2020
DOI: 10.1108/jeim-10-2018-0228
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The relationship between Bitcoin returns, volatility and volume: asymmetric GARCH modeling

Abstract: PurposeTo show that when volume of trades is taken into consideration, Bitcoin does not seem as volatile as it claimed. Further, to study the relationship between Bitcoin trading volume, volatility and returns, and the asymmetry in response to economic information for the period from July 2010 to November 2017.Design/methodology/approachComparison of Bitcoin price volatility with that of six currencies and gold. We repeat the analysis using returns divided by volume. We examine the relationship between volume,… Show more

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Cited by 22 publications
(17 citation statements)
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References 35 publications
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“…The existing literature supports the notion that, in contexts of symmetric information, the GARCH (1, 1) model effectively captures data volatilities and returns. Conversely, under conditions of asymmetric information, asymmetric GARCH models prove to be more appropriate (see Bhowmik and Wang 2020;Pichl and Kaizoji 2017;Sapuric et al 2022).…”
Section: Methodsmentioning
confidence: 99%
“…The existing literature supports the notion that, in contexts of symmetric information, the GARCH (1, 1) model effectively captures data volatilities and returns. Conversely, under conditions of asymmetric information, asymmetric GARCH models prove to be more appropriate (see Bhowmik and Wang 2020;Pichl and Kaizoji 2017;Sapuric et al 2022).…”
Section: Methodsmentioning
confidence: 99%
“…Significant volatility may lead to the perception that the asset is a high‐risk currency rather than a secure investment and may be considered as a speculative asset (Baek & Elbeck, 2015; Cheah & Fry, 2015; Sapuric et al, 2022). Significant declines in price might deter investors, since the asset's abrupt devaluation can result in substantial losses and prompt investors to find a secure shelter (Baur & Hoang, 2020).…”
Section: Selection Of Variable and Data Collectionmentioning
confidence: 99%
“…The leverage effect describes the tendency for bad news to have a significantly larger impact on the conditional variance of stock returns than good news (Marquering and de Goeij 2005). As digital currencies have gained popularity, so has the body of research comparing their volatility qualities to those of conventional financial instruments (Sapuric et al 2020). The GARCH model is the most effective model used to measure crypto-associated volatility (Naimy et al 2021).…”
Section: Literature Reviewmentioning
confidence: 99%