Some comments on Bitcoin market (in)efficiency

Dimitrova, V.; Fernández–Martínez, M.; Sánchez-Granero, M.A.; Segovia, Juan Evangelista Trinidad

doi:10.1371/journal.pone.0219243

Cited by 42 publications

(30 citation statements)

References 42 publications

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“…And later, Baur et al, Kapar et al and Fassas et al [ 27 – 29 ] provided empirical evidence on the linkages between Bitcoin spot and futures. Dimitrova et al [ 30 ] investigated the efficiency of the Bitcoin market and pointed out the existence of anti-persistent memory in the BTC-USD series. A recent study done by Nikolova et al [ 31 ] further pointed out that the volatility in cryptocurrencies changed faster than in traditional assets, and much faster than in forex pairs.…”

Section: Literature Reviewmentioning

confidence: 99%

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Zhu

Zhang

et al. 2021

PLoS ONE

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This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.

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Section: Literature Reviewmentioning

confidence: 99%

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Zhu

Zhang

et al. 2021

PLoS ONE

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“…Results showed that the past values of the variance of cryptocurrencies have the greatest effect on the current variance, and that cryptocurrencies have an exploding variance forecast. Recently, Nikolova, Trinidad Segovia, Fernández-Martínez & Sánchez-Granero [ 12 ] and Dimitrova, Fernández-Martinez, Sánchez-Granero & Trinidad Segovia [ 13 ] analyzed the bitcoin stylized facts related to volatility.…”

Section: Introductionmentioning

confidence: 99%

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

2021

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This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies’ volatility and revealed persistence and “intensifying” levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.

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“…On this basis, to explore the presence of market memory is an alternative method to test the market efficiency. Relevant contributions are Beben and Orłowski [26], DiMatteo et al [27], Zunino et al [28], Cajueiro [29], Kristoufek [30], Ferreira et al [31], Kristoufek [32] and Dimitrova et al [33].…”

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confidence: 99%

Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency

et al. 2021

Self Cite

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In this paper, we use a statistical arbitrage method in different developed and emerging countries to show that the profitability of the strategy is based on the degree of market efficiency. We will show that our strategy is more profitable in emerging ones and in periods with greater uncertainty. Our method consists of a Pairs Trading strategy based on the concept of mean reversion by selecting pair series that have the lower Hurst exponent. We also show that the pair selection with the lowest Hurst exponent has sense, and the lower the Hurst exponent of the pair series, the better the profitability that is obtained. The sample is composed by the 50 largest capitalized companies of 39 countries, and the performance of the strategy is analyzed during the period from 1 January 2000 to 10 April 2020. For a deeper analysis, this period is divided into three different subperiods and different portfolios are also considered.

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Some comments on Bitcoin market (in)efficiency

Cited by 42 publications

References 42 publications

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Investor attention and cryptocurrency: Evidence from the Bitcoin market

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency

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