Persistence in the Cryptocurrency Market

Caporale, Guglielmo Maria; Gil-Alana, Luís A.; Plastun, Alex

doi:10.2139/ssrn.3084023

“…Cryptocurrencies have very high unconditional volatility and are subject to sudden, massive price swings. As the search for determinants of cryptocurrency returns goes on, researchers have found broad themes, such as market forces of supply and demand, the arrival of additional information, trust, speculators (Ciaian, Rajcaniova, & Kancs, 2016), price clustering at round numbers (Urquhart, 2017), persistence (Caporale, Gil-Alana, & Plastun, 2017), mining difficulty and block size (Adjei, 2019), market stress and herding (Raimundo Júnior, Palazzi, Tavares, & Klotzle, 2020; Youssef, 2020) and investor attention (Subramaniam & Chakraborty, 2020).…”

Section: The Literature On Cryptocurrenciesmentioning

confidence: 99%

Pro forma modeling of cryptocurrency returns, volatilities, linkages and portfolio characteristics

Malladi

¹

2022

CAFR

3

0

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PurposeCritics say cryptocurrencies are hard to predict, lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.Design/methodology/approachWe use daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top-six cryptocurrencies that constitute 80% of the market. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effects model (FEM), random-effects model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.FindingsThe seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) Cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are: emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.Practical implicationsOne of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.Originality/valueThis paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices. Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods. Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

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“…Bitcoin, being the most capitalized cryptocurrency, as a rule, sets and determines the main trends of the crypto market as a whole. Therefore, except for the part of the work where the study of collective processes in the market is carried out, we will use the time series of Bitcoin [64]. From figure 1 it can be seen that at the beginning of its existence, Bitcoin's dynamic was determined mainly by the processes of the formation of the market as a whole and characterized by high volatility, which, however, was not associated with critical phenomena.…”

Section: Data and Classificationmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

Bielinskyi

¹

,

Serdyuk

²

,

Семеріков

³

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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Econophysics of cryptocurrency crashes: a systematic review

Bielinskyi¹,

Serdyuk²,

et al. 2021

View full text Add to dashboard Cite

Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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Persistence in the Cryptocurrency Market

Cited by 4 publications

References 27 publications

Pro forma modeling of cryptocurrency returns, volatilities, linkages and portfolio characteristics

Pro forma modeling of cryptocurrency returns, volatilities, linkages and portfolio characteristics

Econophysics of cryptocurrency crashes: an overview

Econophysics of cryptocurrency crashes: a systematic review

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