We quantify how co-jumps impact correlations in currency markets. To disentangle the continuous part of quadratic covariation from co-jumps, and study the influence of co-jumps on correlations, we propose a new wavelet-based estimator. The proposed estimation framework is able to localize the co-jumps very precisely th…
On-device research index
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.
168,982 papers · 148 categories
Trend · papers per month
48 results for “CO(d”
The paper studies co-Hamiltonian diffeomorphisms on compact cosymplectic manifolds.
problem Fix-point theory and co-Hamiltonian diffeomorphisms on compact cosymplectic manifolds.
method Fix-point theory, Arnold's conjecture, co-Hofer norms, topologies, approximations lemmas.
result Minimum number of fix points for co-Hamiltonian diffeomorphisms is at least 1.
Co-branding improves stock performance for firms.
problem Little research on co-branding's impact on firm stock value.
method Developed a conceptual framework and tested hypotheses.
result Co-branding events lead to positive abnormal returns.
Randomized spectral co-clustering speeds up large-scale directed networks.
problem Co-clustering directed networks efficiently for large-scale data.
method Randomized spectral co-clustering algorithms using random-projection and random-sampling techniques.
result Theoretical and numerical validation of approximation and misclustering error rates.
Study reveals strong co-jumping behavior in U.S. yield curves compared to Europe.
problem Understanding co-jumps in interest rate futures markets.
method Localized co-jumps through wavelet coefficients, identified statistically significant ones, and analyzed using high frequency data.
result Stronger co-jumping behavior in U.S. yield curves compared to European ones.
Paper proposes a new co-clustering method for overlapping clusters and outliers.
problem Real-world datasets often contain overlaps and outliers in co-clusters.
method Formulated Non-Exhaustive, Overlapping Co-Clustering problem and developed NEO-CC algorithm.
result NEO-CC algorithm effectively captures underlying co-clustering structure of real-world data.
ecpc R-package improves high-dimensional prediction with co-data.
problem High-dimensional prediction with more variables than samples.
method Adaptive ridge penalised models with co-data, including continuous co-data.
result Improved variable selection and prediction performance.
Toric co-Higgs sheavesmath.AG
Study co-Higgs sheaves on toric varieties, finding explicit examples.
problem Characterizing and understanding co-Higgs sheaves on toric varieties.
method Characterization and explicit computation of examples.
result Explicit examples of co-Higgs sheaves on toric varieties computed.
Identifies images of determinant morphism for specific co-Higgs bundles.
problem Determining images of determinant morphism for co-Higgs bundles.
method Identifying images of the determinant morphism of trace-free co-Higgs bundles modeled on rank 2 Schwarzenberger bundles.
result Identified images of the determinant morphism for specific co-Higgs bundles.
A new NMF model for co-clustering and data approximation.
problem Finding a low rank approximation for nonnegative data.
method Generalizes separability assumption for NMF, proposing Co-Separable NMF (CoS-NMF).
result CoS-NMF outperforms state-of-the-art methods in co-clustering and data approximation.
We show that there exist infinitely many pairwise distinct non-closed G_2-manifolds (some of which have holonomy full G_2) such that they admit co-oriented contact structures and have co-oriented contact submanifolds which are also associative. Along the way, we prove that there exists a tubular neighborhood N of every…
New COS method formula improves option pricing accuracy.
problem Determining the optimal truncation range for COS method.
method Derive new formula using Markov's inequality to ensure convergence.
result New formula leads to more accurate option pricing.
Develops Co_SVR for multi-fidelity modeling combining HF and LF models.
problem Combining high-fidelity and low-fidelity models for efficient design.
method Support vector regression with kernel function and heuristic algorithm.
result Co_SVR outperforms other multi-fidelity surrogate models in prediction accuracy.
We construct cup and cap products in intersection (co)homology with field coefficients. The existence of the cap product allows us to give a new proof of Poincare duality in intersection (co)homology which is similar in spirit to the usual proof for ordinary (co)homology of manifolds.
Proposes a method for two-sided clustering of co-occurrence data.
problem Efficient clustering of co-occurrence data in multi-view settings.
method Information-theoretic multi-view co-clustering (MV-ITCC).
result Demonstrates superior performance on text and image datasets.
