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

27 Pages Posted: 14 Oct 2015

See all articles by Qun Zhang

Qun Zhang

Guangdong University of Foreign Studies; ETH Zürich; South China University of Technology

Qunzhi Zhang

ETH Zürich

Didier Sornette

Risks-X, Southern University of Science and Technology (SUSTech); Swiss Finance Institute; ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Tokyo Institute of Technology

Date Written: October 14, 2015

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

JEL Classification: C14, C21, C53, G01, G17

Suggested Citation

Zhang, Qun and Zhang, Qun and Zhang, Qunzhi and Sornette, Didier, Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities (October 14, 2015). Swiss Finance Institute Research Paper No. 15-43, Available at SSRN: https://ssrn.com/abstract=2674128 or http://dx.doi.org/10.2139/ssrn.2674128

Qun Zhang

Guangdong University of Foreign Studies ( email )

Collaborative Innovation Center for Silk Road
Guangzhou, Guangdong
China

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

South China University of Technology ( email )

Wushan
Guangzhou, AR Guangdong 510640
China

Qunzhi Zhang

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Didier Sornette (Contact Author)

Risks-X, Southern University of Science and Technology (SUSTech) ( email )

1088 Xueyuan Avenue
Shenzhen, Guangdong 518055
China

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
Zurich, ZURICH CH-8092
Switzerland
41446328917 (Phone)
41446321914 (Fax)

HOME PAGE: http://www.er.ethz.ch/

Tokyo Institute of Technology ( email )

2-12-1 O-okayama, Meguro-ku
Tokyo 152-8550, 52-8552
Japan

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