Coverage ratio is cumulative revenue divided by cumulative cost, the primary viability metric in a fee-versus-cost simulation. A ratio above 1.0 means revenue has outpaced cost. Read across percentiles rather than at the median, it reveals how often a modeled economy is underwater.
A median coverage of 1.4 means nothing if the 10th-percentile path sits at 0.6. That design is insolvent in roughly one in ten plausible scenarios.
How it is calculated
Coverage ratio is cumulative protocol revenue divided by cumulative operating cost over the same period. Above 1.0, revenue has outpaced cost. Below 1.0, the protocol is drawing down reserves to keep running. The cumulative framing matters: a protocol can run monthly deficits for a long stretch and still post a ratio above 1.0 if early surpluses were large enough to absorb the shortfall.
In a Monte Carlo context, the ratio is computed for every simulated path and reported as a distribution, usually across the 5th, 25th, 50th, 75th, and 95th percentiles. Reporting the median alone conceals exactly the risk that matters most.
Design consequence
Coverage ratio drives treasury sizing. If the 10th-percentile path drops below 1.0 for an extended stretch, the design needs a reserve large enough to bridge that gap without triggering emergency token issuance. The simulation converts that abstract risk into a concrete reserve requirement, expressed in months of runway at the relevant cost rate.
Example
A protocol with strong creation-fee revenue might show a coverage ratio of 2.1 at the end of year one, then 0.85 at the end of year three as fee volume normalizes and infrastructure costs accumulate. The simulation surfaces this trajectory across all paths, not just the expected one, letting the team decide whether to adjust fees, cut the cost base, or hold more reserves before the deficit window arrives.
Common mistake
Reading the ratio at a single point in time rather than as a time series across the full horizon. A ratio above 1.0 today tells you nothing about whether the model stays solvent in year three. Track the trajectory across the simulation's full horizon and flag every path that crosses below 1.0, along with how long it stays there.
See Tokenomics Audit for how this applies in practice.
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