The logic of uncertainty as a logic of experience and chance and the co~event-based Bayes' theoremq-fin.GN
The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one essence, which is called co~event. The theory of experience and chance is the theory of c…
Develops a measure-theoretic framework for complex co-occurrence data.
problem Modeling and interpreting complex co-occurrences in high-dimensional data.
method Introduces measure-theoretic probability and conditional probability, investigates E-integrals.
result Establishes a rigorous measure-theoretic foundation for co-occurrence modeling.
A new method for higher-order co-occurrences in hypergraphs.
problem Computing higher-order co-occurrences in hypergraphs.
method Face-splitting product or transpose Khatri-Rao product for higher order tuple co-occurrences.
result Demonstrates the utility of the higher order co-occurrence tensor in NLP and hypergraph models.
Defines and classifies toric co-Higgs bundles on projective toric varieties.
problem Classifying co-Higgs bundles on toric varieties.
method Using Klyachko's fan filtration and studying the co-Higgs bundle fiber at a closed point.
result Provides a Lie-theoretic classification of toric co-Higgs bundles.
This article analyzes the relationship between co-persistence and hedging which indicates co-persistence ratio is just the long-term hedging ratio. The new method of exhaustive search algorithm for deriving co-persistence ratio is derived in the article. And we also develop a new hedging strategy of combining co-persis…
The paper investigates -quasi-Einstein structures on almost co-Kähler manifolds.
problem Investigating -quasi-Einstein structures on almost co-Kähler manifolds.
method Analyzing -quasi-Einstein metrics on almost co-Kähler manifolds and studying their properties.
result The paper proves that -quasi-Einstein structures on almost co-Kähler manifolds are rare and have specific properties.
Co-trading networks reveal dynamic market structures and improve covariance estimation.
problem Modeling high-dimensional stock covariances in US equity markets.
method Co-trading-based pairwise similarity measure for constructing dynamic networks, spectral clustering, robust covariance estimator.
result Co-trading networks capture time-evolving stock dependencies and improve portfolio performance.
Extends co-clustering to mixed numerical and binary data.
problem Co-clustering of mixed data types (numerical and binary).
method Latent block models for mixed data types.
result Effectiveness of the proposed approach on simulated data.
On a complex manifold, a co-Higgs bundle is a holomorphic vector bundle with an endomorphism twisted by the tangent bundle. The notion of generalized holomorphic bundle in Hitchin's generalized geometry coincides with that of co-Higgs bundle when the generalized complex manifold is ordinary complex. Schwarzenberger's r…
Classifies Higgs and co-Higgs bundles on symmetric spaces.
problem Classifying Higgs and co-Higgs bundles over Hermitian symmetric spaces.
method Defined homogeneous principal Higgs and co-Higgs bundles, provided a classification up to isomorphism.
result Defined and classified moduli spaces for each type of bundle.
Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selectionstat.ME
Guided adaptive shrinkage uses co-data to improve feature selection in genomic studies.
problem Feature selection challenges in high-dimensional genomics data, especially in clinical settings.
method Guided adaptive shrinkage methods that use co-data to adapt shrinkage parameters.
result Improves feature selection in genomic studies, demonstrated through comparisons and examples.
Flexible co-data learning improves clinical prediction models.
problem High-dimensional clinical data challenges prediction accuracy.
method Combining domain knowledge and external studies to estimate adaptive multi-group ridge penalties.
result Improves prediction performance and variable selection stability.
Research connects Lie algebras to configuration space (co)homology.
problem Understanding the (co)homology of configuration spaces.
method Identifying Lie algebra (co)homology as a counterpart to configuration space (co)homology.
result Lie algebras and configuration spaces have a deep mathematical relationship.
We show how certain topological properties of co-K{ä}hler manifolds derive from those of the Kähler manifolds which construct them. We go beyond Betti number results and describe the cohomology algebra structure of co-Kähler manifolds. As a consequence, we prove that co-Kähler manifolds satisfy the Toral Rank Conjectur…
For each manifold or effective orbifold and commutative ring , we define a new homology theory , -, and a new cohomology theory , -. For the chain complex is generated by quadruples satisfying relations, where is…
Co-Euler structures were studied by Burghelea and Haller on closed manifolds as dual objects to Euler structures. We extend the notion of co-Euler structures to the situation of compact manifolds with boundary. As an application, by studying their variation with respect to smooth changes of the Riemannian metric, co-Eu…
Dual adversarial co-learning improves multi-domain text classification.
problem Improving text classification across multiple domains.
method Dual adversarial co-learning with shared-private networks and dual adversarial regularizations.
result Achieves state-of-the-art performance on multi-domain sentiment classification datasets.
