gjr-garch-x: GJR-GARCH Models with Exogenous Variance Regressors
Abstract
A Python implementation of Glosten-Jagannathan-Runkle (1993) GARCH models with support for exogenous regressors in the conditional variance equation. Features Student-t innovations, quasi-maximum likelihood estimation, and numerical Hessian-based inference. Designed for event studies, sentiment analysis, and regime-dependent volatility modeling.
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 12 July 2026