the sample estimates of correlation matrices are typically noisy and conceal underlying dependence patterns. We consider the case when the variables can be grouped into K clusters with exchangeable dependence; t…
Paper develops efficient algorithms for learning rationalizable equilibria in multiplayer games.
In this study, we attempted to determine how eigenvalues change, according to random matrix theory (RMT), in stock market data as the number of stocks comprising the correlation matrix changes. Specifically, we tested for changes in the eigenvalue properties as a function of the number and type of stocks in the correla…
The paper reduces the complexity of financial market correlation matrices to a 2x2 matrix.
Proposes -CCA for sparse CCA with improved representation learning.
Sparse GCA finds linear relationships in multiple datasets, using gradient descent.
Recent advances in topic models have explored complicated structured distributions to represent topic correlation. For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). While PAM provides more flexibility and gr…
Enhances multimodal generation with Normalizing Flows and correlation analysis.
New hierarchical model improves on standard practice for high-dimensional data.
In the presence of weak overall correlation, it may be useful to investigate if the correlation is significantly and substantially more pronounced over a subpopulation. Two different testing procedures are compared. Both are based on the rankings of the values of two variables from a data set with a large number n of o…
CaLoNet integrates spatial and local correlations for multivariate time series classification.
In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives portfolio, the so-called `London Whale', partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for…
Correlations between asset returns are important in many financial applications. In recent years, multivariate volatility models have been used to describe the time-varying feature of the correlations. However, the curse of dimensionality quickly becomes an issue as the number of correlations is for asse…
Properties of distributions of the number of trades in different intraday time intervals for five stocks traded in MICEX are studied. The dependence of the mean number of trades on the capital turnover is analyzed. Correlation analysis using factorial and moments demonstrates the multifractal nature of these dist…
When assets are correlated, benefits of investment diversification are reduced. To measure the influence of correlations on investment performance, a new quantity - the effective portfolio size - is proposed and investigated in both artificial and real situations. We show that in most cases, the effective portfolio siz…
CDEFs reduce model complexity and uncover time correlations.
Approach for selecting features by discarding nuisance and correlated ones.
This paper generalizes Moody's correlated binomial default distribution for homogeneous (exchangeable) credit portfolio, which is introduced by Witt, to the case of inhomogeneous portfolios. As inhomogeneous portfolios, we consider two cases. In the first case, we treat a portfolio whose assets have uniform default cor…
In this paper, we apply tools from the random matrix theory (RMT) to estimates of correlations across volatility of various assets in the S&P 500. The volatility inputs are estimated by modeling price fluctuations as GARCH(1,1) process. The corresponding correlation matrix is constructed. It is found that the distribut…
The paper describes correlations of spectra for higher rank Anosov representations.
We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view…
Simple model finds high correlation in retail crypto returns.
Study shows disentanglement models learn correlations from data, impacting fairness.
Proposes PSCCA for estimating correlations and canonical correlations in sparse count data.
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features a…
Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation --- a correlation mea…
The paper introduces tests for high-dimensional independence using maximum and average distance correlations.
Neural networks learn faster with correlated latent variables.
The study analyzes the differences between physical and risk-neutral correlation estimates for equity baskets.
We study a distributed estimation problem in which two remotely located parties, Alice and Bob, observe an unlimited number of i.i.d. samples corresponding to two different parts of a random vector. Alice can send bits on average to Bob, who in turn wants to estimate the cross-correlation matrix between the two par…
A new test statistic counts tree co-occurrences to detect edge correlation between networks.