Negative Bubbles Research Articles

When Chinese mania meets global frenzy: Commodity price bubbles

This paper examines price bubbles in global commodity markets. We find that positive bubbles are more driven by fundamental shocks, while negative bubbles are more influenced by pessimistic market views on prices and the economy. Furthermore, bubble determinants vary across geographic regions. Trader behavior and policy uncertainty play prominent roles in influencing price bubbles in China, while global bubbles are predominantly shaped by rational responses to inventory, growth, and inflation. Finally, only positive bubbles exhibit contagion across regions. Overall, our findings suggest that asset price bubbles arise from traders' behavioral responses to a combination of fundamental, macroeconomic, and idiosyncratic shocks.

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.

Is the Metaverse Dead? Insights from Financial Bubble Analysis

This paper explores the economic trends and identifies speculative bubbles within the emerging metaverse, based on the specific example of Decentraland, which is represented by its underlying native token MANA.For comparability, we consider three further tokens: SAND, ETH, and BTC.The study shows price prediction and provides further insight into bubble behavior to provide a deeper insight into the real trend and situation of the metaverse. When comparing all considered tokens, evidence of comovement and positive as well as negative bubbles is identified. This paper makes use of proven modeling techniques, such as SARIMA, for price prediction and LPPLS for financial bubble identification, visualization, and time stamping.

주택매매시장에서의 거품에 관한 실증연구

This study examines the monthly real house price index movements over the period 1986-2023 using the GSADF method to determine whether bubbles existed in the Korean housing market, where they occurred, when they occurred, and how long they lasted by housing type (total, apartment, detached house, and townhouse). We summarise the main findings from our empirical analysis as follows. First, there were multiple bubbles in housing prices across the nationwide, Seoul, and all provincial metropolitan areas. However, there are many differences in how bubbles occurred across regions and types of housing. Second, the post-bubble period lasted much longer in the detached house market than in the apartment and row house markets. Third, many housing market bubbles occurred during periods of abundant liquidity. Fourth, while most bubbles are positive, appearing during periods of rapid house price appreciation, negative bubbles appeared in some areas (Busan, Gwangju, and Ulsan) during the 1998 Asian financial crisis. Fifth, the detached house markets in Busan and Ulsan are found to be bubbling at the end of the sample (December 2023). These results suggest that the GSADF test can not only detect historical bubble periods, but can also detect bubbles in real time, providing early warning of bubbles.

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.

Asset bubbles and wealth inequality

AbstractWe examine the causal link between asset bubbles and wealth inequality in a two‐agent macroeconomic model. Bubbles influence wealth inequality through two channels: altering the debt–asset ratio and fuelling speculation. When bubbles grow, they can temporarily decrease wealth inequality if asset prices rise faster than debt. However, when they burst, wealth inequality increases as the debt–asset ratio rises. Steady‐state wealth inequality is unaffected by bubbles if household types share symmetric speculative timing. Although macroprudential policy, communication, and leaning against the wind can reduce negative bubble effects on aggregate utility, they have a limited effect on wealth inequality.

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.

Abstract 14878: Be Aware of False Negative Bubble Study in Diagnosis of Patent Foramen Ovale!

Introduction: Echocardiography plays an important role in diagnosis of Patent Foramen Ovale (PFO). However, there are some anatomic variations which may result in a false negative study. Case presentation: A 55-year-old male was hospitalized for acute right-hemiplegia. The EKG revealed normal sinus rhythm. Brain imaging revealed acute bilateral infarcts. The transthoracic echocardiogram (TTE) showed a normal ejection fraction without significant valve disease and a negative agitated saline Bubble study. A Trans-esophageal echocardiogram (TEE) revealed a negative Bubble study using intravenous access via the left arm. The TEE also revealed an atrial septal aneurysm and large eustachian valve (EV). The Bubble study was then repeated with agitated saline injected from lower extremity. This demonstrated a significant right to left shunt through a PFO. Discussion: EV is a remnant of the valve of the inferior vena cava that directs blood flow through the fossa ovalis during embryonic life. A large or prominent EV may be a risk factor for paradoxical embolism by preventing spontaneous foramen closure. Agitated saline originating from the superior vena cava (SVC) may result in a false-negative study due to the prominent EV preventing microbubbles from the SVC from crossing the interatrial septum. In this case, the patient’s age, clinical presentation and the finding of EV and atrial septal aneurysm raised the suspicion of PFO. Repeating the bubble study using a lower extremity peripheral access was positive. This step played a crucial role in the diagnosis of PFO in this patient and his subsequent management. Conclusion: Cardiologists should be aware that the presence of a prominent EV may result in a false negative bubble study result when the injection is performed via upper limb intravenous access.

