Log-periodic Power Law Singularity Research Articles

Monetary policy shocks and multi-scale positive and negative bubbles in an emerging country: the case of India

We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify positive and negative bubbles in the short-, medium, and long-term for the Indian stock market, using weekly data from November 2003 to December 2020. We use a nonparametric causality-in-quantiles approach to analyze the predictive impact of monetary policy shocks on bubble indicators. We find, in general, strong evidence of predictability across the entire conditional distribution for the two monetary policy shock factors, with stronger impacts for negative bubbles. Our findings have critical implications for the Reserve Bank of India, academics, and investors.

Stock market volatility and multi-scale positive and negative bubbles

We study whether booms and busts in the stock market of the United States (US) drives its volatility. Given this, first, we employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. We successfully detect major crashes and rallies during the weekly period from January 1973 to December 2020. Second, we utilize a nonparametric causality-in-quantiles approach to analyze the predictive impact of our bubble indicators on daily data-based weekly realized volatility (RV). This econometric framework allows us to circumvent potential misspecification due to nonlinearity and instability, rendering the results of weak causal influence derived from a linear framework invalid. The MS-LPPLS-CIs reveal strong evidence of predictability for RV over its entire conditional distribution. We observe relatively stronger impacts for the positive bubbles indicators, with our findings being robust to an alternative metric of volatility, namely squared returns, and weekly realized volatilities derived from 5 (RV5)- and 10 (RV10)-minutes interval intraday data. Furthermore, we detect evidence of predictability for RV5 and RV10 of nine other developed and emerging stock markets. In addition, we also find strong evidence of causal feedbacks from RV5 and RV10 on to the MS-LPPLS-CIs of the 10 countries considered. Finally, time-varying connectedness of the RVs of the G7 stock markets is also shown to be strongly (positively) predicted by the connectedness of the six bubbles indicators. Our findings have significant implications for investors and policymakers.

Detecting market bubbles: A generalized LPPLS neural network model

To enhance bubble detection capabilities, we introduce two significant improvements to the Log-Periodic Power Law Singularity (LPPLS) model: (1) a novel fitting approach, which yields more accurate predictions of critical price distributions within a single sample window; (2) a restructured neural network approach further enhances the estimations of the probability distributions of the critical points across both time and price dimensions, and it can be fine-tuned with real-world data. The simulation and practical applications to typical asset price bubbles in cryptocurrencies, commodities, and equity indices demonstrate that our refined model, the Generalized-LPPLS Neural Network (G-LPPLS-NN), outperforms all other models we examined in terms of predictive accuracy for critical point distributions.

Identifying price bubbles in global carbon markets: Evidence from the SADF test, GSADF test and LPPLS method

Amid growing climate concerns, Emissions Trading Schemes (ETS) serve as key mechanisms for reducing greenhouse gas emissions through market forces. This study delves into the phenomenon of price bubbles within six major ETSs globally: the European Union Emissions Trading Scheme (EU ETS), the New Zealand Emissions Trading Scheme (NZ ETS), the California-Québec Emissions Trading Scheme (CQ ETS), the South Korea Emissions Trading Schemes (K-ETS), the China Emissions Trading Scheme (C-ETS), and the United Kingdom Emissions Trading Schemes (UK ETS). Employing the sup augmented Dickey-Fuller (SADF) test, generalized sup ADF (GSADF) test, and log-periodic power law singularity (LPPLS) model, our analysis aims to uncover the dynamics and implications of these bubbles. Our findings reveal varied patterns of price bubbles across the schemes, with the EU ETS showing the highest frequency and the NZ ETS exhibiting the longest durations. The period of 2021 and 2022 marked a significant increase in bubble occurrences across all ETSs, suggesting the influence of post-COVID-19 economic recovery and geopolitical tensions on market stability. These bubbles are attributed to factors including carbon market policies, macroeconomic conditions, energy price fluctuations, and market uncertainties. The study offers valuable insights for compliance firms, policymakers, and investors on navigating carbon market volatility. Understanding bubble dynamics can aid in formulating more effective emission reduction strategies, carbon pricing policies, and investment decisions. This research underscores the importance of integrating monetary policy considerations with environmental objectives within ETS frameworks, paving the way for future research on predictive bubble detection and the development of robust carbon market regulations.

