Long-range memory, distributional variation and randomness of bitcoin volatility

Lahmiri, Salim; Bekiros, Stelios; Salvi, Antonio

doi:10.1016/j.chaos.2017.12.018

Cited by 125 publications

(58 citation statements)

References 23 publications

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“…In the Bitcoin case, the long-term effect of changes in developers' affect metrics may be correlated with the market efficiency. Indeed, in [10,12] the authors show that the Bitcoin market is not efficient, i.e. that all information is not instantly incorporated into prices, hence the large time lag of the causality.…”

Section: General Remarks For the Bitcoin And Ethereum Analysismentioning

confidence: 99%

“…Analyses of the cryptocurrency markets [8][9][10] yielded insights on their maturity, efficiency and structure. A large body of literature is also looking at the volatility of cryptocurrencies, from the model estimation point of view [11,12] as well as by extrapolating the mechanisms driving the fluctuations. Studies showed, for example, a strong correlation with global economic activity [13,14] and volume of trades [15].…”

Section: Introductionmentioning

confidence: 99%

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The Butterfly “Affect”: impact of development practices on cryptocurrency prices

et al. 2020

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The network of developers in distributed ledgers and blockchains open source projects is essential to maintaining the platform: understanding the structure of their exchanges, analysing their activity and its quality (e.g. issues resolution times, politeness in comments) is important to determine how "healthy" and efficient a project is. The quality of a project affects the trust in the platform, and therefore the value of the digital tokens exchanged over it. In this paper, we investigate whether developers' emotions can effectively provide insights that can improve the prediction of the price of tokens. We consider developers' comments and activity for two major blockchain projects, namely Ethereum and Bitcoin, extracted from Github. We measure sentiment and emotions (joy, love, anger, etc.) of the developers' comments over time, and test the corresponding time series (i.e. the affect time series) for correlations and causality with the Bitcoin/Ethereum time series of prices. Our analysis shows the existence of a Granger-causality between the time series of developers' emotions and Bitcoin/Ethereum price. Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE)-associated with the prediction of the prices of cryptocurrencies-significantly decreases when including the affect time series.

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Section: General Remarks For the Bitcoin And Ethereum Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Butterfly “Affect”: impact of development practices on cryptocurrency prices

et al. 2020

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“…The hierarchical clustering algorithm is a methodology that robustly explores the clustering of a dataset to mine this information for connectedness visualization. It is worth nothing that GARCH-based models, entropy, and hierarchical clustering have successfully been applied to model volatility [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], to evaluate randomness in financial and economic data [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ], and to cluster financial data [ 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

Lahmiri

Bekiros

2020

Entropy

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The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.

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“…Bu durum hem akademisyenlerin hem de yatırımcıların ilgisini çekmiş ve Bitcoin piyasasını inceleyen akademik çalışmaların sayısının artmasını sağlamıştır. Bu çalışmaların temel dayanağını Bitcoin piyasasındaki fiyat hareketleri oluşturmuştur (Fink ve Johann, 2014;Kristoufek, 2015;Osterrieder ve Lorenz, 2017;Jiang, Ruan ve Nie 2018;Lahmiri, Bekiros ve Salvi, 2018;Bariviera, Basgall, Hasperue ve Naiouf, 2017;Shi Sun, Gao, Xu, Shen ve Cheng, 2019). Finansal piyasalardaki fiyat hareketleri, o piyasayı anlamak, modellemek ve riski ölçmek için kilit rol oynamaktadır (Bouchaud ve Potters, 2003).…”

Section: Anahtar Kelimelerunclassified

The Day of the Week Anomaly in Bitcoin Market

Evci

2020

Alanya Akademik Bakış

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ÖZET Kripto para piyasası kısa dönemde çok hızlı bir gelişim göstermiş hem yatırımcıların hem de akademisyenlerin ilgisini çekmiştir. Bu piyasada en fazla piyasa değerine sahip kripto para birimi Bitcoin'dir. Gerek geleneksel finansal piyasaların işleyişinden farklı bir piyasa işleyişine sahip olması gerekse para yaratma sürecinde farklı bir sistemi kullanması yatırımcılar açısından Bitcoin fiyatlarında değişime yol açan faktörleri anlamayı gerekli kılmaktadır. Bu çalışma ile Bitcoin fiyatlarında haftanın günü anomalisinin varlığının araştırılması amaçlanmıştır. Bitcoin getirilerinde haftanın günü anomalisi, 2013-2019 yıllarına ait günlük fiyatlar kullanılarak asimetrik GARCH modeliyle incelenmiştir. Çalışmadan elde edilen bulgular Bitcoin getirileri üzerinde Pazartesi, Perşembe ve Pazar günlerinin negatif etkileri olduğunu ve en fazla kaybın Perşembe günü gerçekleştiğini ortaya koymuştur.

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Long-range memory, distributional variation and randomness of bitcoin volatility

Cited by 125 publications

References 23 publications

The Butterfly “Affect”: impact of development practices on cryptocurrency prices

The Butterfly “Affect”: impact of development practices on cryptocurrency prices

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

The Day of the Week Anomaly in Bitcoin Market

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