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.

COVID caused a negative bubble. Who profited? Who lost? How stock markets changed?

Compiling a unique, worldwide collection of trading data, we analyze investor types' aggregate trading in stock markets throughout the COVID-19 episode, to assess investor types' role in a worldwide negative bubble and their degree of sophistication in responding to it. Individual investors were the main buyers and consequently the winners during the rebound. Foreign institutional investors exited host markets; some domestic institutions exploited the negative bubble by well-timed buying. In US index futures, asset managers heavily sold into crash; dealers profited from the rebound. Individual investors' buying was driven by their contrarian behavioral traits and a unique positive shock to retail investor demand for self-serviced investing in stocks, driven by work-from-home practices and unprecedented stimulus. This shock has changed the participant composition of world stock markets. Overall, the COVID-19 episode has many unique aspects that cannot be accounted for under existing theories.

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.

Monetary policy and bubbles in G7 economies using a panel VAR approach: Implications for sustainable development

We use the LPPLS Multi-Scale Confidence Indicator approach to detect both positive and negative bubbles in the short-, medium- and long-run for the stock markets of the G7 countries. We were able to detect major crashes and rallies in the seven stock markets over the monthly period of 1973:02 to 2020:09. We also observed similar timing of strong (positive and negative) LPPLS indicator values across the G7 countries, suggesting synchronized extreme movements in these stock markets. Given this, to obtain an overall picture of the G7, we used a panel VAR model to analyze the impact of monetary policy shocks on the six indicators of bubbles. We found that monetary policy not only impacts the bubble indicators but also responds to them, with the nature of the underlying responses contingent on whether bubbles are positive or negative in nature, as well as the time-scale we are analyzing. In light of these findings, our results have serious implications for monetary authorities of these advanced markets in terms of sustainable development, given the finance-growth nexus. But in general, we can conclude that central banks of the G7 can indeed “lean against the wind”, and they have also been doing so under both conventional and unconventional monetary policy periods.

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.

GSADF 검정 및 Probit 모형을 이용한 시도별 주택가격거품에 대한 연구

This study estimated the date and duration of the bubble and analyzed what factors were important during bubble episodes in apartment market in Korea from October 2003 to January 2021. Main results were as follows. First, more than two bubble episode appeared in all regions and the negative bubble episode appeared more than once in all regions. It was found that the negative bubble episode generally lasted shorter than that of the positive bubble episode. Second, as a result of the marginal effect analysis it was found that housing sales prices generally increased the probability of bubble and household loan growth and interest rates lowered the probability of bubble. These results differed by regions, with housing sales prices having a greater impact on the probability of bubble occurrence in the non-metropolitan, and household loan growth having a greater impact on the metropolitan.

Behavioral Framework of Asset Price Bubbles: Theoretical and Empirical Analyses

Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated investors, and we assessed the time series characteristics of asset prices under different types of information shocks. According to the research results, good news about the fundamentals can lead to positive asset price bubbles, and correspondingly, bad news can lead to negative asset price bubbles; however, the decrease in asset prices in the case of negative bubbles is not as substantial as the increase in prices in the case of positive bubbles, and the time for prices to reverse is also long, which can be explained by the short-selling constraints. According to the comparative static analysis, the scales of the positive and negative foams depend on the proportion of investors in the market and the extrapolation coefficient. We verified the conclusion of the theoretical model from two aspects: (1) we analyzed the relationship between investor sentiment and the prevalence of informed trading, and according to the results, the increase (decrease) in investor sentiment can reduce the information content of asset prices and increase price volatility; however, the impact of low sentiment is not substantial, which preliminarily tests the conclusion of the theoretical model; (2) we examined the relationship between the cumulative change in investor sentiment and future portfolio returns, and we found that the cumulative increase in investor sentiment can have a positive impact on future portfolio returns at the initial stage, and depress future portfolio returns in the long term, which forms positive asset price bubbles. The cumulative depression of investor sentiment can depress the future portfolio returns at the initial stage, and positively influence the future portfolio returns in the long term, which forms negative asset price bubbles. Moreover, these two nonlinear relationships exhibit cross-sectional differences in different types of asset portfolios, which further validates the key proposition of the theoretical model.