Does every cloud (bubble) have a silver lining? An investigation of ESG financial markets

This paper investigates the presence of financial bubbles in the environmentally friendly investments captured by the ESG markets. By using the log-periodic power law singularity framework, we identified several periods of positive and negative bubbles in the short, medium, and long term. Moreover, we examined the relationship between ESG attention sentiment and financial bubbles. We found an asymmetric effect of ESG sentiment on financial bubbles, i.e., increasing positive and decreasing negative bubbles. Our empirical results provide valuable insights into the stability of environmentally friendly markets, which help risk managers and policymakers respond appropriately to financial and social bubbles.

Monetary policy uncertainty and the price bubbles in energy markets

We examine the relationship between the monetary policy uncertainty (MPU) and the price bubbles in U.S. oil futures, including WTI crude oil future, heating oil future, and gasoline future. The Log Periodic Power Law Singularity (LPPLS) model is firstly used to analyze and validate the price bubbles of U.S. oil futures. We find that (1) the timing of local peaks of MPU closely aligns with the occurrence of price bubbles in U.S. oil futures, (2) MPU is significantly correlated with crude oil future price bubble but not with heating oil future (or gasoline future) price bubble, and MPU significantly amplify the magnitude of price bubble risk for crude oil future, (3) MPU is an effective set of factors for machine-learning based identification of all three U.S. oil future price bubbles in the non-linear model, even though the impact of MPU on heating oil (or gasoline) future price bubble is not significant in logit regression as the linear model. Collectively, our findings highlight the significance of MPU in relation to price bubbles in U.S. oil futures, offering new insights for investors and policymakers to explore the (non)-linear relationship between MPU and price bubbles in energy markets.

Detection of financial bubbles using a log‐periodic power law singularity (LPPLS) model

AbstractThis article provides a systematic review of the theoretical and empirical academic literature on the development and extension of the log‐periodic power law singularity (LPPLS) model, which is also known as the Johansen–Ledoit–Sornette (JLS) model or log‐periodic power law (LPPL) model. Developed at the interface of financial economics, behavioral finance and statistical physics, the LPPLS model provides a flexible and quantitative framework for detecting financial bubbles and crashes by capturing two salient empirical characteristics of price trajectories in speculative bubble regimes: the faster‐than‐exponential growth of price leading to unsustainable growth ending with a finite crash‐time and the accelerating log‐periodic oscillations. We also demonstrate the LPPLS model by detecting the recent bubble status of the S&P 500 index between April 2020 and December 2022, during which the S&P 500 index reaches its all‐time peak at the end of 2021. We find that the strong corrections of the S&P 500 index starting from January 2022 stem from the increasingly systemic instability of the stock market itself, while the well‐known external shocks, such as the decades‐high inflation, aggressive monetary policy tightening by the Federal Reserve, and the impact of the Russia/Ukraine war, may serve as sparks.This article is categorized under: Applications of Computational Statistics > Computational Finance Algorithms and Computational Methods > Computational Complexity Statistical Models > Nonlinear Models

Stock market bubbles and the realized volatility of oil price returns

Using monthly data for the G7 countries from 1973 to 2020, we study whether stock market bubbles help to forecast out-of-sample the realized volatility of oil price returns. We use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. First, we successfully detect major crashes and rallies using the MS-LPPLS-CIs. Having established the relevance of the bubbles indicators, and given the large number of them, we use widely-studied shrinkage (Lasso, elastic net, ridge regression) approaches to estimate our forecasting models. We find that stock market bubbles have predictive value for realized volatility at a short to intermediate forecast horizon. The number of bubble predictors included in the penalized forecasting models tend to increase in the forecast horizon. We obtain our main finding for the various types of stock market bubbles, and for good and bad realized volatilities.

Extreme events, economic uncertainty and speculation on occurrences of price bubbles in crude oil futures

This paper examines the impacts of extreme events, economic uncertainty and speculation on price bubbles in crude oil futures. For better forecast and estimate the positive/negative price bubbles of crude oil futures, the log-periodic power law singularity confidence multi-scale indicators application is used. The panel probit model examines impacts of extreme events, economic uncertainty and speculation on price bubbles in crude oil. The results show that the recent extreme events including Corona Virus Disease 2019 and Russia-Ukraine war significantly affect both positive/negative bubbles in crude oil futures. Measures of economic uncertainty significantly affect both positive/negative bubbles in the crude oil. However, the results find that the speculation significantly affect the positive bubbles and does not significantly affect the negative bubbles. Overall, extreme events, economic uncertainty, and speculation can all contribute to the occurrences of price bubbles in crude oil futures markets. This study provides new insights for the policy makers and market participants to monitor the occurrence of price bubbles.

