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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.

169,341 papers · 148 categories

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0111 · Mar 200519922001200920182026
16 results for VG

The paper analyzes a five-parameter Variance-Gamma model for European option pricing.

problem Developing a stochastic volatility model for accurate European option pricing.
method Introduced a five-parameter Variance-Gamma model and applied it to empirical data.
result The five-parameter VG model produces underpriced OTM and overpriced ITM options compared to the Black-Scholes model.

Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.

problem Understanding the structure and dynamics of crude oil futures markets during global challenges.
method Visibility graph analysis of daily and high-frequency data.
result Crude oil futures markets exhibit small-world properties and assortative mixing, with unique sensitivities to global disruptions.

We discuss various analytic and numerical methods that have been used to get option prices within a framework of the VG model. We show that some popular methods, for instance, Carr-Madan's FFT method could blow up for certain values of the model parameters even for an European vanilla option. Alternative methods - one …

2005-03-16abs ↗pdf ↗

Given a virtual knot KK, we construct a group VGKVG_K called the virtual knot group, and we use the elementary ideals of VGKVG_K to define invariants of KK called the virtual Alexander invariants. For instance, associated to the k=0k=0 ideal is a polynomial HK(s,t,q)H_K(s,t,q) in three variables which we call the virtual Alexa…

2014-09-04abs ↗pdf ↗

This paper extends subordinated models to include stochastic time changes, improving financial modeling.

problem Improving financial models to better capture market features like jump clustering and volatility persistence.
method Subordinated processes with Levy and stochastic arrival mechanisms.
result Strong consistency and asymptotic normality results for VG and VGSA processes under various stochastic arrival models.

Study compares L1 and VG sparsity priors in inverse problems.

problem Sparse regularization in inverse problems with incomplete or corrupted measurements.
method Compared L1 regularization with Variational Garrote (VG), a probabilistic method approximating L0 sparsity.
result VG often achieves lower minimum generalization error and improved stability in strongly underdetermined regimes.

Latent Gaussian models (LGMs) are widely used in statistics and machine learning. Bayesian inference in non-conjugate LGMs is difficult due to intractable integrals involving the Gaussian prior and non-conjugate likelihoods. Algorithms based on variational Gaussian (VG) approximations are widely employed since they str…

2013-06-05abs ↗pdf ↗

A framework for faster, better infographic design by non-experts and experts alike.

problem Designing infographics is time-consuming and tedious for non-experts and even professionals.
method Semi-automated infographic framework for structured and flow-based designs, including automatic design ranking and customization options.
result Designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining quality.

The study explores mixed Killing vector fields on almost coKähler manifolds.

problem Characterizing mixed Killing vector fields on almost coKähler manifolds.
method Generalized Bochner's theorem for mixed Killing vector fields and studied in the context of almost coKähler structures.
result The Reeb vector field on an almost coKähler manifold is mixed Killing if and only if the operator h=0h=0.

In this paper, we investigate the fixed-point set of an element of a CAT(0) group in its boundary. Suppose that a group GG acts geometrically on a CAT(0) space XX. Let gGg\in G and let Fg\mathcal{F}_g be the fixed-point set of gg in the boundary X\partial X. Then we show that Fg=L(Zg)\mathcal{F}_g=L(Z_g), where ZgZ_g is …

2005-10-24abs ↗pdf ↗