Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Balcılar, Mehmet; Bouri, Elie; Gupta, Rangan; Roubaud, David

doi:10.1016/j.econmod.2017.03.019

Cited by 596 publications

(347 citation statements)

References 45 publications

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“…Given that Bitcoin does not provide any cash flows like most of equities and bonds, its price is often affected by supply-demand imbalances, Bitcoin's attractiveness Kristoufek, 2015), macrofinancial developments (Li & Wang, 2017), technological factors and computer programming enthusiasts (Yelowitz & Wilson, 2015), energy (Bouri, Jalkh, Molnár, & Roubaud, 2017), or trading volume (Balcilar, Bouri, Gupta, & Roubaud, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

et al. 2018

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Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

et al. 2018

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“…The main finding is the the Bitcoin proves useful as a diversifier rather than as a hedge instrument. Finally, [Balcilar et al, 2017] detect nonlinearities in the returnvolume relationship, which allows for return prediction.…”

Section: Introductionmentioning

confidence: 99%

An analysis of cryptocurrencies conditional cross correlations

Aslanidis

Bariviera

Martínez-Ibañez

2019

Finance Research Letters

126

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This letter explores the behavior of conditional correlations among main cryptocurrencies, stock and bond indices, and gold, using a generalized DCC class model. From a portfolio management point of view, asset correlation is a key metric in order to construct efficient portfolios. We find that: (i) correlations among cryptocurrencies are positive, albeit varying across time; (ii) correlations with Monero are more stable across time; (iii) correlations between cryptocurrencies and traditional financial assets are negligible. * nektarios.aslanidis@urv.cat † aurelio.fernandez@urv.cat ‡ oscar.martinez@urv.cat

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“…Beyond these seminal articles, the picture gets more complicated as more studies provide further breakdowns and/or out-of-sample evidence. Studies of return autocorrelation conditional on trading volume (e.g., McKenzie & Faff, 2005;Säfvenblad, 2000) or the dynamic (causal) relation between volume and returns (e.g., Balcılar, Bouri, Gupta, & Roubaud, 2017;S. Chen, 2012;Chuang, Kuan, & Lin, 2009;Gebka & Wohar, 2013;Hiemstra & Jones, 1994) also obtain inconsistent or weak results.…”

Section: Introductionmentioning

confidence: 99%

Trading volume and prediction of stock return reversals: Conditioning on investor types' trading

Ülkü

Оніщенко

2019

Journal of Forecasting

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We show that contrasting results on trading volume's predictive role for short‐horizon reversals in stock returns can be reconciled by conditioning on different investor types' trading. Using unique trading data by investor type from Korea, we provide explicit evidence of three distinct mechanisms leading to contrasting outcomes: (i) informed buying—price increases accompanied by high institutional buying volume are less likely to reverse; (ii) liquidity selling—price declines accompanied by high institutional selling volume in institutional investor habitat are more likely to reverse; (iii) attention‐driven speculative buying—price increases accompanied by high individual buying‐volume in individual investor habitat are more likely to reverse. Our approach to predict which mechanism will prevail improves reversal forecasts following return shocks: An augmented contrarian strategy utilizing our ex ante formulation increases short‐horizon reversal strategy profitability by 40–70% in the US and Korean stock markets.

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Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Cited by 596 publications

References 45 publications

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

An analysis of cryptocurrencies conditional cross correlations

Trading volume and prediction of stock return reversals: Conditioning on investor types' trading

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