CHALLENGE 03 · INTERACTIVE SIMULATION
Tokens are divisible. Liquidity isn’t automatic.
A fictional $10m asset pool faces a wave of exit requests. Explore what can actually turn a token back into cash.
Scenario levers
Fixed pool: $10m including cash. Trading is matched at assumed NAV, with no fees or price impact. Reserves fund only the remaining requests.
Follow the exit demand
Asset pool
$10.00m
Tokens
100,000
NAV / token
$100.00
Requested exits
$2.00m this window
All requested exits fit within this window. Increasing token count alone will not improve that capacity; increasing the cash reserve reduces the amount invested.
Apply a shock
No shock applied. Adjust a lever to explore the relationship.
Trade-off to watch
Cash supports exits, but each extra reserve dollar is a dollar outside the underlying investment.
Scenario versus baseline
Baselines stay fixed at the starting settings with no shock, so every change you make is measured against the same reference.
Exit capacity coverage
100.0%
Baseline 100.0%
Unfilled requests
$0.00m
Baseline $0.00m
Cash paid in redemptions
$1.00m
Baseline $1.00m
Model notes and assumptions
Assumptions: A fixed $10m pool, one settlement window, a single class of tokens, sufficient redemption permissions, no fees, and trades at net asset value (NAV). Buying capital is independent of token count. Available cash is part of the $10m pool, not extra capital.
Requests = $10m × exit percentage; matched trades = min(requests, buying capital); redemptions = min(unmatched requests, cash reserve); queued = requests − trades − redemptions.
The surge doubles the selected exit percentage, capped at 100%. Buyers retreat removes 75% of selected buying capital. Coverage is 100% when there are no requests. This model illustrates capacity, not executable market prices; it does not estimate discounts, spreads or future demand.
Research basis: “RWA Tokenization Market Hurdles,” the research report reproduced on the overview page. This lab translates its qualitative themes into teaching scenarios; it does not treat the report’s market statistics or case-study claims as verified model coefficients. The operational chapter is split into liquidity and data freshness; regulation and expertise are combined in one explainer so all six research themes remain covered.
Reading the results: Inputs and shock magnitudes are illustrative assumptions. Outputs are arithmetic under those assumptions. Baselines are the initial input settings with no shock. No overall risk score, probability of loss, investment recommendation or legal determination is produced.