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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.

168,695 papers · 148 categories

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12 results for Chile

This review examines TDFs in Chile's pension reform, recommending dynamic glide paths and diversified benchmarks.

problem Chile's pension system needs to adapt to modern investment strategies.
method Comprehensive review of TDFs, highlighting challenges and opportunities for Chilean regulators and fund managers.
result Dynamic glide paths and diversified benchmarks are recommended for better retirement outcomes.

This study forecasts climate data in Chile using EOFs and machine learning models.

problem Predicting climatic variability in Chile for resource management and planning.
method Combines EOF decomposition, wavelet analysis, and neural networks for spatiotemporal forecasting.
result Improved accuracy in forecasting climate data through a hybrid ML approach.

The study improves solar irradiance forecasts for Chile using machine learning.

problem Accurate short-term PV power forecasts for Chile's Atacama Desert.
method 8-member ensemble forecasts of solar irradiance using WRF model, calibrated with EMOS and DRN.
result Machine learning-based post-processing methods improve forecast accuracy and calibration.

Boosting algorithms predict financial vulnerability of farmers in Chile and Tunisia.

problem Predict financial vulnerability of farmers in Chile and Tunisia using environmental data.
method Interpretable boosting algorithms based on ridge-regularized generalized linear models.
result Interaction effects improve predictive power only when included in two-step boosting.

This is a survey of our program of perturbative quantization of gauge theories on manifolds with boundary compatible with cutting/pasting and with gauge symmetry treated by means of a cohomological resolution (Batalin-Vilkovisky) formalism. We also give two explicit quantum examples -- abelian BF theory and the Poisson…

2016-02-01abs ↗pdf ↗

Study applies HRP to Latin American markets, showing smoother risk-return profile.

problem Lack of empirical analyses of HRP in Latin American markets.
method Hierarchical Risk Parity (HRP) with hierarchical clustering and recursive bisection.
result HRP portfolio outperforms Max Sharpe portfolio in NUAM markets, with smoother risk-return profile.

A graph neural network improves multivariate post-processing of ensemble forecasts.

problem Systematic biases in ensemble forecasts and loss of dependencies across forecast dimensions.
method A composite-Loss Graph Neural Network (dualGNN) trained with a composite loss function combining ES and VS.
result The dualGNN outperforms traditional methods in multivariate verification metrics and captures spatial relationships.

A declining CVaR glidepath framework for TDF design with Chilean pension system application

problem Designing Target-Date Funds around an explicit return objective while controlling risk
method Propose a framework for designing TDFs with a declining CVaR constraint
result Key feature: conservative evaluation of each glidepath