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.
New method clusters strong and weak views effectively, improving performance by up to 40%.
The noncompact Yamabe flow can lead to incomplete metrics over infinite time.
New flow preserves singularities on incomplete manifolds.
The possibility of statistical evaluation of the market completeness and incompleteness is investigated for continuous time diffusion stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one.…
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
In order to find a way of measuring the degree of incompleteness of an incomplete financial market, the rank of the vector price process of the traded assets and the dimension of the associated acceptance set are introduced. We show that they are equal and state a variety of consequences.
This paper solves hedging in incomplete markets using neural networks.
We investigate the possibility of statistical evaluation of the market completeness for discrete time stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one. The paper shows that market inc…
We consider the problem of optimal consumption of multiple goods in incomplete semimartingale markets. We formulate the dual problem and identify conditions that allow for existence and uniqueness of the solution and give a characterization of the optimal consumption strategy in terms of the dual optimizer. We illustra…
In the setting of exponential investors and uncertainty governed by Brownian motions we first prove the existence of an incomplete equilibrium for a general class of models. We then introduce a tractable class of exponential-quadratic models and prove that the corresponding incomplete equilibrium is characterized by a …
Study Dirac operators on incomplete cusp edge spaces, proving self-adjointness and Fredholm properties.
Algorithm recovers sparse PCA support from incomplete data.
New ML method detects incomplete bid-rigging cartels.
We show that when the price process represents a fully incomplete market, the optimal super-replication of any Markovian claim with being nonnegative and lower semicontinuous is of buy-and-hold type. Since both (unbounded) stochastic volatility models and rough volatility models are examples of …
The paper extends cost-efficiency analysis to incomplete markets.
New estimator for symmetric kernel expectations, robust to missing data.
Study optimal investment and consumption in incomplete markets with nonlinear expectations.
Improved VAE estimation from incomplete data using variational mixtures.
Motivated by recent interest in the spectrum of the Laplacian of incomplete surfaces with isolated conical singularities, we consider more general incomplete m-dimensional manifolds with singularities on sets of codimension at least 2. With certain restrictions on the metric, we establish that the spectrum is discrete …
Solves ambiguity in incomplete markets by minimizing price measure entropy.
Proves limit curve theorem for incomplete metric spaces, applies to null distance in Lorentzian manifolds.
Abstract and counterexamples show limitations of cost-efficiency in incomplete markets.
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of Gaussian Mixture Models for incomplete data on different scales, we are able to learn a kernel for incomplete data that does not depend on a…
GapNet uses incomplete datasets to train neural networks, improving disease detection.
The paper proves pseudolocality theorems for Ricci flows on incomplete manifolds.
New method estimates model parameters from incomplete data.
New method discovers causal structures from incomplete data.
Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between per…
We derive a formula for the index of a Dirac operator on a compact, even-dimensional incomplete edge space satisfying a "geometric Witt condition". We accomplish this by cutting off to a smooth manifold with boundary, applying the Atiyah-Patodi-Singer index theorem, and taking a limit. We deduce corollaries related to …
New theorem shows black holes are real, with clearer event horizon.
In this work we introduce the notion of fully incomplete markets. We prove that for these markets the super-replication price coincide with the model free super-replication price. Namely, the knowledge of the model does not reduce the super-replication price. We provide two families of fully incomplete models: stochast…
Paper proposes a tensor data model for incomplete imaging data.
Robustly estimates mean in incomplete data with corrupted examples.
In this paper we prove local existence of a Ricci de Turck flow starting at a space with incomplete edge singularities and flowing for a short time within a class of incomplete edge manifolds. We derive regularity properties for the corresponding family of Riemannian metrics and discuss boundedness of the Ricci curvatu…
We consider the problem of finding embedded closed geodesics on the two-sphere with an incomplete metric defined outside a point. Various techniques including curve shortening methods are used.
Synthetic proof of Gannon-Lee theorem for spacetimes.
This essay quantifies convexities in incomplete markets using entropy, adjusting prices for risk and incompleteness.
We define a system of ODE that gives Einstein 4-dimensional metrics. We found new Ricci-flat incomplete metric of cohomogeneity 1 in explicit formulas and study its characteristics.
Deep learning improves option pricing in incomplete markets.
A new method for clustering incomplete data.
Study finds optimal retirement timing in uncertain wage scenarios.
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomp…
A new criterion HBIC improves model selection for factor analysis with missing data.
Extends cohomology to incomplete Riemannian manifolds.
In 2002, Isenberg-Mazzeo-Pollack (IMP) constructed a series of vacuum initial data sets via a gluing construction. In this paper, we investigate some local geometry of these initial data sets as well as implications regarding their spacetime developments. In particular, we state conditions for the existence of outer tr…
In real-world applications, not all instances in multi-view data are fully represented. To deal with incomplete data, Incomplete Multi-view Learning (IML) rises. In this paper, we propose the Joint Embedding Learning and Low-Rank Approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete d…
The paper analyzes how wealth affects investment strategies in incomplete markets.
We clarify the relationship between the null geodesic completeness of an Einstein Lorentz manifold and its conformal Kobayashi pseudodistance. We show that an Einstein manifold has at least one incomplete null geodesic if its pseudodistancfe is nontrivial. If its pseudodistance is nondegenerate, all of its null geodesi…