Robustified Gaussian quasi-BIC for volatility
Statistics Theory
2026-04-01 v1 Statistics Theory
Abstract
We develop a theoretical foundation for robust model comparison in a class of non-ergodic continuous volatility regression models contaminated by finite-activity jumps. Using the density-power weighting and the H\"{o}lder(-inequality)-based normalization of the conventional Gaussian quasi-likelihood function, we propose two Schwarz-type statistics and also establish their model selection consistency with respect to the minimal true parametric volatility coefficient. Numerical experiments are conducted to illustrate our theoretical findings.
Keywords
Cite
@article{arxiv.2603.29463,
title = {Robustified Gaussian quasi-BIC for volatility},
author = {Shoichi Eguchi and Hiroki Masuda},
journal= {arXiv preprint arXiv:2603.29463},
year = {2026}
}