Bitcoin and Main Altcoins: Causality and Trading Strategies
Using daily data from November 9, 2017 to December 31, 2022, this paper uses Granger causality in the mean and the distribution to investigate the transmission of information between return, volume, volatility, and illiquidity for Bitcoin and the nine most important altcoins in terms of market capi...
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| Format: | Article |
| Language: | English |
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Coimbra University Press
2025-08-01
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| Series: | Notas Económicas |
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| Online Access: | https://impactum-journals.uc.pt/notaseconomicas/article/view/16622 |
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| author | Soraia Santos Helder Sebastião Nuno Silva |
| author_facet | Soraia Santos Helder Sebastião Nuno Silva |
| author_sort | Soraia Santos |
| collection | DOAJ |
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Using daily data from November 9, 2017 to December 31, 2022, this paper uses Granger causality in the mean and the distribution to investigate the transmission of information between return, volume, volatility, and illiquidity for Bitcoin and the nine most important altcoins in terms of market capitalization. Additionally, the forecastability of Bitcoin returns is examined using linear models with different predictor spaces estimated using LASSO and the performance of several trading strategies devised upon those forecasts is assessed. The causal relationships between returns, volumes and volatilities of Bitcoin and each altcoin are more evident in the left tail of the distribution, where Bitcoin acts mostly as a transmitter of information, and in the right tail for causality regarding illiquidity. In bullish markets, Bitcoin acts mostly as a receiver of information. The best Bitcoin trading strategy is based on the model which incorporates the information on all cryptocurrencies, exhibiting a cumulative return of 331% and an annualized Sharpe ratio of 94.59%, considering an enter/exit threshold of 0.25% and after 0.5% round-trip transaction costs. These results are statistically significant when compared with the buy-and-hold strategy, which renders a cumulative return of 121% and a Sharpe ratio of 64.74%. These results point out the importance of considering information from other cryptocurrencies to forecast and trade on Bitcoin.
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| format | Article |
| id | doaj-art-97a721cbf0264b349b80cd632915d02c |
| institution | Kabale University |
| issn | 0872-4733 2183-203X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Coimbra University Press |
| record_format | Article |
| series | Notas Económicas |
| spelling | doaj-art-97a721cbf0264b349b80cd632915d02c2025-08-20T03:44:00ZengCoimbra University PressNotas Económicas0872-47332183-203X2025-08-015910.14195/2183-203X_59_1Bitcoin and Main Altcoins: Causality and Trading StrategiesSoraia Santos0Helder Sebastião1Nuno Silva2KPMGUniv Coimbra, CeBER, Faculty of EconomicsUniv Coimbra, CeBER, Faculty of Economics Using daily data from November 9, 2017 to December 31, 2022, this paper uses Granger causality in the mean and the distribution to investigate the transmission of information between return, volume, volatility, and illiquidity for Bitcoin and the nine most important altcoins in terms of market capitalization. Additionally, the forecastability of Bitcoin returns is examined using linear models with different predictor spaces estimated using LASSO and the performance of several trading strategies devised upon those forecasts is assessed. The causal relationships between returns, volumes and volatilities of Bitcoin and each altcoin are more evident in the left tail of the distribution, where Bitcoin acts mostly as a transmitter of information, and in the right tail for causality regarding illiquidity. In bullish markets, Bitcoin acts mostly as a receiver of information. The best Bitcoin trading strategy is based on the model which incorporates the information on all cryptocurrencies, exhibiting a cumulative return of 331% and an annualized Sharpe ratio of 94.59%, considering an enter/exit threshold of 0.25% and after 0.5% round-trip transaction costs. These results are statistically significant when compared with the buy-and-hold strategy, which renders a cumulative return of 121% and a Sharpe ratio of 64.74%. These results point out the importance of considering information from other cryptocurrencies to forecast and trade on Bitcoin. https://impactum-journals.uc.pt/notaseconomicas/article/view/16622CryptocurrenciesGranger causalityLASSOtrading strategies |
| spellingShingle | Soraia Santos Helder Sebastião Nuno Silva Bitcoin and Main Altcoins: Causality and Trading Strategies Notas Económicas Cryptocurrencies Granger causality LASSO trading strategies |
| title | Bitcoin and Main Altcoins: Causality and Trading Strategies |
| title_full | Bitcoin and Main Altcoins: Causality and Trading Strategies |
| title_fullStr | Bitcoin and Main Altcoins: Causality and Trading Strategies |
| title_full_unstemmed | Bitcoin and Main Altcoins: Causality and Trading Strategies |
| title_short | Bitcoin and Main Altcoins: Causality and Trading Strategies |
| title_sort | bitcoin and main altcoins causality and trading strategies |
| topic | Cryptocurrencies Granger causality LASSO trading strategies |
| url | https://impactum-journals.uc.pt/notaseconomicas/article/view/16622 |
| work_keys_str_mv | AT soraiasantos bitcoinandmainaltcoinscausalityandtradingstrategies AT heldersebastiao bitcoinandmainaltcoinscausalityandtradingstrategies AT nunosilva bitcoinandmainaltcoinscausalityandtradingstrategies |