Locally Adaptive Modeling of Unconditional Heteroskedasticity
Type
working paper
Date Issued
2025-06-20
Author(s)
Abstract
We study local change-point detection in variance using generalized likelihood ratio tests. Building on Suvorikova & Spokoiny (2017), we utilize the multiplier bootstrap to approximate the unknown, non-asymptotic distribution of the test statistic and introduce a multiplicative bias correction that improves upon the existing additive version. This proposed correction offers a clearer interpretation of the bootstrap estimators while significantly reducing computational costs. Simulation results demonstrate that our method performs comparably to the original approach. We apply it to the growth rates of U.S. inflation, industrial production, and Bitcoin returns.
Language
English
Keywords
generalized likelihood ratio test
multiplier bootstrap
local change point detection
economic and financial variance
Publisher
Elsevier BV
Pages
53