Price Bubbles Research Articles

Electricity Price Bubbles and Global Crisis: Does Financial Development Make a Difference?

The aim of this study is to investigate the existence of electricity price bubbles and identify the factors that contribute to their formation by conducting a comparative analysis between a developed country, Germany, and an emerging country, Romania. We employ the generalized sup ADF test, utilizing bi-annual data spanning from 2007 to 2022. Our findings reveal the presence of 2 distinct electricity price bubbles in both Germany and Romania. In Germany, the first bubble emerged in the second half of 2008, coinciding with the onset of the global economic crisis. Conversely, Romania did not experience a bubble as a result of the economic crisis during this period. The second electricity price bubble in both countries was primarily driven by Russia’s decision to halt gas supplies to Europe. Additionally, the surge in energy consumption following the economic recovery from the COVID-19 pandemic played a significant role. This research contributes to the broader discourse on energy economics by elucidating the mechanisms behind electricity price fluctuations in different economic contexts. The comparative approach underscores the importance of tailored policy responses that consider country-specific factors while addressing global challenges. As energy markets continue to evolve, such studies will be instrumental in guiding strategic decisions and fostering sustainable economic growth. JEL Classification: Q41, E31

The Current Situation of the Housing Market Bubble in Chinas First-tier Cities

Abstract: With the progression of China's economy and urbanization, the real estate industry experienced rapid growth, accompanied by increasing speculative activities, which led to a continuous rise in housing prices and the formation of a bubble. Therefore, the focus of this study was the current state of China's real estate bubble and potential solutions. Given the issue of excessively high real estate prices, this study specifically examined the price trends and factors contributing to the real estate bubble in four Chinese cities: Beijing, Shanghai, Guangzhou, and Shenzhen. The research methodology was as follows: First, data on the price-to-income ratio was collected to analyze the current state of housing prices in China, revealing that housing prices were still far above their actual value and beyond a reasonable range, thereby indicating the severity of the real estate bubble in China. Second, by analyzing data and reviewing the literature, the factors contributing to China's real estate bubble were identified and categorized into two main groups: policy-related and economic. On the policy side, factors included policy reforms, monetary easing, and reductions in both the benchmark interest rate and the reserve requirement ratio. On the economic side, factors included the growth in per capita disposable income and speculative behavior. Finally, this paper analyzed how a property tax could curb speculative activities by increasing the cost of purchasing properties, thereby reducing demand and speculative behavior. The findings revealed that the real estate bubble in China's first-tier cities remained severe, and that a property tax could help curb speculative activities and slow the pace of bubble expansion.

Eros, Inc.: Cupid, Capital, and the Crash of 1720

Abstract In Regency Paris, the triumphant introduction into the economy of mass printed paper currencies coincided with images of equally victorious winged amours (cupids): serialised embodiments, in both cases, of the ascendancy of the flitting and fluttering. Examining works by Charles Coypel, Bernard Picart and others, this essay contextualises the reprisal of the omnia vincit amor theme—the idea of love’s inescapable power—alongside the emergence of an equally indomitable financial culture. Amidst the bubble economy engineered by John Law, Amour served as incarnation of more than the salvific and emancipatory promise of a paper economy. Hailed for his ‘empire of love’, the tyrannical god also asserted the blend of seduction and subjugation practiced by a despotic state that demanded acquiescence: both to its banknotes, and to the purportedly ‘soft power’ exercised by the government-licensed Company of the Indies over French Louisiana’s Indigenous population.

Detecting bubbles via FDR and FNR based on calibrated p-values

Detecting bubbles in asset prices is still an open question that has attracted considerable attention in recent years. This paper improves the bubble detection and dating approaches developed in recent years by Phillips and co-authors, proposing to assess the plausibility of its outcomes via the false discovery rate (FDR) and the false non-discovery rate (FNR) based on calibrated p-values. Calibrating p-values of unit root tests, applied sequentially to detect bubbles, allows recovery of their super-uniformity property, which is crucial for a valid implementation of the inferential procedure. The paper also develops original self-calibrated versions of both FDR and FNR for the specific problem of bubble testing. Calibrated p-values are implemented in an online false discovery-based approach which monitors bubbles in real time. The effectiveness of the proposed methods is investigated via a simulation study and an empirical application.

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.

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.

