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

Qun Zhang, Qunzhi Zhang and Didier Sornette
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Qun Zhang: ETH Zurich
Qunzhi Zhang: ETH Zurich
Didier Sornette: ETH Zurich and Swiss Finance Institute

No 15-43, Swiss Finance Institute Research Paper Series from 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.

Keywords: Financial bubble; Log-periodic power law singularity (LPPLS); Quantile regression; Early warning signals; Time scale; Probabilistic forecast (search for similar items in EconPapers)
JEL-codes: C14 C21 C53 G01 G17 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2015-10
New Economics Papers: this item is included in nep-hme
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1543

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