Bitcoin and the day-of-the-week effect

Aharon, David Y.; Qadan, Mahmoud

doi:10.1016/j.frl.2018.12.004

Cited by 84 publications

(56 citation statements)

References 33 publications

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“…Ma and Tanizaki (2019) analyse the day-of-the-week effect for both returns and their volatility in the cryptocurrency market, and find significantly high volatilities on Monday and Thursday. Similar results are reported by Aharon and Qadan (2018). Eross et al (2019) analyse the intraday dynamics of Bitcoin and find that the trade volume in the cryptocurrency market increases during the day and falls from around 4 pm until midnight.…”

Section: Literature Reviewsupporting

confidence: 81%

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Bitcoin fluctuations and the frequency of price overreactions

Caporale

Plastun

Oliinyk

2019

Financ Mark Portf Manag

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This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013-2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits no seasonality (H2). On the whole, the results suggest that it can provide useful information to predict price dynamics in the cryptocurrency market and for designing trading strategies (H1 cannot be rejected), whilst there is no evidence of seasonality (H2 cannot be rejected).

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Section: Literature Reviewsupporting

confidence: 81%

“…Balcilar et al (2017) show that information about trade volumes can be used to predict returns in the cryptocurrency market. Aharon and Qadan (2018) show that normally used variables have limited forecasting power for Bitcoin prices. Khuntia and Pattanayak (2018) explore time-varying linear and nonlinear dependence in Bitcoin returns.…”

Section: Literature Reviewmentioning

confidence: 99%

Bitcoin fluctuations and the frequency of price overreactions

Caporale

Plastun

Oliinyk

2019

Financ Mark Portf Manag

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“…Based on these findings it can be concluded that the growth of bitcoin prices is very much for the monetary system in Indonesia. However, based on research (Aharon & Qadan, 2018) found that the movement of bitcoin is outside the speculation of capital markets, bonds and commodities. This indicates that there will be an impact arising from the movement of the bitcoin.…”

Section: Figure 1 Development Of Bitcoin Price Year 2012-2019mentioning

confidence: 99%

Causality of the Growth of Bitcoin Prices with Monetary Conditions in Indonesia

Aimon¹,

Putra²,

Husein³

2020

Proceedings of the 4th Padang International Conference on Education, Economics, Business and Accounting (PICEEBA-2 2019)

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Virtual currency has been a hot topic for discussion lately. One of the popular virtual currencies is bitcoin which has a status as a commodity in Indonesia today. The purpose of this study is to determine whether there is influence and causality between bitcoin and monetary variables in Indonesia. The study used time series data in monthly form March 2012 to July 2019. The study used Johnsen's co integration test, vector auto regression estimation, granger causality test, forecasting with Impulse Response Function (IRF), and decomposition variance analysis. In this research, it is known that in the long run the growth of bitcoin prices, the exchange rate, inflation, the price of gold, and interest rates does not have balance and similarity in movement. Causally there is a direct relationship between the price of gold and the exchange rate with the consumer price index. In the VAR estimation it is known that bitcoin price growth affects the consumer price index in Indonesia, and the consumer price index affects the price of gold. In impulse response function forecasting there are positive and negative responses of each variable in the study of other variables. In decomposition variance analysis, it is known that the contribution of influence of a variable is more dominated by the variable itself when shock or shock occurs.

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“…Therefore, substantial effort is made to understand the properties and the sources of volatility in Bitcoin and other cryptocurrency markets. Thus far, research has either considered model based estimates of Bitcoin volatility (Aharon and Qadan 2019;Baur and Dimpfl 2018;Chu et al 2017;Katsiampa 2017), relied on realized volatility (Aalborg et al 2019;Catania and Sandholdt 2019;Ma et al 2020) or other non-parametric measures (Bleher and Dimpfl 2019) to construct daily or higher-frequency time series of volatility. In this article, we contribute to the literature with a trading perspective on volatility.…”

Section: Introductionmentioning

confidence: 99%

Bitcoin Price Risk—A Durations Perspective

Dimpfl

Odelli

2020

JRFM

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An important aspect of liquidity is price risk, i.e., the risk that a small transaction leads to a large price change. This usually happens in a thin market, when trading opportunities are scarce and the time between subsequent trades is long. We rely on an autoregressive conditional duration model to extract the probability of a substantial price event in a particular time interval and, thus, an intraday risk profile. Our findings show that price risk is highest at times when European and U.S. investors do not trade. In a second step, we relate daily aggregates to characteristics of the Bitcoin blockchain and investigate whether investors account for features like confirmation time or fees when timing their orders.

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Bitcoin and the day-of-the-week effect

Cited by 84 publications

References 33 publications

Bitcoin fluctuations and the frequency of price overreactions

Bitcoin fluctuations and the frequency of price overreactions

Causality of the Growth of Bitcoin Prices with Monetary Conditions in Indonesia

Bitcoin Price Risk—A Durations Perspective

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