GitHub topics: factor-models

Repositories

princeoncada/quant-pca-risk

Applies Principal Component Analysis (PCA) to daily returns of 20 US equities (2015–2025) to uncover hidden risk factors. Explores variance explained, scree, loadings, factor returns, covariance reconstruction, and Varimax rotation. Results show 3–5 PCs capture ~75% of portfolio risk.

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etiennechatreaux/DimRed-KAN-Finance

Exploring Kolmogorov–Arnold Networks (KANs) as nonlinear autoencoders for dimensionality reduction in finance

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zjwang1013/sparseGFM

sparseGFM implements sparse generalized factor models for dimension reduction and variable selection in high-dimensional continuous, count, and binary data. Features include 12 penalty functions (Lasso, SCAD, MCP and their adaptive/group versions), automatic hyperparameter selection via cross-validation, and missing data support.

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lunageens/PerceivedMarketDependence

An empirical analysis of European markets. This thesis compares the perceived dependence of stock and market returns, as measured by the frequency of comovement following Ungeheuer and Weber (2020), with the traditional interpretation of market dependency measured by Sharpe’s beta (1964).

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akuzamilyt/high_dimensional_matrix_variate_diffusion_index_models

🐙 High-dimensional matrix-variate diffusion index models: fast estimation, factor extraction and inference for large matrix time series.

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chirindaopensource/high_dimensional_matrix_variate_diffusion_index_models

End-to-end Python implementation of Ma et al.'s (2025) matrix-variate diffusion index models for macroeconomic forecasting. Features α-PCA factor extraction, supervised screening, and ILS estimation for high-dimensional forecasting with preserved structural information.

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a91quaini/intrinsicFRP

An R package for Factor Model Asset Pricing

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chirindaopensource/randomized_test_alpha

End-to-End Python implementation of Massacci et al.'s (2025) novel Randomized Alpha Test for high-dimensional factor models. Features robust OLS estimation, Extreme Value Theory-based inference, Monte Carlo simulation engine, and rolling-window empirical analysis. Handles N>T panels with non-Gaussian, heteroskedastic returns.

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