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Stablecoin Repayment Spikes as Signals of Ethereum Volatility

Article Amberdata research
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Summary

This analysis studies whether lending activity in USDC, USDT, and DAI can help anticipate Ethereum volatility. It examines lagged repayments, withdrawals, and borrowing, then defines shock days as days when repayment counts exceed each stablecoin’s historical 95th percentile. An event window compares ETH volatility over the three days before and after those days.

Across all three stablecoins, recent repayment counts show positive correlations with next-day volatility, with weaker relationships at longer lags. The report also describes volatility rising into repayment shock days and easing afterward; for USDC and USDT, it gives 55 shock days each and volatility observations across the event window. These are correlations and event-window patterns, not evidence that repayment activity causes volatility or a tested trading strategy. The report flags intercorrelated activity measures as a modeling concern, and its supplied text is incomplete in the USDT shock-day discussion and does not fully show the DAI event results.

Key ideas

  • Recent repayment counts in USDC, USDT, and DAI are positively correlated with next-day ETH volatility.
  • The reported correlations generally weaken as the activity data become older.
  • Shock days are defined separately for each stablecoin using a 95th-percentile repayment threshold.
  • ETH volatility rises into repayment shock days and, in the reported USDC results, declines over the following days.
  • Correlated activity variables may create multicollinearity, and the analysis does not establish causation.

Tags

CryptoDeFiVolatilityStatisticsRisk management

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.