Japanese financial elites and banking supervision: the Ministry of Finance and the Bank of Japan

ABSTRACT Japan experienced a bubble economy in the late 1980s and a financial crisis in the 1990s, and the banking supervision system was criticized for failing to prevent excessive risk-taking behavior among commercial banks. In this study, I examine whether, and if so why, the banking supervision system deteriorated over the period in question, focusing on human capital, especially among the elites, in relation to banking supervision in Japan. I focus on two elements of banking supervision: the transition of supervisory policy and the characteristics (e.g. education, career path) of the heads of supervisory departments. The 49 Directors of the Financial Inspection Department in the MOF from 1927 to 1998 and the 26 Directors-General of the Bank Examination Department in the BOJ from 1928 to 1981 are analyzed. I also analyze the performance of several regional banks that appointed an ex-Director of the MOF as their president to check whether such an intimate bank – supervisor relationship worsened those banks’ management. The results showed no evidence of bad management as a result of that relationship. Rather, an increased burden on examiners and a reduced awareness of the importance of risk management led to the reduced effectiveness of banking supervision.

Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation

The Bitcoin, one of the most discussed event of recent times, is the first cryptocurrency to be released. Bitcoin has the biggest market capitalization in the cryptocurrencies, and it is a decentralized payment tool that cannot be controlled by any government institution. Cryptocurrencies operate on top of a decentralized network, recording and verifying transactions using a technology called Blockchain. Many bubble price formations occurred in financial markets throughout history. Cryptocurrency markets are prone to manipulation because they are not connected to any center. This study aims to to investigate the buble formations in Bitcoin (BTC) US Dollar price for the period of 11 December 2017 - 31 July 2023, and also to determine the main financial variables affecting Bitcoin prices. ARDL Bounds test proves cointegration between Bitcoin price and dependent variables. Error Correction Model determines positive connection between BTC and Nasdaq100 index. A daily change in Nasdaq100 index leads to a 0.58% increase in BTC price. Model captures also negative relationship between Volatility Index, USD Index and BTC price. No long-term relationship emerges in the results. In the long run, unique and different variables shape BTC price formation.

Government debt and stock bubbles in China

Herein, we used Vector Autoregressive and Dynamic Stochastic General Equilibrium models to examine the relation between central government debt and stock bubbles in China. The empirical findings indicate a considerable negative correlation between central government debt and stock bubbles. The model's results also illustrate that the liquidity substitution effect mainly drives a negative linkage. When commercial banks hold government debt, the effect establishes a connection between central government debt and asset bubbles through commercial banks' balance sheets. Further analysis indicates that countercyclical fiscal policies impact the negative correlation between government debt and stock bubbles. The findings suggest that governments should regulate debt levels to manage asset bubbles.

Prediction of Financial Bubbles and Back-Testing of a Trading Strategy

Prediction of Financial Bubbles and Back-Testing of a Trading Strategy

FINANCIAL CRISIS AND INSTABILITY

Financial crises and instability are common occurrences in the world economy, and they have significant effects on both developed and developing nations. The numerous causes of financial crises and the ensuing instability in the financial markets are thoroughly examined. It explains the theoretical foundations of financial crises, going over important ideas including market bubbles, excessive leverage, and regulatory shortcomings The paper also looks into how regulations and governmental actions can either exacerbate or lessen financial crises, how financial institutions contribute to systemic risk propagation and how interconnected they are. It looks at how the spread of crises across borders and asset classes can aggravate their severity through the contagion effect. It also covers the socio-economic fallout from financial crises, such as social unrest, income inequality, and unemployment. The summary concludes by highlighting how crucial it is to have strong regulatory control and proactive risk management to avert and lessen future financial disasters. It promotes a comprehensive strategy that blends international collaboration, regulatory changes, and responsible risk-taking to promote long-term economic growth and stability in a global financial system that is becoming more linked.

Investor Sentiment and Corporate Governance: The Role of Behavioral Biases in Stock Price Volatility and Managerial Decisions

Behavioural biases influence investor sentiment and behaviour, which in turn influence stock returns, stock price volatility and corporate governance structures. This paper focuses on the role of three common behavioural biases (overconfidence, herding and loss aversion) on investor behaviour, and how these biases drive inefficiencies in financial markets. Using worldwide market data from 1970 to 2019 and focusing on key events such as the 2008 financial crisis and the dot-com bubble, we investigate how these behavioural biases cause market volatility. This analysis further reinforces the importance of strong corporate governance structures such as independent boards, executive pay structures and risk management protocols that help reduce the impact of investor sentiment. The role of speculative bubbles, market overreaction and volatility clustering is also examined further, and corporate governance mechanisms are proposed to help mitigate the effects of behavioural biases on stock prices. The findings demonstrate the importance of adopting long-term corporate goals, transparency and strategies to help better manage market sentiment and safeguard shareholder value.

