Cryptocurrencies as financial bubbles: The case of Bitcoin

Geuder, Julian; Kinateder, Harald; Wagner, Niklas

doi:10.1016/j.frl.2018.11.011

Cited by 159 publications

(90 citation statements)

References 10 publications

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“…As we move to the Newey and West (1987) covariance estimators accounting for five lags, we obtain similar levels of statistical significance as we obtain using our proposed heteroscedasticity-and-autocorrelation-robust test statistics using a random block length of h = 20. The findings of our analysis are in line with Godfrey (2009), who argues that asymptotically valid tests such as the HAC-robust t-tests as proposed by Newey and West (1987) may involve severe size distortions, especially when the data set is small and the data are fat-tailed and highly skewed. Moreover, it is important to stress that using Newey and West (1987) covariance estimators, we lose (due to the lag structure) information that could be important for statistical inference.…”

Section: Metricsupporting

confidence: 87%

“…A wide range of recent literature suggests (dynamic and other) dependency structures in cryptocurrencies such as speculative bubble formation (Cretarola and Figà-Talamanca, 2020;Geuder et al, 2019;Chaim and Laurini, 2019;Fry, 2018), regime switches and volatility clustering (Ardia et al, 2019;Caporale and Zekokh, 2018;Conrad et al, 2018;Chu et al, 2017;Dyhrberg, 2016;Katsiampa, 2017), and seasonal patterns (Aharon and Qadan, 2019;Baur et al, 2019;Caporale and Plastun, 2019). To provide an illustrative example of these issues, we plot the time series evolution of daily log-returns of the cryptocurrency Ethereum in Fig.…”

Section: Statistical Inference and Blocks Bootstrapsmentioning

confidence: 99%

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Speculation and Lottery-Like Demand in Cryptocurrency Markets

Grobys

Junttila

2020

SSRN Journal

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This is the first paper that explores lottery-like demand in cryptocurrency markets. Since recent research provides evidence that cryptocurrency returns appear to be short-memory processes, we modify Bali, Cakici and Whitelaw's (2011) and Bali, Brown, Murray, and Tang's (2017) MAX measure and employ a weekly forecast horizon and daily log-returns from the previous week to calculate the metric for our portfolio sorts. From an econometric point of view, this study proposes statistical tests that are robust to unknown dynamic dependency structures in the cryptocurrency data. Our results show that average raw and risk-adjusted return differences between cryptocurrencies in the lowest and highest MAX quintiles exceed 1.50% per week. These results are robust after controlling for Bitcoin risk or potential microstructure effects. Our findings are important also from a theoretical point of view because they suggest that parallel to stock markets, similar behavioral mechanisms of underlying investor behavior are present also in new virtual currency markets.

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Section: Metricsupporting

confidence: 87%

Section: Statistical Inference and Blocks Bootstrapsmentioning

confidence: 99%

Speculation and Lottery-Like Demand in Cryptocurrency Markets

Grobys

Junttila

2020

SSRN Journal

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“…Hu and Oxley [44] (2018) not only suggest there were asset price bubbles in Japan in the 80s and 90s, but they also find contagion from the equity to the real estate market. Geuder et al [45] use the Phillips et al [31] methodology, and suggest find that Bitcoin had several bubble periods in 2017, but since January 2018 this kind of behavior does not exist. Chaim and Laurini [46] verify that Bitcoin had bubble episodes in the period between early 2013 and mid-2014, but on the other hand, it did not have in 2017.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods

Perifanis¹

2019

Energies

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Oil prices have had considerable surges and bursts since the first oil crisis of 1973. Until then its price was stable, with almost zero volatility. Since then, apart from the two oil crises of 1973 and 1978/9, oil prices had consecutive bubble episodes like the surges up to 2008 and 2014 and their successive bursts, respectively. The trace of these bubble periods is of crucial importance for policymakers, since their drivers and consequences impact global economic developments. Phillips et al. and Phillips et al. methodologies are applied to detect whether West Texas Intermediate prices experienced bubble periods. Both methodologies suggest that WTI prices experienced explosive episodes, which could be fundamentally, speculatively, or politically attributed. Some suggested periods coincide for both methods, but the second methodology seems to be more sensitive than its predecessor is, leading to better bubble detection but also to identification of non-existent bubbles. The identified bubble periods are compared to relevant research in the literature concerning their presence, duration, and explosiveness. The main goal of the research, apart from the detection of bubbles’ presence and duration, is to identify the causal underlying reasons for each explosive episode. Further, we compare the start and endpoints of each bubble episode with time-points when structural changes occurred. The contribution of the paper is that it clearly defines the bubble episodes with their corresponding drivers. The paper identifies the importance of market fundamentals’ swifts in explaining the bubble periods. The findings of the papers can help policymakers and other stakeholders to monitor oil price shifts and their underlying reasons, and then proceed with prompt actions. Since bubble episodes are fundamentally explained, then the practical utility is that by focusing on the market fundamentals, stakeholders can avoid actions that could result in market failures.

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“… Smales (2019) argues against it because of Bitcoin's high volatility, illiquidity, and transaction cost. Chaim & Laurini (2019) also point out the potential bubble in Bitcoin, albeit it is more probable for the period before December 2017 ( Geuder, Kinateder, & Wagner, 2019 ). During the COVID-19 market downturn, Conlon & McGee (2020) state that Bitcoin is not a safe-haven since its price moves closely with S&P500.…”

Section: Introductionmentioning

confidence: 99%