Locally Adaptive Modeling of Unconditional Heteroskedasticity

Fengler, Matthias R.; Jäger, Bruno; Okhrin, Ostap

doi:10.51387/25-NEJSDS91

The New England Journal of Statistics in Data Science

Locally Adaptive Modeling of Unconditional Heteroskedasticity

Matthias R. Fengler Bruno Jäger Ostap Okhrin

https://doi.org/10.51387/25-NEJSDS91

Pub. online: 10 September 2025 Type: Methodology Article

Open Access

Area: Statistical Methodology

Accepted
18 July 2025

Published
10 September 2025

Abstract

We study local change point detection in variance using generalized likelihood ratio tests. Building on [24], 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.

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Keywords

Generalized likelihood ratio test Multiplier bootstrap Local change point detection Economic and financial variance

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