LPPLS bubble indicators over two centuries of the S&P 500 index

Qunzhi Zhang, Didier Sornette, Mehmet Balcilar, Rangan Gupta, Zeynel Ozdemir () and Ibrahim Yetkiner ()

Physica A: Statistical Mechanics and its Applications, 2016, vol. 458, issue C, 126-139

Abstract: The aim of this paper is to present novel tests for the early causal diagnostic of positive and negative bubbles in the S&P 500 index and the detection of End-of-Bubble signals with their corresponding confidence levels. We use monthly S&P 500 data covering the period from August 1791 to August 2014. This study is the first work in the literature showing the possibility to develop reliable ex-ante diagnostics of the frequent regime shifts over two centuries of data. We show that the DS LPPLS (log-periodic power law singularity) approach successfully diagnoses positive and negative bubbles, constructs efficient End-of-Bubble signals for all of the well-documented bubbles, and obtains for the first time new statistical evidence of bubbles for some other events. We also compare the DS LPPLS method to the exponential curve fitting and the generalized sup ADF test approaches and find that DS LPPLS system is more accurate in identifying well-known bubble events, with significantly smaller numbers of false negatives and false positives.

Keywords: S&P 500; LPPL method; Stock market bubble; Forecast; Bubble indicators (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Working Paper: LPPLS Bubble Indicators over Two Centuries of the S&P 500 Index (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:458:y:2016:i:c:p:126-139

DOI: 10.1016/j.physa.2016.03.103

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