Information Transmission Between Cryptocurrencies: Does Bitcoin Rule the Cryptocurrency World?

Bação, Pedro; Duarte, António Portugal; Sebastião, Hélder; Redžepagić, Srdjan

doi:10.2478/saeb-2018-0013

Cited by 42 publications

(24 citation statements)

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“…For instance, Ji et al ( 2019a ) show that litecoin and bitcoin are at the center of returns and volatility connectedness, and that while bitcoin is the most influential cryptocurrency in terms of volatility spillovers, ethereum is a recipient of spillovers; thus, it is dominated by both larger and smaller cryptocurrencies. Bação et al ( 2018 ) show that lagged information transmission occurs mainly from litecoin to other cryptocurrencies, especially in their last subsample (August 2017–March 2018), and Tran and Leirvik ( 2020 ) conclude that, on average, in the period 2017–2019, litecoin was the most efficient cryptocurrency.…”

Section: Resultsmentioning

confidence: 99%

“…The overall input set is formed by 50 variables, most of them coming from the raw data after some transformation. This set includes the log returns of the three cryptocurrencies lagged one to seven days earlier (the returns of cryptocurrencies are highly interdependent at different frequencies, as shown in Bação et al 2018 ; Omane-Adjepong and Alagidede 2019 ; and Hyun et al 2019 ) and two proxies for the daily volatility, namely the relative price range, , and the range volatility estimator of Parkinson ( 1980 ), , computed respectively as: where and are the highest and lowest prices recorded at day t . More precisely, the set includes the first lag of and lags one to seven of (for other applications of the Parkinson estimator to cryptocurrencies see, for example, Sebastião et al 2017 and Koutmos 2018 ).…”

Section: Data and Preliminary Analysismentioning

confidence: 99%

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Forecasting and trading cryptocurrencies with machine learning under changing market conditions

2021

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This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Data and Preliminary Analysismentioning

confidence: 99%

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

2021

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“…For example, Ciaian, Rajcaniova, and Kancs (2015) propose VECM specifications for Bitcoin prices as a suitable methodology to account for potential endogeneity between the variables, see Lütkepohl and Krätzig (2014). Other recent studies exploiting the cointegration methodology to investigate the long‐run relationship between Bitcoin and other cyrptocurrencies are Bação, Duarte, Sebastião, and Redzepagic (2018), Ciaian, Rajcaniova, and Kancs (2018), Leung and Nguyen (2019) and Van den Broek (2018).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Bitcoin prices using market and sentiment variables

Kapar

Olmo

2020

World Economy

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There is an emerging empirical stream of literature that has attempted to explain the mechanisms behind cryptocurrency pricing and, in particular, Bitcoin. This paper belongs to this recent tradition; however, in contrast to most empirical studies on the dynamics of the cryptocurrency, we focus on uncovering its efficient price. Informally, the efficient price can be interpreted as an average price that reflects the long-run dynamics of the price after removing short-term fluctuations due to market noise, outliers or the occurrence of other transitory disruptions in the market. The contribution of this paper is twofold. First, we identify the long-run relationship between Bitcoin and a set of variables with power to explain the dynamics of the cryptocurrency. This is done applying cointegration methods. If Bitcoin is cointegrated with any of the other variables considered, it implies that there is a long-run relationship between them. This can be used by investors to make strategic trading decisions. When there is a deviation from the long-run equilibrium, investors can act on that in the belief that it will return to the long-run equilibrium. The second contribution is to explore the factors with ability to explain the efficient Bitcoin price. More formally, we define the efficient Bitcoin price as the permanent component of the cryptocurrency log-price obtained from the permanent-transitory (P-T) decomposition proposed in Gonzalo and Granger (1995). To the best of our knowledge, this is the first paper that identifies the efficient price of Bitcoin by applying the permanent-transitory (P-T) decomposition proposed by these authors. Our empirical strategy is based on estimating a vector error correction model (VECM), see Johansen (1988, 1991), to assess the long-run relationship between the Bitcoin spot price and a set of financial, economic and sentiment variables. Motivated by the empirical literature exploring the determinants of Bitcoin and also by the properties of cryptocurrencies, we entertain the following 46 | KAPAR And OLMO variables: (a) the S&P 500 index capturing the performance of financial markets, (b) the price of gold, a commodity with investment properties similar to those characterising Bitcoin, (c) Google Search as a variable that quantifies the entries in the Google engine associated to Bitcoin, in particular, and cryptocurrencies, in general, and (d) a fear index proxied by the FED Financial Stress Index that captures market sentiment. The VECM specification is applied to two overlapping periods characterised by very different dynamics. A first period considering data up to January 2018, and a second period that considers data up to May 2019 characterised by a steep decline in the Bitcoin price and a slow recovery. The findings of our empirical study are in stark contrast across periods. For the first period, we find strong positive comovements between the dynamics of Bitcoin price and online interest in the cryptocurrency measured by Google searches. The other factor with a positive relatio...

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“…Tables 2 to 5 present the VEC results and simultaneous equations that analyze the effects of all cryptocurrencies on all other ones traded in USD. Among the recent empirical studies, Bação et al (2018), Smith (2015, and Huynh (2019) used variants of the vector autoregressive model, whereas Gandal and Halaburda (2016) employed the ordinary least squares (OLS) regression technique to examine interactions between cryptocurrencies. The results provide additional details and insights that could help to explain the degree of substitution and reinforcement effects in each market over time.…”

Section: Correlations Analysis Tablementioning

confidence: 99%

Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?

et al. 2021

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We analyzed cryptocurrency dynamics in the global U.S. dollar–denominated market and the emerging market economies (EMEs) with a view to ascertaining whether activities in these markets are predominantly shaped by reinforcement or substitution effect. Cryptocurrencies analyzed include the Bitcoins, Ethereum, Litecoin, Steller, Bitcoin Cash, and USD Tether. The results suggest that, on average, correlation between digital assets in the cryptocurrencies’ ecosystem is positive. However, there is evidence of an outlier with respect to the USD Tether (USDT) in the global market, revealing that the USDT is negatively associated with all other cryptocurrencies. This is supported by the dynamic regression results that provided evidence of reinforcement effect in favor of the USDT in the global crypto market, thus confirming the status of the USDT as “Stablecoin” as it is pegged 1:1 to USD. In the global market context, the results also revealed that USDT/USD returns had identical outliers that could portend lesser chances of extreme gains or losses compared with suggestions of extreme gains or losses in the EMEs. Furthermore, USDT did not seem to have similar evolution in the EMEs where it had relatively marginal influence in the markets. The vector error correction (VEC) estimate showed mixed results between Altcoins in all the markets; moreover, our finding showed that reinforcement effects hold in favor of Steller (XLM) both in the Russian ruble and Indian rupee crypto markets, whereas the Chinese yuan crypto market was predominantly characterized by substitution effect in favor of Bitcoin.

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Information Transmission Between Cryptocurrencies: Does Bitcoin Rule the Cryptocurrency World?

Cited by 42 publications

References 45 publications

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Analysis of Bitcoin prices using market and sentiment variables

Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?

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