Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles

Jiang, Zhi‐Qiang; Zhou, Wei‐Xing; Sornette, Didier; Woodard, Ryan; Bastiaensen, Ken; Cauwels, Peter

doi:10.2139/ssrn.1479479

Cited by 18 publications

(8 citation statements)

References 43 publications

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“…Fig 5 is the same as Fig 4 but for LPPLS quantile regressions performed with the more restrictive search conditions m ∈ [0.1, 0.9], ω ∈ [6, 13] and t c ∈ [ t end − 0.20 dt , t end + 0.20 dt ], which are derived from previous investigations [5, 35, 36]. Reducing the search space of the two key nonlinear LPPLS parameters m and ω has two major effects: (i) the distributions of tend to be more stable as a function of t end and bracket the true T c for all cases, except for the earliest t end = 1987.03.19 at the shortest time scale dt = 500 days; (ii) the spreads of values over the different scenarios are narrower, indicating that the LPPLS quantile regressions provide more precise predictions of the true T c .…”

Section: Methodology Metrics and Tests On “Sandp 500 1987” Bubblementioning

confidence: 99%

“…The critical time t c is searched in the interval [ t end − ηdt , t end + ηdt ], with η is typically equal to 0.20. Previous calibrations of the LPPLS specification Eq (3) to the log-price development during a number of historical financial bubbles have suggested to qualify fits based on the parameters of the LPPLS model belonging to the following intervals [5, 35, 36]: m ∈ [0.1, 0.9], ω ∈ [6, 13], | C | ≤ 1, B < 0. In our explorations, we have found that relaxing the search space to the larger intervals m ∈ [0, 2] and ω ∈ [1, 50] does not change the results significantly, particularly for the calibrated critical times within statistical fluctuations.…”

Section: Model and Calibrationsmentioning

confidence: 99%

“…The first one is S&P 500 2007, which was studied in [6, 8]. The second and third one are SSEC 2007 and SSEC 2009, discussed in details in [35]. For each bubble, we picked one value of t end , spanning from one to three months before the crash that terminated the bubble at T c , as given in Table 1.…”

Section: Applications To the Prediction Of The End Of Four Historicalmentioning

confidence: 99%

“…While informative, the effective use of so many fluctuating and often conflicting signals to inform on the danger for a bubble burst and to trigger an actionable decision remains a challenge. To address this, we propose two indicators that aggregate these signals, inspired from previous works on historic bubbles [6, 35, 38] via the implementation of pattern recognition of LPPLS structures and filtering, as suggested in Fig 5. These two indicators have been briefly discussed to present the ex-ante forecast of the Chinese bubble and its burst that started in June 2015 [39].…”

Section: Consolidated Ds Lppls™ Indicatorsmentioning

confidence: 99%

“…[43] for the analysis of the 2008 bubble) shown in Fig 22 and to the SSEC Chinese index shown in Fig 23 (see Ref. [35] for an early account).…”

Section: Empirical Analysis Of 16 Historical Bubbles With the Consolimentioning

confidence: 99%

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

Zhang¹,

Zhang²,

Sornette³

2016

PLoS ONE

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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™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the “S&P 500 1987” bubble and presenting analyses of 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.

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Section: Methodology Metrics and Tests On “Sandp 500 1987” Bubblementioning

confidence: 99%

Section: Model and Calibrationsmentioning

confidence: 99%

Section: Applications To the Prediction Of The End Of Four Historicalmentioning

confidence: 99%

Section: Consolidated Ds Lppls™ Indicatorsmentioning

confidence: 99%

“…[43] for the analysis of the 2008 bubble) shown in Fig 22 and to the SSEC Chinese index shown in Fig 23 (see Ref. [35] for an early account).…”

Section: Empirical Analysis Of 16 Historical Bubbles With the Consolimentioning

confidence: 99%

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

Zhang¹,

Zhang²,

Sornette³

2016

PLoS ONE

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Diagnosis and prediction of tipping points in financial markets: Crashes and rebounds

2010

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By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law (LPPL) model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. The power of the LPPL model is illustrated by two recent real-life predictions performed recently by our group: the peak of the Oil price bubble in early July 2008 and the burst of a bubble on the Shanghai stock market in early August 2009. We then present the concept of "negative bubbles", which are the mirror images of positive bubbles. We argue that similar positive feedbacks are at work to fuel these accelerated downward price spirals. We adapt the LPPL model to these negative bubbles and implement a pattern recognition method to predict the end times of the negative bubbles, which are characterized by rebounds (the mirror images of crashes associated with the standard positive bubbles). The out-of-sample tests quantified by error diagrams demonstrate the high significance of the prediction performance.

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Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?

Deng

Girardin

Joyeux

et al. 2017

Pacific Economic Review

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The speculative nature of both stock and housing markets in China has attracted the attention of observers. However, while stock market data are easily available, the low frequency and low quality of publicly available housing price data hampers the study of the relationship between the two markets. We use original hedonic weekly resale housing prices of a major Chinese housing market and study them in conjunction with Shanghai's stock market index in the second half of the 2000s. The use of the Phillips et al. (2015 a,b) recursive explosive‐root test enables us to detect and date speculative episodes in both markets. We then implement the Greenaway‐McGrevy and Phillips (2016) methodology to detect the presence of migration between the two types of bubbles. We detect significant migration from the stock to the housing market bubble in 2009 and a temporary spillover in 2007.

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Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles

Cited by 18 publications

References 43 publications

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

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

Diagnosis and prediction of tipping points in financial markets: Crashes and rebounds

Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?

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