A Statistical Classification of Cryptocurrencies

Pele, Daniel Traian; Wesselhöfft, Niels; Härdle, Wolfgang Karl; Kolossiatis, Michalis; Yatracos, Yannis G.

doi:10.2139/ssrn.3548462

Cited by 7 publications

(10 citation statements)

References 32 publications

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“…To cope with these limitations, we resort to the characteristic based clustering method proposed by Wang et al (2005). It was recently applied by Pele et al (2020) for classifying cryptos in order to distinguish them from traditional assets. This methods recommends to incorporate various global measures describing the structural characteristics of a time series for a clustering problem.…”

Section: Methodsmentioning

confidence: 99%

Blockchain Mechanism and Distributional Characteristics of Cryptos

et al. 2020

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Section: Methodsmentioning

confidence: 99%

Blockchain Mechanism and Distributional Characteristics of Cryptos

et al. 2020

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“…Hu et al (2019) analyse the stylized facts and return properties of 222 cryptocurrencies and find a large degree of skewness and volatility in the population of returns. Furthermore, according to Pele et al (2020) cryptocurrencies can be clearly separated from classical assets, mainly due to their tail behaviour. However, their cluster results also reveal that the behaviour of the cryptocurrencies is diverse.…”

Section: Contentmentioning

confidence: 99%

“…And identifying them, it is useful to better understand the cryptocurrency market, but also for building a diversified portfolio. In the same way, they use different representations of the cryptocurrencies: correlations (Song et al, 2019;Stosic et al, 2018), factors extracted from the correlation matrix (Pele et al, 2020) and time series (Sigaki et al, 2019). Each representation focuses on different aspects of the cryptocurrency that are meaningful for the purpose of the analysis.…”

Section: Contentmentioning

confidence: 99%

“…Corbet et al (2018) show that cryptocurrencies are highly connected among themselves and disconnected from mainstream assets (bonds, stocks, S&P500, gold). In turn, Pele et al (2020) merge classification based on asset profiles and dynamic evolution of clusters. First, they characterize of selected group of log-returns assets including 150 cryptocurrencies, stocks commodities and exchange rates to estimate a multidimensional vector applying a dimensionality reduction with factor analysis.…”

Section: Clustering Of Cryptocurrenciesmentioning

confidence: 99%

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Analysis of the cryptocurrency market applying different prototype-based clustering techniques

Lorenzo¹,

Arroyo²

2021

Preprint

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Since the appearance of Bitcoin, cryptocurrencies have experienced enormousgrowth not only in terms of capitalization but also in number. As a result, thecryptocurrency market can be an attractive arena for investors as it offers manypossibilities, but a difficult one to understand as well. In this work, we aim tosummarize and segment the whole cryptocurrency market in 2018 with the helpof data analysis tools. We will use three different partitional clustering algorithmseach of them using a different representation for cryptocurrencies, namely: yearlymean and standard deviation of the returns, distribution of returns, and timeseries of returns. Since each representation will provide a different andcomplementary perspective of the market, we will also explore the combination ofthe three clustering results to obtain a fine-grained analysis of the main trends ofthe market. Finally, we will analyse the association of the clustering results withother descriptive features of the cryptocurrencies, including the age, technologicalattributes, and financial ratios derived from them. This will help to enhance theprofiling of the clusters with additional insights. As a result, this work offers adescription of the market and a methodology that can be reproduced by investorsthat want to understand the main trends on the market and that look forcryptocurrencies with different financial performance.

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“…In such a case, having m common stochastic trends allows to construct N − m mean reverting portfolios, each having weights given by a user-chosen linear combination of the cointegrating vectors. Cryptocurrency data have been paid increasing attention (Makarov and Schoar, 2020); however, despite the evidence that returns on cryptocurrencies exhibit heavy tails (see Pele et al, 2020), this is not usually accounted for in applications.…”

Section: Real Data Examplesmentioning

confidence: 99%

Inference in heavy-tailed non-stationary multivariate time series

Barigozzi¹,

Cavaliere²,

Trapani³

2021

Preprint

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We study inference on the common stochastic trends in a non-stationary, N -variate time series y t , in the possible presence of heavy tails. We propose a novel methodology which does not require any knowledge or estimation of the tail index, or even knowledge as to whether certain moments (such as the variance) exist or not, and develop an estimator of the number of stochastic trends m based on the eigenvalues of the sample second moment matrix of y t . We study the rates of such eigenvalues, showing that the first m ones diverge, as the sample size T passes to infinity, at a rate faster by O (T ) than the remaining N − m ones, irrespective of the tail index. We thus exploit this eigen-gap by constructing, for each eigenvalue, a test statistic which diverges to positive infinity or drifts to zero according to whether the relevant eigenvalue belongs to the set of the first m eigenvalues or not. We then construct a randomised statistic based on this, using it as part of a sequential testing procedure, ensuring consistency of the resulting estimator of m. We also discuss an estimator of the common trends based on principal components and show that, up to a an invertible linear transformation, such estimator is consistent in the sense that the estimation error is of smaller order than the trend itself. Finally, we also consider the case in which we relax the standard assumption of i.i.d. innovations, by allowing for heterogeneity of a very general form in the scale of the innovations. A Monte Carlo study shows that the proposed estimator for m performs particularly well, even in samples of small size. We complete the paper by presenting four illustrative applications covering commodity prices, interest rates data, long run PPP and cryptocurrency markets.

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A Statistical Classification of Cryptocurrencies

Cited by 7 publications

References 32 publications

Blockchain Mechanism and Distributional Characteristics of Cryptos

Blockchain Mechanism and Distributional Characteristics of Cryptos

Analysis of the cryptocurrency market applying different prototype-based clustering techniques

Inference in heavy-tailed non-stationary multivariate time series

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