Locally adaptive modeling of unconditional heteroskedasticity

Matthias Fengler, Bruno Jäger and Ostap Okhrin
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Bruno Jäger: Eastern Switzerland University of Applied Sciences
Ostap Okhrin: Dresden University of Technology

No 25-60, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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.

Keywords: generalized likelihood ratio test; multiplier bootstrap; local change point detection; economic and financial variance (search for similar items in EconPapers)
Pages: 53 pages
Date: 2025-06
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2560

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