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 shows decay of correlations on specific types of flows.
Method preserves correlations in synthetic data.
We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to both positive and negative pairwise dissimilarities. Then, in the following, we study unsupervised representation learning with such hierarchical correlation clusteri…
Using first principles from inference, we design a set of functionals for the purposes of \textit{ranking} joint probability distributions with respect to their correlations. Starting with a general functional, we impose its desired behaviour through the \textit{Principle of Constant Correlations} (PCC), which constrai…
In a very high-dimensional vector space, two randomly-chosen vectors are almost orthogonal with high probability. Starting from this observation, we develop a statistical factor model, the random factor model, in which factors are chosen at random based on the random projection method. Randomness of factors has the con…
Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial …
New estimator reveals intraday betas mainly driven by correlations.
New neural network captures spatial correlations in wind speed predictions.
Diagonal transformations preserve independence structures in non-Gaussian distributions.
In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of p…
We perform a systematic investigation on the components of the empirical multifractality of financial returns using the daily data of Dow Jones Industrial Average from 26 May 1896 to 27 April 2007 as an example. The temporal structure and fat-tailed distribution of the returns are considered as possible influence facto…
We consider a binary sequence generated by thresholding a hidden continuous sequence. The hidden variables are assumed to have a compound symmetry covariance structure with a single parameter characterizing the common correlation. We study the parameter estimation problem under such one-parameter models. We demonstrate…
Locally private algorithm improves online federated learning with correlated noise.
Domain adaptation aims to assist the modeling tasks of the target domain with knowledge of the source domain. The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA). A core issue of HDA is how to pre…
Proposes a method to generate private synthetic data in a decentralized setting using correlated noise.
Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.
GRASP removes spurious correlations in fine-tuned models, improving task performance and reducing bias.
Enhances multimodal generation with Normalizing Flows and correlation analysis.
A new geometric framework embeds correlation matrices into Euclidean space for scalable brain network analysis.
New risk measures improve portfolio diversification and stability.
In structural credit risk models, default events and the ensuing losses are both derived from the asset values at maturity. Hence it is of utmost importance to choose a distribution for these asset values which is in accordance with empirical data. At the same time, it is desirable to still preserve some analytical tra…
The paper uses deep learning to detect financial market regimes from correlation matrices.
Study the structure of international trade through hypergraphs.
In this paper we provide evidence that financial option markets for equity indices give rise to non-trivial dependency structures between its constituents. Thus, if the individual constituent distributions of an equity index are inferred from the single-stock option markets and combined via a Gaussian copula, for examp…
CSTS benchmarks time series clustering by evaluating correlation structures.
Standardizes weighted ranking correlation coefficients to maintain zero expected value.
We use Random Matrix Theory (RMT) and information theory to analyze the correlations and flow of information between 64,939 news from The New York Times and 40 world financial indices during 10 months along the period 2015-2016. The set of news was quantified and transformed into daily polarity time series using tools …
Paper breaks down risk contribution into inherent and correlation risk components.
Spectral denoising recovers meaningful network structure from noisy financial correlations.
Pricing and hedging exotic options using local stochastic volatility models drew a serious attention within the last decade, and nowadays became almost a standard approach to this problem. In this paper we show how this framework could be extended by adding to the model stochastic interest rates and correlated jumps in…
Quantum circuits predict volatility dynamics preserving asymmetry.
Neighbor Mixture Model captures node correlations in graphs.
Two new algorithms reduce feature space while preserving non-linear relationships.
New method improves tensor completion by selectively preserving important elements.
A fast method learns plasma collision kernels from simulations, improving kinetic models.
One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation becomes an appealing objective because of its operational meaning and desirable propert…
Interpretable surrogates of black-box predictors trained on high-dimensional tabular datasets can struggle to generate comprehensible explanations in the presence of correlated variables. We propose a model-agnostic interpretable surrogate that provides global and local explanations of black-box classifiers to address …
Develops log-Euclidean Lie groups for SPD and correlation matrices.
The paper extends sequences while preserving statistical properties using a mixture model.
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
We characterize when a convex risk measure associated to a law-invariant acceptance set in can be extended to , , preserving finiteness and continuity. This problem is strongly connected to the statistical robustness of the corresponding risk measures. Special attention is paid to concre…
We consider a few quantities that characterize trading on a stock market in a fixed time interval: logarithmic returns, volatility, trading activity (i.e., the number of transactions), and volume traded. We search for the power-law cross-correlations among these quantities aggregated over different time units from 1 mi…
The stability of the financial system is associated with systemic risk factors such as the concurrent default of numerous small obligors. Hence it is of utmost importance to study the mutual dependence of losses for different creditors in the case of large, overlapping credit portfolios. We analytically calculate the m…
Introduces Spectral Attention for better long-range time series forecasting.
Study on martingale property and moment explosions in signature volatility models.
Variable selection is a challenging issue in statistical applications when the number of predictors far exceeds the number of observations . In this ultra-high dimensional setting, the sure independence screening (SIS) procedure was introduced to significantly reduce the dimensionality by preserving the true mod…
Quantum model generates complex time series data with preserved temporal dynamics.
Finding relationships between multiple views of data is essential both for exploratory analysis and as pre-processing for predictive tasks. A prominent approach is to apply variants of Canonical Correlation Analysis (CCA), a classical method seeking correlated components between views. The basic CCA is restricted to ma…