Log-periodicity: Fact or fiction?
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https://creativecommons.org/licenses/by/4.0/
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Kuvaus
© 2025 The Author. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
A common empirical practice in LPPLS applications is to calibrate the model under parameter bounds and then declare an “LPPLS signature” when ADF/PP tests on calibration residuals reject a unit root at conventional tabulated critical values. We show that this procedure exhibits substantial size distortion. Using synthetic series that preserve the roughness and volatility of financial data while excluding log-periodic structure, we compute bootstrap critical values by re-estimating the full two-stage procedure on each synthetic sample. Applied to S&P 500 monthly and daily data, conventional thresholds yield inflated rejection rates. In contrast, the bootstrap restores empirical size to nominal levels and overturns many purported signatures. These findings highlight the need for estimation-aligned inference in LPPLS diagnostics and call for a re-examination of published LPPLS evidence that may reflect size-induced false positives.
Emojulkaisu
ISBN
ISSN
1873-8079
1057-5219
1057-5219
Aihealue
Kausijulkaisu
International review of financial analysis|110
OKM-julkaisutyyppi
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä (vertaisarvioitu)