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Locally adaptive modeling of unconditional heteroskedasticity

Author

Listed:
  • Matthias R. Fengler

    (University of St. Gallen - SEPS: Economics and Political Sciences; Swiss Finance Institute)

  • Bruno Jäger

    (Eastern Switzerland University of Applied Sciences)

  • Ostap Okhrin

    (Dresden University of Technology)

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.

Suggested Citation

  • Matthias R. Fengler & Bruno Jäger & Ostap Okhrin, 2025. "Locally adaptive modeling of unconditional heteroskedasticity," Swiss Finance Institute Research Paper Series 25-60, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2560
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