Valuation frameworks for crypto assets
Assets that require custom classification before a valuation model can be applied responsibly.
The model set is selected for the economic behavior of this asset class.
Static class view of valuation gap and evidence quality.
Other Bubble Map
Static methodology preview. Live data pipeline not yet connected.
Each model states what it measures and where its confidence breaks down.
Flags assets that need classification before model selection.
Core inputs
Forcing a model too early creates false precision.
Maps a niche asset to the closest valuation class or hybrid.
Core inputs
Hybrid assets may require several model families.
Assesses whether a niche asset has enough durable liquidity for valuation work.
Core inputs
Poor liquidity can dominate all other model outputs.
Reviews whether token utility has economic relevance.
Core inputs
Utility without capture may not support token-level value.
Method confidence reflects evidence quality, source availability, and token-level capture clarity.
A local assumption shell for testing scenario sensitivity without live data.
Scenario sensitivity
Static methodology preview. Changes here recompute a local scenario number only.
Planned source families for turning this framework into a data-backed dashboard.
Assets currently mapped to this valuation class.