Forecasting Cryptocurrency Staking Rewards
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911761840472064 |
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| author | Gupta, Sauren Katharaki, Apoorva Hathi Xu, Yifan Krishnamachari, Bhaskar Gupta, Rajarshi |
| author_facet | Gupta, Sauren Katharaki, Apoorva Hathi Xu, Yifan Krishnamachari, Bhaskar Gupta, Rajarshi |
| contents | This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors. We investigate two predictive methodologies: a) a straightforward sliding-window average, and b) linear regression models predicated on historical data. The findings reveal that ETH staking rewards can be forecasted with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively, using a 7-day sliding-window average approach. Additionally, we discern diverse prediction accuracies across various cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is identified as superior to the moving-window average for perdicting in the short term for XTZ and ATOM. The results underscore the generally stable and predictable nature of staking rewards for most assets, with MATIC presenting a noteworthy exception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forecasting Cryptocurrency Staking Rewards Gupta, Sauren Katharaki, Apoorva Hathi Xu, Yifan Krishnamachari, Bhaskar Gupta, Rajarshi Statistical Finance Cryptography and Security Machine Learning This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors. We investigate two predictive methodologies: a) a straightforward sliding-window average, and b) linear regression models predicated on historical data. The findings reveal that ETH staking rewards can be forecasted with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively, using a 7-day sliding-window average approach. Additionally, we discern diverse prediction accuracies across various cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is identified as superior to the moving-window average for perdicting in the short term for XTZ and ATOM. The results underscore the generally stable and predictable nature of staking rewards for most assets, with MATIC presenting a noteworthy exception. |
| title | Forecasting Cryptocurrency Staking Rewards |
| topic | Statistical Finance Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2401.10931 |