Detection of financial bubbles using a log-periodic power law singularity (LPPLS) model

Detection of financial bubbles using a log-periodic power law singularity (LPPLS) model

Why topological data analysis detects financial bubbles?

We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price.

A finite-time singularity in the dynamics of the US equity market: Will the US equity market eventually collapse?

Fitting Dow Jones 30 index data for the 1790–1999 period into a log-periodic power-law singularity (LPPLS) model, the seminal paper by Johansen and Sornette (2001) was the first to show that the US equity growth rate is accelerating such that the market is growing as a power law toward a spontaneous singularity. Their model suggests that the US equity market will reach this critical point in the year 2052 ± 10 years, signaling an abrupt transition to a new regime. This study re-examines this important issue using (i) a novel approach to calibrate the LPPLS model and (ii) a different data set including >20 years of additional data. The extended data account for the dot.com bubble burst (2000), the Global Financial Crisis period (2008–2009), the COVID−19 crisis (2020−2022), and the ongoing Russian–Ukrainian war (starting in 2022), which are all events with severe consequences for the global economy. The calibrated LPPLS model suggests that the US equity market will reach a singularity condition by June 2050.

Positive and negative price bubbles of Chinese agricultural commodity futures

This study examines the predictability of positive and negative price bubbles of the Chinese agricultural commodity futures with the log-periodic power law singularity (LPPLS) confidence multi-scale indicators application. To measure the bubble risks of these commodity futures, the duration, the frequency and the magnitude of the price bubble are adopted as the evaluation indicators. The results found that the Chinese agricultural commodity futures with higher bubble risks are soybean, soybean oil, soybean meal, white sugar, cotton and corn futures. Furthermore, the price bubbles of Chinese agricultural commodity futures are affected by the investor sentiment and the overall degree of macroeconomic factors. Economic growth, money supply, and inflation have positive effects on the occurrences of the positive bubbles, while currency appreciation and interest rate have negative effects and vice versa on the occurrences of the negative bubbles. Financialization has positive effects on both occurrences of the positive and negative bubbles. Overall, this study provides new insights for the policy makers and market participants to monitor the occurrence of price bubbles.

Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries

Firstly, we use the log-periodic power law singularity multi-scale confidence indicator (LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock market indices of the G7 countries. Secondly, we apply heterogeneous coefficients panel data-based regressions to analyse the impact of investor sentiment, proxied by business and consumer confidence indicators, on the indicators of bubbles of the G7. Controlling for the impacts of output growth, inflation, monetary policy, stock market volatility, and growth in trading volumes, we find that investor sentiment increases the positive and reduces the negative LPPLS-CIs, primarily at the medium- and long-term scales for the G7, considered together, with the result being driven by at least five of the seven countries. Our results have important implications for both investors and policymakers, as the collapse (improvement) of investor sentiment can lead to a crash (recovery) in a bull (bear) market.

US monetary policy and BRICS stock market bubbles

We use the multi-scale Log-Periodic Power Law Singularity (LPPLS) confidence indicator approach to detect both positive and negative bubbles at short-, medium- and long-run for the stock markets of the BRICS countries. Then, we utilize impulse responses obtained from the local projection method (LPM) framework to capture the effect of US monetary policy shocks on the BRICS bloc equity market. The effect of these shocks on the bubble indicators for each country is limited, with a strong positive impact observed under the medium-term negative bubble indicator of Brazil, China and South Africa. Given the findings, associated policy implications are discussed.

Dissecting the 2015 Chinese stock market crash

AbstractWe perform a novel analysis of the 2015 Chinese stock market crash by calibrating the log‐periodic power law singularity (LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early 2014 to June 2015. Our analysis indicates that the LPPLS model can readily detect the bubble behaviour of the faster‐than‐exponential increase corrected by the accelerating logarithm‐periodic oscillations in the crash. The existence of the log‐periodicity is identified by applying the Lomb spectral analysis on the detrended residuals. The Ornstein–Uhlenbeck property and the stationarity of the LPPLS fitting residuals are confirmed by the Phillips–Perron test and the Dickey–Fuller test. We find that the actual critical day of bubble crash can be well predicted by the LPPLS model as far back as 2 months before the actual crash. We have shown that the covariance matrix adaptation evolution strategy (CMA‐ES) can be used as an alternative optimization algorithm for the LPPLS model fit. Furthermore, the change rate of the prediction end time gap ( ) can be used as an additional indicator along with the key indicator to improve the prediction of bubble burst.

