ARCH models in Python
Model Confidence Set (MCS) implementation in Python
Easily evaluate your forecasts with (multivariate) Diebold-Mariano and (multivariate) Giacomini-White tests of equal predictive ability and MCS.
This repository includes the scripts to replicate the results of my paper entitled "A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection".
The MCB for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level.
End-to-End Python implementation of Lacava's (2026) "Shifting Correlations" research. Features Numba-compiled GJR-GARCH volatility filtering, augmented DCC-X framework with exogenous Trade Policy Uncertainty integration, structural break testing, out-of-sample GMV optimization, and Model Confidence Set validation.
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