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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.

168,657 papers · 148 categories

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1122 · Jun 202619922001200920172026
9 results for multi-sector

Improves industry classification for diversified companies.

problem Traditional industry classification struggles with multi-sector conglomerates.
method Bayesian Non-Parametrics, Markov Updating, and hierarchical modeling.
result MIS-2 provides a measurable improvement over GICS in predicting future correlations.

Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.

problem Explaining the difference in default dependence between monthly and annual aggregation.
method Dynamic low-rank state-space model with AR(1) latent credit-state factors.
result Effective correlation matrices and rank copulas are generated from monthly default count data.

Sector specific multifactor CES elasticity of substitution and the corresponding productivity growths are jointly measured by regressing the growths of factor-wise cost shares against the growths of factor prices. We use linked input-output tables for Japan and the Republic of Korea as the data source for factor price …

2016-08-03abs ↗pdf ↗

We introduce a complete obstruction to the existence of nonvanishing vector fields on a closed orbifold QQ. Motivated by the inertia orbifold, the space of multi-sectors, and the generalized orbifold Euler characteristics, we construct for each finitely generated group ΓΓ an orbifold called the space of ΓΓ-sectors o…

2008-07-17abs ↗pdf ↗

For a finitely generated discrete group ΓΓ, the ΓΓ-sectors of an orbifold QQ are a disjoint union of orbifolds corresponding to homomorphisms from ΓΓ into a groupoid presenting QQ. Here, we show that the inertia orbifold and kk-multi-sectors are special cases of the ΓΓ-sectors, and that the ΓΓ-sectors are orbif…

2009-02-06abs ↗pdf ↗

A new ML algorithm solves complex economic control problems.

problem Solving high-dimensional, finite-horizon stochastic control problems in economics.
method Deep neural network representation of optimal policy functions with three key features.
result Efficiently solves various economic control problems including recursive utility and growth models.

Breaks circular dependency in synthetic option pricing with a novel model.

problem Circular dependency in implied volatility limits synthetic data for machine learning and risk analysis.
method Uses a Jump-Hidden Markov Model to generate price paths and a modified Heston process to convert paths into implied volatility.
result Framework generates realistic synthetic American option prices without external calibration.