Corpus-based critical discourse analysis of NFT art within mainstream art-market discourse and implications for the political economy of digital art

The recent advent of non-fungible tokens (NFTs) has profoundly disrupted the contemporary art market, but their status remains contested. While advocates tout the democratising potential of NFTs, critics dismiss them as a speculative bubble. This study elucidates the position of NFT art within mainstream art market discourse through an interdisciplinary analysis synthesising critical discourse analysis, corpus linguistics, and the political economy of art. Examining three key 2023 art market reports furnishes empirical insights into how influential institutions frame and valorise NFT art. Linguistic patterns reveal NFTs’ increasingly solid yet variable positioning across art market segments. NFT art appears well-integrated into mainstream discourse but remains emergent at the high end. The analysis reveals a complex picture, with the data showing both positive institutional framing and valorisation of NFT art, as well as the identification of ethical tensions and concerns around issues of accessibility, diversity, and sustainability within the NFT art market discourse. We find NFT art simultaneously amplifies and challenges existing hierarchies, creating new opportunities counterposed by risks.

Price Bubble and Co-bubble in the Green Economy Market

In light of growing concerns about climate change and environmental issues, investor interest has surged in the new green economy market. However, the existing literature is limited regarding potential price bubble and co-bubble within this new domain. This study examines price bubble and co-bubble in the new green economy market, covering 31 indexes classified into three groups: the green economy market and its components, geographical regions, and sectors. Using daily data from August 31, 2005, to May 31, 2024, a test procedure is first applied to detect periods of price bubble in the various indexes, then logistic regressions are employed to examine price co-bubble behaviours. The results show evidence of price bubble in the green economy market, particularly in solar and wind indexes, with peaks during the COVID-19 pandemic and Russia-Ukraine conflict, whereas the water index is the least prone to price bubble. Regarding geographical region, the USA market exhibits a higher tendency for price bubble than the Asian or European markets. Several sectors are resistant to price bubble. The co-bubble analysis reveals a strong reliance of wind index on price bubble in the solar and water indexes. Price bubble in Asia significantly influences price bubble in Europe and the USA. These findings have implications for investment portfolio management and risk management strategies in the new green economy market.

BEHAVIOUR BIAS IN INVESTMENT DECISIONS: EMPIRICAL STUDY OF INVESTOR PSYCHOLOGY IN INDONESIA

Investing involves putting money into a variety of asset classes for a predetermined amount of time in the hopes of increasing total value and producing more income. The goals of investments and economics, such as value creation, profit generation, and other related goals, are strongly correlated. It is critical to recognize that investments may include additional financial, social, and spiritual goals. This research aims to assess investor behavior from internal and external perspectives concerning investing decisions. This study employs the Structural Equation Model (SEM) methodology utilizing the Smart-PLS software. This study's findings demonstrate that qualities including anger, anxiety, overconfidence, and self-monitoring positively influence investment decisions. Herding mitigates the adverse impacts of anger, anxiety, overconfidence, and self-monitoring on investment decisions. This research's consequences can assist investors in circumventing irrational risks and yield more objective findings grounded in thorough study. This tendency can result in inefficient market transactions, asset bubbles, and significant volatility. This study arose as a counter to the conventional understanding of exchange markets predicated on the premise of trader rationality. This research identifies that emotional emotions, risk perception, and cognitive biases frequently affect investment decisions by comprehending human nature.

Local powers of least‐squares‐based test for panel fractional Ornstein–Uhlenbeck process

In recent years, significant advancements have been made in the field of identifying financial asset price bubbles, particularly through the development of time‐series unit‐root tests featuring fractionally integrated errors and panel unit‐root tests. This study introduces an innovative approach for assessing the sign of the persistence parameter () within a panel fractional Ornstein‐Uhlenbeck process, based on the least squares estimator of . This method incorporates three distinct test statistics based on the Hurst parameter (), which can take values in the range of , be equal to , or fall within the interval of . The null hypothesis corresponds to . Based on a panel of continuous records of observations, the null asymptotic distributions are obtained when the time span () is fixed and the number of cross sections () goes to infinity. The power function of the tests is obtained under the local alternative where is close to zero in the order of . This alternative covers the departure from the unit root hypothesis from the explosive side, enabling the calculation of lower power in bubble tests. The hypothesis testing problem and the local power function are also considered when a panel of discrete‐sampled observations is available under a sequential limit, that is, the sampling interval shrinks to zero followed by the goes to infinity.

