Speculative bubble tendencies in time series of Bitcoin market prices

Demmler, Michael; Domínguez, Amilcar Orlian Fernández

doi:10.15446/cuad.econ.v41n86.85391

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…Inflation or economic crises influence Bitcoin's volatility. Speculative trading can intensify price swings, as traders buy and sell based on price movements rather than Bitcoin's intrinsic value [48].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

Bâra,

Georgescu,

Oprea

et al. 2024

IEEE Access

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In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec's Probability of Failure (POF) test and Christoffersen's test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD→BRENT and BTCUSD→S&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.INDEX TERMS backtesting, Bitcoin, Brent crude oil, GARCH model, price volatility, S&P500

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

Bâra,

Georgescu,

Oprea

et al. 2024

IEEE Access

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show abstract

Are markets sentiment driving the price bubbles in the virtual?

Ben Osman,

Galariotis,

Guesmi

et al. 2024

International Review of Economics & Finance

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Analysis of Bubbles Assets for Weighted Average Interest Rates Applied to Bank Loans

Tarkun,

Işığıçok

2024

Yönetim Ve Ekonomi Araştırmaları Dergisi

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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.

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Speculative bubble tendencies in time series of Bitcoin market prices

Cited by 4 publications

References 42 publications

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

Are markets sentiment driving the price bubbles in the virtual?

Analysis of Bubbles Assets for Weighted Average Interest Rates Applied to Bank Loans

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