Bitcoin price–volume: A multifractal cross-correlation approach

Alaoui, Marwane El; Bouri, Elie; Roubaud, David

doi:10.1016/j.frl.2018.12.011

Cited by 78 publications

(44 citation statements)

References 17 publications

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“…To estimate the generalized Hurst exponent, we use the MF-DFA, which may be applied to non-stationary time series (Kantelhardt et al, 2002). The MF-DFA has become a popular method to study the multifractal properties of various time series, and studies on Bitcoin have already applied this method, for example, Takaishi (2018);El Alaoui et al (2018). Let us consider the time series x i : i = 1, 2, ...N .…”

Section: Methodsmentioning

confidence: 99%

Rough volatility of Bitcoin

Takaishi

2020

Finance Research Letters

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Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the generalized Hurst exponent is less than 1/2, which indicates log-volatility increments that are rough. Furthermore, we find that the generalized Hurst exponent is not constant. This observation indicates that the log-volatility has multifractal property. Using shuffled time series of the log-volatility increments, we infer that the source of multifractality partly comes from the distributional property.

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Section: Methodsmentioning

confidence: 99%

Rough volatility of Bitcoin

Takaishi

2020

Finance Research Letters

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“…Our empirical analyses are not only useful to investors for the construction of improved forecasts of long-term volatility in the Cryptocurrency market but to policy-makers concerned about market efficiency in this young Cryptocurrency market. This makes our paper related to the literature on the efficiency of financial markets in general (Malkiel, 1989) and the Cryptocurrency market in particular (see, among others, Urquhart, 2016, Kristoufek, 2018, Alaoui et al, 2018.…”

Section: Introductionmentioning

confidence: 95%

Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting

Walther

Klein

Bouri

2019

Journal of International Financial Markets, Institutions and Mo

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123

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We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.

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“…In fact, this term is gathering a great variety of methods. Just mentioning the most often used: classical variance analysis and Pearson correlation coefficient [ 10 , 11 , 12 , 13 , 14 , 15 ], cointegration analysis [ 16 , 17 , 18 , 19 ], multifractal analysis [ 20 , 21 , 22 , 23 ], random matrix theory [ 24 , 25 , 26 , 27 ], power law classification scheme [ 28 , 29 , 30 ], or entropy-based methods [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market

Miśkiewicz

2021

Entropy

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Within the paper, the problem of globalisation during financial crises is analysed. The research is based on the Forex exchange rates. In the analysis, the power law classification scheme (PLCS) is used. The study shows that during crises cross-correlations increase resulting in significant growth of cliques, and also the ranks of nodes on the converging time series network are growing. This suggests that the crises expose the globalisation processes, which can be verified by the proposed analysis.

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Bitcoin price–volume: A multifractal cross-correlation approach

Cited by 78 publications

References 17 publications

Rough volatility of Bitcoin

Rough volatility of Bitcoin

Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting

Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market

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