Dissection of Bitcoin's Multiscale Bubble History

Gerlach, J-C; Demos, Guilherme; Sornette, Didier

doi:10.2139/ssrn.3164246

Cited by 25 publications

(29 citation statements)

References 56 publications

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“…The Bitcoin phenomenon has attracted significant attention in the academic literature with regard to its fundamental value (Woo, Gordon, and Iaralov (2013), Garcia et al (2014), Hayes (2015), Hayes (2017)), price dynamics (Buchholz et al (2012), Kristoufek (2013), Garcia et al (2014), Garcia and Schweitzer (2015), Glaser et al (2014), , Bouoiyour, Selmi, and Tiwari (2015), Ciaian, Rajcaniova, and Kancs (2016), Bouri, Azzi, and Dyhrberg (2017)), bubble modelling (MacDonell (2014), Cheah and Fry (2015), Gerlach, Demos, and Sornette (2018)), price discovery (Brandvold et al (2015)), and more recently about univariate volatility modelling (Dyhrberg (2016a) Gkillas and Katsiampa (2018) studied the tail behavior of the returns of five major cryptocurrencies by using again extreme value analysis and computing the Valueat-Risk and Expected Shortfall, but no backtesting analysis was implemented. Trucios (2018) compared the one-step-ahead volatility forecast of Bitcoin using several GARCH-type models and also evaluated the performance of several procedures when estimating the Value-at-Risk.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In general, standard models for market risk tend to work poorly with cryptocurrencies due to the frequent presence of structural breaks, see Bouri et al (2016), Fantazzini et al (2016), Fantazzini et al (2017), Mensi, Al-Yahyaee, and Kang (2018) and Thies and Molnar (2018). To make matters worse, price manipulations and market frauds caused by the lack of financial oversight (Gandal et al (2018), Griffin andShams (2018)) and the fact that cryptocurrencies are still mainly used for speculative purposes, make financial bubbles a recurring phenomenon, see Corbet, Lucey, and Yarovaya (2018), Cheah and Fry (2015) and Gerlach, Demos, and Sornette (2018). A potential solution could be to use complex model…”

Section: Literature Reviewmentioning

confidence: 99%

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A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies

Fantazzini

Zimin

2019

J. Ind. Bus. Econ.

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This paper proposes a set of models which can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously to estimate also their credit risk using the Zero Price Probability (ZPP) model by , which is a methodology to compute the probabilities of default using only market prices. For this purpose, both univariate and multivariate models with different specifications are employed. Two special cases of the ZPP with closed-form formulas in case of normally distributed errors are also developed using recent results from barrier option theory. A backtesting exercise using two datasets of 5 and 15 coins for market risk forecasting and a dataset of 42 coins for credit risk forecasting was performed. The Value-at-Risk and the Expected Shortfall for single coins and for an equally weighted portfolio were calculated and evaluated with several tests. The ZPP approach was used for the estimation of the probability of default/death of the single coins and compared to classical credit scoring models (logit and probit) and to a machine learning algorithm (Random Forest). Our results reveal the superiority of the t-copula/skewed-t GARCH model for market risk, and the ZPP-based models for credit risk.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies

Fantazzini

Zimin

2019

J. Ind. Bus. Econ.

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“…We now mention some related works. A methodology to detect bubbles in the price dynamics of cryptocurrencies (specifically, Bitcoin), using multi-scale analysis, as well as k-means clustering, is presented in [19]. In [9] the predictability of cryptocurrencies time series is investigated through several alternative univariate and multivariate models in point and density forecasting.…”

Section: Introductionmentioning

confidence: 99%

Topological Recognition of Critical Transitions in Time Series of Cryptocurrencies

Gidea

Goldsmith

Katz³

et al. 2018

SSRN Journal

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We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 -beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -k-means clustering -in order to automatically recognize the emerging chaotic regime in a complex system approaching a critical transition. We first test our methodology on the complex system dynamics of a Lorenz-type attractor, and then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions in the cryptocurrency markets, even though the relevant time series exhibit a highly erratic behavior.

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“…With regard to the expected return, the cryptocurrencies are characterised by values that rise and fall (Bech and Garratt ; Liew and Hewlett ; Chuen et al ; Frunza and Guegan ; Gerlach et al ). In some cases, their values change with financial anomalies (Caporale et al ; Caporale and Plastun ; Liu and Tsyvinski ).…”

Section: Would We Like Central Bank Digital Currencies and Cryptocurrmentioning

confidence: 99%

Central Bank Digital Cash and Cryptocurrencies: Insights from a New Baumol–Friedman Demand for Money

Masciandaro

2018

Australian Economic Review

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This article analyses ongoing changes in the supply of alternative media of payments (MOPs). The comparison between old (cash and deposits) and new (cryptocurrencies and central bank digital currencies) MOPs is based on a novel definition of money where a MOP has three properties: the first two are the standard functions of medium of exchange (liquidity costs) and store of value (opportunity costs) and the third is the novel function of store of information (privacy costs). Given such properties and that the evolution of the different MOPs likely depends on individual preferences, the relevance of experimental economics is highlighted.

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Dissection of Bitcoin's Multiscale Bubble History

Cited by 25 publications

References 56 publications

A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies

A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies

Topological Recognition of Critical Transitions in Time Series of Cryptocurrencies

Central Bank Digital Cash and Cryptocurrencies: Insights from a New Baumol–Friedman Demand for Money

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