Cryptocurrencies: Stylized Facts on a New Investible Instrument

Hu, Albert S.; Parlour, Christine A.; Rajan, Uday

doi:10.2139/ssrn.3182113

Cited by 71 publications

(52 citation statements)

References 10 publications

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“…DeMiguel et al (2007) and references therein. Moreover, Hu et al (2018) and Brauneis and Mestel (2019) analyzed cryptocurrency-portfolios and showed that the simple 1/N portfolio outperforms all competing portfolio strategies. However, most investors in crypto-currencies use capitalization-weighted portfolios, where individual assets are weighted according to their total market capitalization see, for example, the monthly trading volume rankings published at coinmarketcap.com/currencies/volume/monthly.…”

Section: Capitalization-weighted Portfoliosmentioning

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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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: Capitalization-weighted Portfoliosmentioning

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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“…Prior work (e.g. Balcilar et al , 2017; Hu et al , 2018; Koutmos, 2018; Liu & Tsyvinski, 2018; Ji et al , 2019) has suggested that, given its dominance in terms of market capitalisation and investor attention, bitcoin returns are a key driver for other cryptocurrencies. If this were true, then we would expect bitcoin returns to be closely correlated with the identified first principal component (recall that PCA is conducted with portfolios not containing bitcoin).…”

Section: Empirical Analysismentioning

confidence: 99%

One Cryptocurrency to Explain Them All? Understanding the Importance of Bitcoin in Cryptocurrency Returns

Smales

2020

J of App Econs and Policy

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We utilise principal component analysis to determine whether a (small) set of factors can explain cryptocurrency returns and whether this varies over time. We find that a substantial proportion of cryptocurrency return variation is explained by a single principal component that is highly correlated with bitcoin returns. The explanatory power of this factor is greatest for larger cryptocurrencies and increases markedly in the most recent part of the sample. Our results have implications for investors determining optimal portfolio decisions and for policy-makers wary of systemic risk.

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“…The advent of more sophisticated blockchains such as Ethereum and EOS has enabled a much wider range of applications, including insurance contracts, voting schemes, and contingent investment products. ICOs are a fundraising mechanism in which a new token is sold to investors and prospective returns, see Benedetti and Kostovetsky (2018), Chanson et al (2018), Dittmar and Wu (2018), Drobetz et al (2018), Hu et al (2018), Lu (2018), Momtaz (2018a), Stastny (2018), Yuryev (2018), and Stanley (2019). On the amount raised, including failure to meet a set target, see Adhami et al (2018), , Ante et al (2018), Blaseg (2018), Burns and Moro (2018), Davydiuk et al (2018), Feng et al (2018), Fenu et al (2018), Fisch (2018), , Momtaz (2018b), Momtaz (2018c), Rhue (2018b), Albrecht et al (2019), Ante and Fiedler (2019), Cai and Gomaa (2019), Cerchiello et al (2019), Chen (2019), Johnson and Yi (2019), Masiak et al (2019), Philippi et al (2019), and Wu et al (2019).…”

Section: The Ico Marketmentioning

confidence: 99%