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 33 publications

(26 citation statements)

References 42 publications

(44 reference statements)

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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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“…Some of the authors analyzed the Bitcoin market efficiency by applying different approaches, including the Hurst exponent (cf. [30] for a detailed review), whereas others investigated its volatility using other methods. For instance, Letra [31] used a GARCH model for Bitcoin daily data; Bouoiyour and Selmi [32] carried out many extensions of GARCH models to estimate Bitcoin price dynamics; Bouri, Azzi, and Dyhberg [33] analyzed the relation between volatility changes and price returns of Bitcoin based on an asymmetric GARCH model; Balcilar et al [34] analyzed the relation between the trading volume of Bitcoin and its returns and volatility by employing, in contrast, a non-parametric causality in quantiles test; and Baur et al [35] studied the statistical properties of Bitcoin and its relations with traditional asset classes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets

et al. 2020

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One of the main characteristics of cryptocurrencies is the high volatility of their exchange rates. In a previous work, the authors found that a process with volatility clusters displays a volatility series with a high Hurst exponent. In this paper, we provide a novel methodology to calculate the probability of volatility clusters with a special emphasis on cryptocurrencies. With this aim, we calculate the Hurst exponent of a volatility series by means of the FD4 approach. An explicit criterion to computationally determine whether there exist volatility clusters of a fixed size is described. We found that the probabilities of volatility clusters of an index (S&P500) and a stock (Apple) showed a similar profile, whereas the probability of volatility clusters of a forex pair (Euro/USD) became quite lower. On the other hand, a similar profile appeared for Bitcoin/USD, Ethereum/USD, and Ripple/USD cryptocurrencies, with the probabilities of volatility clusters of all such cryptocurrencies being much greater than the ones of the three traditional assets. Our results suggest that the volatility in cryptocurrencies changes faster than in traditional assets, and much faster than in forex pairs.

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Some comments on Bitcoin market (in)efficiency

Cited by 33 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

A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets

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