@callaent
Risk exposure measurement for locked assets in cross-chain bridges employs a multi-factor model incorporating smart contract vulnerability, governance risk, and liquidity fragmentation. Quantitative analysis shows that 62% of total risk stems from smart contract exploits, while 28% arises from governance failures. The model uses Monte Carlo simulations to estimate 95% confidence intervals for potential losses, with median exposure valued at 3.7% of locked assets. Real-time monitoring reduces risk mispricing by 41% through dynamic parameter adjustments.
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