COS method convergence conditions expanded for heavy-tailed distributions.
problem Ensuring convergence of the COS method for various densities.
method Analyzing truncation error and providing conditions for convergence.
result Conditions for COS method convergence extended to include heavy-tailed distributions.
We show that the co-rays to a ray in a complete non-compact Finsler manifold contain geodesic segments to upper level sets of Busemann functions. Moreover, we characterise the co-point set to a ray as the cut locus of such level sets. The structure theorem of the co-point set on a surface, namely that is a local tree, …
Presented spherical symmetric teleparallel geometry frames and field equations.
problem Teleparallel geometry with spherical symmetry.
method Developed proper and diagonal co-frames, spin connections, and field equations.
result Advantage of diagonal co-frame over proper in f(T) teleparallel gravity.
Study of rays and co-rays in Wasserstein space, introducing Busemann functions.
problem Understanding rays and co-rays in Wasserstein space.
method Representation of rays as probability measures, existence of co-rays, introduction of Busemann functions.
result Existence and properties of co-rays and Busemann functions.
Proposes using external data to improve predictions in medical applications with limited samples.
problem Small sample sizes and complex covariate-response relationships in medical data.
method Integrates external co-data into Bayesian Additive Regression Trees (BART) using an empirical Bayes framework.
result Improves prediction accuracy compared to standard BART, especially for nonlinear relationships.
We show how certain topological properties of co-Kähler manifolds derive from those of the Kähler manifolds which construct them. In particular, we show that the existence of parallel forms on a co-Kähler manifold reduces the computation of cohomology from the de Rham complex to certain amenable sub-cdga's defined by g…
Every torus knot can be represented as a Fourier-(1,1,2) knot which is the simplest possible Fourier representation for such a knot. This answers a question of Kauffman and confirms the conjecture made by Boocher, Daigle, Hoste and Zheng. In particular, the torus knot T(p,q) can be parameterized as x(t)=cos(pt), y(t)=c…
Co-adaptation is a special form of on-line learning where an algorithm must assist an unknown algorithm to perform some task. This is a general framework and has applications in recommendation systems, search, education, and much more. Today, the most common use of co-adaptive algorithms is …
We construct a natural co-Riemannian structure on the manifold of smooth loops in a Riemannian manifold. We show that the smooth loop space of a string manifold is a per-Hilbert-Schmidt locally equivalent co-spin manifold and thus admits a Dirac operator.
Paper proposes efficient co-adaptation of robot morphology and behavior.
problem Infeasibility of co-adapting morphology and behavior in robots due to long manufacturing times and need for new controllers.
method Uses deep reinforcement learning, specifically the soft actor critic algorithm, to automatically and efficiently co-adapt robot morphology and behavior.
result Reduces the number of morphologies and behaviors tested, making co-adaptation more data-efficient.
Paper tackles co-generation with GANs, developing a new algorithm.
problem Inferring the most likely configuration for a subset of variables given the rest.
method Annealed Importance Sampling based Hamiltonian Monte Carlo algorithm.
result Significantly outperforms classical gradient based methods.
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we de…
The COS method for European options pricing is improved with a new bound for the number of terms.
problem Determining the optimal number of terms in the COS method for accurate European option pricing.
method Using Fourier-cosine expansion, the study finds an explicit bound for the number of terms N in the cosine series approximation.
result The COS method achieves exponential convergence when the log-return density is smooth, but not when it has heavy tails.
Co-training improves sequential decision-making policies from multiple views.
problem Learning policies in settings with multiple state-action representations.
method Inspired by co-training for classification, we present a co-training framework for sequential decision making.
result Our framework improves upon learning from a single view alone.
Given a multisymplectic manifold and a Lie algebra acting on it by infinitesimal symmetries, Fregier-Rogers-Zambon define a homotopy (co-)moment as an -algebra-homomorphism from to the observable algebra associated to , in analogy with and generalizing the notio…
Irreducible skew-Berger algebras $\g\subset\gl(n,\Co)$, i.e. algebras spanned by the images of the linear maps $R:\odot^2\Co^n\to\g$ satisfying the Bianchi identity, are classified. These Lie algebras can be interpreted as irreducible complex Berger superalgebras contained in $\gl(0|n,\Co)$.