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.
Statistical comparison of forecasting models: Diebold-Mariano, stationary block bootstrap, Giacomini-White, Model Confidence Set, and a forecast stability diagnostic.
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.
Out-of-sample realized-volatility forecasting benchmark with HAR models, microstructure estimators, and Model Confidence Set testing.
Add a description, image, and links to the model-confidence-set topic page so that developers can more easily learn about it.
To associate your repository with the model-confidence-set topic, visit your repo's landing page and select "manage topics."