INFERENCE IN MILDLY EXPLOSIVE AUTOREGRESSIONS UNDER UNCONDITIONAL HETEROSKEDASTICITY

Mildly explosive autoregressions have been extensively employed in recent theoretical and applied econometric work to model the phenomenon of asset market bubbles. An important issue in this context concerns the construction of confidence intervals for the autoregressive parameter that represents the degree of explosiveness. Existing studies rely on intervals that are justified only under conditional homoskedasticity/heteroskedasticity. This paper studies the problem of constructing asymptotically valid confidence intervals in a mildly explosive autoregression where the innovations are allowed to be unconditionally heteroskedastic. The assumed variance process is general and can accommodate both deterministic and stochastic volatility specifications commonly adopted in the literature. Within this framework, we show that the standard heteroskedasticity- and autocorrelation-consistent estimate of the long-run variance converges in distribution to a nonstandard random variable that depends on nuisance parameters. Notwithstanding this result, the corresponding t-statistic is shown to still possess a standard normal limit distribution. To improve the quality of inference in small samples, we propose a dependent wild bootstrap-t procedure and establish its asymptotic validity under relatively weak conditions. Monte Carlo simulations demonstrate that our recommended approach performs favorably in finite samples relative to existing methods across a wide range of volatility specifications. Applications to international stock price indices and U.S. house prices illustrate the relevance of the advocated method in practice.

ANALYSIS OF BUBBLES ASSETS FOR WEIGHTED AVERAGE INTEREST RATES APPLIED TO BANK LOANS

This study examines the existence of the "bubble effect" in the average interest rates of loans applied by banks to various types of loans for consumers. The purpose of this article is the research of the bubble formations in the average interest rates applied by the banks to consumer, housing (home), vehicle, and commercial loan types. This article examines the existence of bubbles in the average loan interest rates of banks in Turkey using the GSADF unit root test developed by Phillips et al. (2015). Bubble assets are analyzed with weekly data for the period June 2019–March 2023. Monte Carlo simulations performed obtained critical values for the GSADF unit root test. The results show that the bubble effect on housing loan interest rates is statistically significant at the 0.05 level. Against this, although the existence of a bubble effect in consumer, vehicle, and commercial loan interest rates has been determined, these bubbles are statistically insignificant.

Detecting house price bubbles in G7 countries: New evidence and heterogeneous determinants

In recent years, the high growth of house prices in G7 countries has attracted some attention from all sectors due to the role of the real estate bubble in the global financial crisis of 2008. In this research, we analyzed the house price bubbles and their determinants in G7 countries. Firstly, we detected multiple house price bubbles in G7 countries between 1970 and 2022 and located when the house price bubbles emerged and burst. Second, we investigated the determinants of house price bubbles in each country and in the G7 countries as a whole. The results show that the level of house price bubbles and their determinants is significantly diversified over countries. However, total population, size of private debt, GDP per capita, and urbanization are among the common drivers of the whole G7 countries. Based on these results, we provide some suggestions on the critical points for a stable real estate market development.

Artificial intelligence as a core of the new industrial revolution: prospects and limitations

The purpose of the article is to define prospects and limitations of artificial intelligence as a core of in the new industrial revolution. The definition of the concept of AI in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unification of the concept of AI. Based on the analysis conducted and literary sources, the following prospects for AI can be identified on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) formation of new products and markets; (2) increasing labor and capital productivity; (3) massive creation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small businesses and startups. Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and, as a consequence, the possible formation of a speculative bubble in the global stock market; (3) energy consumption of AI; (4) outdated pre-AI corporate culture and regulatory environment. Further improvement of AI (including the transition from AI to AGI) and the expansion of its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy. AI, certainly acting as a locally useful tool at the level of individual enterprises and organizations, causes the acceleration of attracting funds to the stock market, which can lead to the formation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency of AI will be revised, and some AI-related companies will experience significant margin reductions (perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development, will accelerate its transition from the current narrow specialization to the creation of full-fledged general artificial intelligence (artificial general intelligence), which has a greater potential to change the economy at all levels. As a result, AI will become established as the core of the new industrial revolution.