Early warning indicator for financial crashes using the log periodic power law

Kurz‐Kim, Jeong‐Ryeol

doi:10.1080/13504851.2011.633885

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…As the critical time approaches, both the gradient of exponential growth and the frequency of log-periodic oscillation tend to increase. Therefore, the simultaneous increase of these two indicates an imminent market crash [51]. Figure 2a implies that the gradient of exponential growth and the frequency of log-periodic oscillation are increasing.…”

Section: Risk Of Financial Crashmentioning

confidence: 98%

“…Figure 2a implies that the gradient of exponential growth and the frequency of log-periodic oscillation are increasing. Furthermore, using the German stock index, Kurz-Kim [51] provided empirical evidence of the imminence of critical time, in which the gradient of exponential growth increases from 0.27%, to 0.67%, to 1.70%, to 4.29%, to 12.9%, while the period of log-periodic oscillation decreases by 60% in each interval from 255, to 102, to 41, to 16, to 7 trading days. Our analysis notes that the exponential growth rate is greater than 0.27%, i.e., 0.1% and 0.59% at the second and third peaks, respectively, and is about to enter the second stage of the example in Reference [51].…”

Section: Risk Of Financial Crashmentioning

confidence: 99%

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Real Estate Soars and Financial Crises: Recent Stories

et al. 2018

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This paper studies the contribution of real estate bubble to a financial crisis. First, we document symptoms of a real estate bubble along with a slowdown of the real economy and find indicators of an imminent crash of the stock market, triggering a sense of déjà vu from the 2008 crisis. However, we show that the relationship between real estate and financial markets has changed since the crisis. The empirical analyses provide evidence that the monetary policy has recovered its control over mortgage rates, which had been lost prior to the global financial crisis, and that the real estate market does not have a Granger causality relationship with the stock market any more. Findings suggest that an imminent financial market crash is not likely to be catalyzed by a real estate bubble.

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Section: Risk Of Financial Crashmentioning

confidence: 98%

Section: Risk Of Financial Crashmentioning

confidence: 99%

Real Estate Soars and Financial Crises: Recent Stories

et al. 2018

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“…A brief review of its recent developments ensues. Kurz-Kim [25] applied the LPPLS model to capture the crash in German stock index. Geraskin and Fantazzini [26] reviewed the LPPLS model and employed three different calibration methods to fit the model.…”

Section: Introductionmentioning

confidence: 99%

The 2021 Bitcoin Bubbles and Crashes—Detection and Classification

Shu

Song

Zhu

2021

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In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.

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“…Moreover, even using such a model, the prediction of the critical time is not very accurate, Kurz-Kim (2012) shows that LPPL models could be used as a early warning mechanism of regime switching in the case of a stock market.…”

Section: Lppl Fit For Stock Market Bubblesmentioning

confidence: 99%