The 2021 Bitcoin Bubbles and Crashes—Detection and Classification

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.

Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach

This paper provides an analysis of crude oil, diesel, and gasoline prices for the period from November 1, 2019 to December 31, 2020. We apply Log Periodic Power-Law Singularity (LPPLS) and Discrete Scale LPPLS bubble indicators to explore the dynamic bubbles of oil prices and predict their crash times. The results indicate that West Texas Light crude oil and North Sea Brent crude oil experienced a statistically significant negative financial bubble during the COVID-19 outbreak. In addition, gasoline and diesel prices are mainly driven by fundamentals. Our findings are expected to be useful to oil market investors, policymakers, and energy experts.

The 2020 global stock market crash: Endogenous or exogenous?

Starting on February 20, 2020, the global stock markets began to suffer the worst decline since the Great Recession in 2008, and the COVID-19 has been widely blamed on the stock market crashes. In this study, we applied the log-periodic power law singularity (LPPLS) methodology based on multilevel time series to unravel the underlying mechanisms of the 2020 global stock market crash by analyzing the trajectories of 10 major world stock market indexes from both developed and emergent stock markets, including the S&P 500, the DJIA, and the NASDAQ from the United State, the FTSE from the United Kingdom, the DAX from Germany, the NIKKEI from Japan, the CSI 300 from China, the HSI from Hong Kong, the BSESN from India, and the BOVESPA from Brazil. In order to effectively distinguish between endogenous crash and exogenous crash in stock market, we proposed using the LPPLS confidence indicator as a classification proxy. The results show that the apparent LPPLS bubble patterns of the super-exponential increase, corrected by the accelerating logarithm-periodic oscillations, have indeed presented in the price trajectories of the seven indexes: S&P 500, DJIA, NASDAQ, DAX, CSI 300, BSESN, and BOVESPA, indicating that the large positive bubbles have formed endogenously prior to the 2020 stock market crash, and the subsequent crashes for the seven indexes are endogenous, stemming from the increasingly systemic instability of the stock markets inherently, while the well-known external shocks, such as the COVID-19 pandemic, the corporate debt bubble, and the 2020 Russia–Saudi Arabia oil price war, only served as sparks during the 2020 global stock market crash. In contrast, the crashes in the three remaining indexes: FTSE, NIKKEI, and HSI, are exogenous and hence are perhaps the only crashes truly due to the COVID-19 pandemic. We also found that in terms of the regime changes of the stock markets, no obvious LPPLS negative bubble pattern has been observed in the price trajectories of the 10 stock market indexes, indicating that the regime changes from a bear market to a bull market in late March 2020 are exogenous, stemming from external factors. The unprecedented market and economy rescue efforts from federal reserves and central banks across the world in unison may have played a critical role in quelling the 2020 global stock market crash in the nick of time. This paper creates a paradigm for future studies in real-time crash detection and underlying mechanism dissection. It serves to warn us of the imminent risks in not only the stock market but also other financial markets and economic indexes.

Revisiting the Predictability of the Haicheng and Tangshan Earthquakes

We analyze a set of precursory data measured before but compiled in retrospect of the MS7.5 Haicheng earthquake in February 1975 and the MS7.6–7.8 Tangshan earthquake in July 1976. We propose a robust and simple coarse-graining method that aggregates and counts how all the anomalies together (levelling, geomagnetism, soil resistivity, earth currents, gravity, earth stress, well water radon, well water level) develop as a function of time. We demonstrate strong evidence for the existence of an acceleration of the number of anomalies leading up to the major Haicheng and Tangshan earthquakes. In particular for the Tangshan earthquake, the frequency of occurrence of anomalies is found to be well described by the log-periodic power law singularity (LPPLS) model, previously proposed for the prediction of engineering failures and later adapted to the prediction of financial crashes. Using a mock real-time prediction experiment and simulation study, based on this methodology of monitoring accelerated rates of physical anomalies measured at the surface, we show the potential for an early warning system with a lead time of a few days.