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.

A PRACTICAL ALGORITHM TO DETECT SUPEREXPONENTIAL BEHAVIOR IN FINANCIAL ASSET PRICE RETURNS

To assist with the detection of bubbles and negative bubbles in financial markets, a criterion is introduced to indicate whether a market is likely to be in a superexponential regime (where growth in such a regime would correspond to an asset price bubble and decline to an negative bubble) as opposed to “normal” exponential behavior typified by a constant rate of growth or decline. The criterion is founded on the Johansen–Ledoit–Sornette model of asset dynamics in a bubble and is derived from a linear fit to observed data with a nonlinear time transformation with parameters distributed uniformly in their permitted ranges. Making use of expected values rather than the underlying distribution, the criterion is straightforward and efficient to compute and can in principle be applied in real time to intra-day markets as well as longer timescales. In some circumstances, the criterion is shown to have certain predictive qualities when applied to a portfolio of stocks, and could be used as input into algorithmic trading strategies. A simple strategy is described which is based on market reversion predictions of a portfolio of stocks and which in back-testing generates notable returns.

Bubble testing under polynomial trends

Summary This paper develops the asymptotic theory of the least squares estimator of the autoregressive (AR) coefficient in an AR(1) regression with intercept when data is generated from a polynomial trend model in different forms. It is shown that the commonly used right-tailed unit root tests tend to favour the explosive alternative. A new procedure, which implements the right-tailed unit root tests in an AR(2) regression, is proposed. It is shown that, when the data generating process has a polynomial trend, the test statistics based on the new procedure cannot find evidence of explosiveness. Whereas, when the data generating process is mildly explosive, the new procedure finds evidence of explosiveness. Hence, it enables robust bubble testing under polynomial trends. Empirical application of the proposed procedure using data from the US real estate market reveals some interesting findings. In particular, all the negative bubble episodes flagged by the traditional method are no longer regarded as bubbles by the new procedure.

Bubble Test and Carotid Ultrasound to Guide Indication of Transesophageal Echocardiography in Young Patients With Stroke

Background and PurposeIndication of transesophageal echocardiography (TEE) in patients ≤60 years with brain ischemia is uncertain.MethodsThis prospective double-blinded study included patients with cryptogenic acute ischemic stroke or transient ischemic attack (TIA) ≥18 and ≤60 years. After routine diagnostics, all patients underwent patent foramen ovale (PFO) screening by transcranial Doppler (TCD) bubble test, carotid ultrasound for atherosclerosis screening (intima-media-thickness >0.90 mm or plaques), and TEE. We calculated sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the combined non-invasive ultrasound to predict therapy-relevant TEE findings.ResultsWe included 240 consecutive patients (median 51 years, 39% women) of which 68 (28.3%) had both a negative bubble test and no carotid atherosclerosis. Of these, 66 (97.1%) had unremarkable TEE findings; in one patient a small PFO was found and closed subsequently, in another patient a 4.9 mm thick aortic atheroma was found, and double platelet inhibition initiated. Of the other 172 (71.7%) patients, 93 (54%) had PFO and 9 (5.2%) complex aortic plaques. No other therapy-relevant findings were present in both groups. Non-invasive ultrasound had a sensitivity of 98.0%, specificity of 47.8%, NPV of 97.1%, and PPV of 58.1% for therapy-relevant TEE findings.ConclusionsBubble test and carotid ultrasound could be used for the individual decision for/against TEE in patients with cryptogenic stroke ≤60 years. If they are unremarkable, TEE can be omitted with high safety regarding secondary prevention. If bubble test is positive and/or carotid ultrasound shows atherosclerosis, TEE should be carried out if PFO or aortic atheroma are potentially relevant for further patient management.