Forecasting Cryptocurrency Staking Rewards
Sauren Gupta Apoorva Hathi Katharaki Yifan Xu Bhaskar Krishnamachari Rajarshi Gupta
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.