Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash

Berrada, Tony; Coupy, Sebastien

doi:10.2139/ssrn.2647354

Cited by 23 publications

(71 citation statements)

References 34 publications

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“…1 appear in 2007 and 2015, corresponding to the two infamous crashes following two huge bubbles from 2005 to December 2007 30 and from 2014 to June 2015. 50 It is found that the AVR statistic is signi¯cantly positive around the crash, indicating a very di®erent market correlation structure. The return time series exhibit statistically signi¯cant nonlinear dependence if the p-value falls down beneath the dashed line corresponding to the signi¯cance level of 5%.…”

Section: -34mentioning

confidence: 96%

Time-Varying Return Predictability in the Chinese Stock Market

Shi

Jiang

Zhou

2017

Rep. Adv. Phys. Sci.

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China's stock market is the largest emerging market in the world. It is widely accepted that the Chinese stock market is far from e±ciency and it possesses possible linear and nonlinear dependencies. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic variance ratio test and the generalized spectral test. We¯nd that the return predictability vary over time and a signi¯cant return predictability is observed around market turmoils. Our¯ndings are consistent with the Adaptive Markets Hypothesis (AMH) and have practical implications for market participants and policy makers. A predictability index can be constructed for each asset, which might help warn a crisis is in store, ease the development of the ongoing bubble, and stabilize the market.

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Section: -34mentioning

confidence: 96%

Time-Varying Return Predictability in the Chinese Stock Market

Shi

Jiang

Zhou

2017

Rep. Adv. Phys. Sci.

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“…Following the methodology of Sornette et al [24] and Zhang et al [25], we use the LPPLS Confidence Indicator as a diagnostic tool for the recognition of bubbles. In a nutshell, the LPPLS Confidence Indicator at a given time t 2 is the fraction of windows [t 1 , t 2 ], obtained by scanning t 2 − t 1 over a certain window size interval, for which the calibration of the log-price of Bitcoin by the LPPLS formula (1) passes the criteria shown in Table 2.…”

Section: Lppls Multiscale Confidence Indicators As Bubble Diagnosticsmentioning

confidence: 99%

“…In order to minimise calibration problems and address the sloppiness of the model (16) with respect to some of its parameters (and in particular t c ), we use a number of filters to select the viable solutions, which are summarised in Table 4. For further information about the sloppiness of the LPPLS model, we refer to [24,83,84]. These filters derive from the empirical evidence gathered in investigations of previous bubbles [85,25,24].…”

Section: Appendix C Estimation Of the Lppls Modelmentioning

confidence: 99%

“…For further information about the sloppiness of the LPPLS model, we refer to [24,83,84]. These filters derive from the empirical evidence gathered in investigations of previous bubbles [85,25,24]. Only those calibrations that meet the conditions given in Table (4 other words they are more likely to be selected when deciding according to the criterion above.…”

Section: Appendix C Estimation Of the Lppls Modelmentioning

confidence: 99%

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Dissection of Bitcoin's Multiscale Bubble History

2018

Self Cite

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“…Lin, Ren and Sornette [22] developed a self-consistent model for explosive financial bubbles which combines a mean-reverting volatility process and a stochastic conditional return. Sornette, Demos, Zhang, Cauwels, Filimonov and Zhang [23] proposed the LPPLS Confidence indicator and the LPPLS Trust indicator to evaluate the performance of the real-time prediction of bubble crash in 2015 Shanghai stock market. Filimonov, Demos and Sornette [24] calibrated the LPPLS model by applying the modified profile likelihood inference method and obtained the interval estimation for the critical time.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of Bitcoin bubble crashes

Shu

Zhu

2020

Physica A: Statistical Mechanics and its Applications

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In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale -on a day to week scale, and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale -on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk in not only the cryptocurrency market but also other financial markets.

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Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash

Cited by 23 publications

References 34 publications

Time-Varying Return Predictability in the Chinese Stock Market

Time-Varying Return Predictability in the Chinese Stock Market

Dissection of Bitcoin's Multiscale Bubble History

Real-time prediction of Bitcoin bubble crashes

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