An analysis of cryptocurrencies conditional cross correlations

Aslanidis, Nektarios; Bariviera, Aurelio F.; Martínez-Ibañez, Oscar

doi:10.1016/j.frl.2019.04.019

Cited by 122 publications

(40 citation statements)

References 33 publications

(24 reference statements)

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“…and Taylor [2007]). Aslanidis et al [2019] found three key results when analysing the behaviour of conditional correlations among main cryptocurrencies, stock and bond indices, and gold, using a generalised DCC class model. Firstly, correlations among cryptocurrencies are positive, albeit varying across time.…”

Section: Introductionmentioning

confidence: 97%

Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple

Celeste

Corbet

Gurdgiev

2020

The Quarterly Review of Economics and Finance

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The substantial volatility and growth in cryptocurrencies valuations between 2009 and the end of 2017 strongly suggest that both long memory and price volatility and return spillovers should be present in these assets' dynamics. To date, literature on the major cryptocurrencies price processes does not address jointly and comprehensively their fractal properties, long memory and wavelet analysis, that could robustly confirm the presence of fractal dynamics in their prices, and confirm or deny the validity of the Fractal Market Hypothesis as being applicable to the cryptocurrencies.This research shows that Bitcoin prices exhibit long term memory, although its trend has been reducing overtime. In fact, assessing Bitcoin, Ethereum and Ripple across the period between 2016 and 2017, focusing solely on the period prior to the crash of 2018, we can conclude that Bitcoin was better described by a random walk, showing signs of markets maturity emerging, in contrast, other cryptocurrencies such as Ethereum and Ripple present evidence of a growing underlying memory behaviour.

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Section: Introductionmentioning

confidence: 97%

Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple

Celeste

Corbet

Gurdgiev

2020

The Quarterly Review of Economics and Finance

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“…the different cryptocurrencies protocols) in the cross-behavior of cryptocurrencies. An exception is Aslanidis et al (2019), who speculate on the distinct Monero validation algorithm to justify its singular behavior. However, their explanation is provisional.…”

Section: Citation Sources and Authors Graphsmentioning

confidence: 99%

A bibliometric analysis of bitcoin scientific production

Merediz-Solà

Bariviera

2019

Research in International Business and Finance

Self Cite

140

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Blockchain technology, and more specifically Bitcoin (one of its foremost applications), have been receiving increasing attention in the scientific community. The first publications with Bitcoin as a topic, can be traced back to 2012. In spite of this short time span, the production magnitude (1162 papers) makes it necessary to make a bibliometric study in order to observe research clusters, emerging topics, and leading scholars. Our paper is aimed at studying the scientific production only around bitcoin, excluding other blockchain applications. Thus, we restricted our search to papers indexed in the Web of Science Core Collection, whose topic is "bitcoin". This database is suitable for such diverse disciplines such as economics, engineering, mathematics, and computer science. This bibliometric study draws the landscape of the current state and trends of Bitcoin-related research in different scientific disciplines.

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“…Not only about their most differentiating characteristics [45][46][47][48] but also for the analysis of volatility models [43,[49][50][51][52][53] and risk forecasts [36]. Their behavior for hedging has been studied in portfolios with other kinds of assets [54,55], or with others cryptos [29,[56][57][58]. Our application fits in this latter framework, since GC models are applied to a portfolio with the three best known and most representative crypto assets: Bitcoin, Litecoin and Ripple.…”

Section: Cryptocurrenciesmentioning

confidence: 99%

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Jiménez¹,

Mora–Valencia²,

Ñíguez³

et al. 2020

Preprint

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The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of Dynamic Equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio on cryptocurrencies by implementing backtesting techniques and for different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. The results support our proposal showing that the SNP-DCC model has better performance for a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.

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An analysis of cryptocurrencies conditional cross correlations

Cited by 122 publications

References 33 publications

Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple

Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple

A bibliometric analysis of bitcoin scientific production

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

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