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Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

Author

Listed:
  • Qun ZHANG

    (ETH Zurich)

  • Qunzhi ZHANG

    (ETH Zurich)

  • Didier SORNETTE

    (ETH Zurich and Swiss Finance Institute)

Abstract

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence\textsuperscript{TM} and Trust\textsuperscript{TM} indicators that enrich considerably the diagnostic of bubbles. Using extensive synthetic signals, a detailed analysis of the "S\&P 500 1987" bubble and the application to 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.

Suggested Citation

  • Qun ZHANG & Qunzhi ZHANG & Didier SORNETTE, 2015. "Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities," Swiss Finance Institute Research Paper Series 15-43, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1543
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    Citations

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    Cited by:

    1. V. Filimonov & G. Demos & D. Sornette, 2017. "Modified profile likelihood inference and interval forecast of the burst of financial bubbles," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1167-1186, August.
    2. Li, Chong, 2017. "Log-periodic view on critical dates of the Chinese stock market bubbles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 305-311.
    3. Guilherme Demos & Didier Sornette, 2017. "Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions," Papers 1707.07162, arXiv.org.

    More about this item

    Keywords

    Financial bubble; Log-periodic power law singularity (LPPLS); Quantile regression; Early warning signals; Time scale; Probabilistic forecast;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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