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Forecasting Cryptocurrency Staking Rewards

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Listed:
  • Sauren Gupta
  • Apoorva Hathi Katharaki
  • Yifan Xu
  • Bhaskar Krishnamachari
  • Rajarshi Gupta

Abstract

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.

Suggested Citation

  • Sauren Gupta & Apoorva Hathi Katharaki & Yifan Xu & Bhaskar Krishnamachari & Rajarshi Gupta, 2024. "Forecasting Cryptocurrency Staking Rewards," Papers 2401.10931, arXiv.org.
    • Handle: RePEc:arx:papers:2401.10931
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    References listed on IDEAS

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    1. Jiahua Xu & Yebo Feng, 2022. "Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols," Papers 2210.04194, arXiv.org, revised Dec 2022.
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