Interoperability Effects: Extending DeFi Lending Risk Models to Multi-Chain Environments
For DeFi risk managers and protocol designers, this paper provides the first empirical evidence that cross-chain metrics must be incorporated into lending risk models, though the findings are incremental as they apply existing econometric methods to a new domain.
This study empirically analyzes how cross-chain interoperability affects DeFi lending protocol performance using panel regression on data from 15 protocols and 53 bridges across 9 blockchains (Oct 2022–Jan 2025). Results show bridge volume significantly impacts TVL and revenue, but increased bridge integrations reduce both, indicating liquidity outflows; bridge hacks surprisingly correlate with increased performance.
On-chain lending has expanded across multiple distributed ledgers as DeFi becomes increasingly multi-chain. This environment introduces novel technical and financial mechanisms, particularly cross-blockchain communication and asset transfer protocols, yet cross-chain elements remain understudied in lending protocol risk management. To address this gap, we applied panel regression fixed effects and OLS models to empirically analyze cross-blockchain interoperability solutions, using TVL and total revenue as performance proxies from October 2022 to January 2025. Our data set covers 15 decentralized lending protocols and 53 cross-chain bridges across 9 EVM-compatible blockchains, categorized as Ethereum, alternative layer-1s, and Ethereum layer-2 networks. Results reveal that cross-chain activity impacts on protocol performance. Bridge volume emerges as a critical driver, exerts a significant effect on TVL and revenue across different categories, though the direction of this effect varies heterogeneously. Increased bridge integrations are associated with decreased TVL and protocol revenue across categories, indicating liquidity escapes from those lending ecosystems. Liquidations produce heterogeneous effects across categories. New network launches do not have as significant relationships with TVL and revenue while bridge hacks show a significant and positive relationship. High R-squared values confirm meaningful explanatory power. We further show Ethereum attracts large depositors, while layer-2s skew toward retail participation. We conclude that effective DeFi risk models should incorporate cross-chain metrics and adopt a layer-aware approach to accurately reflect the evolving multi-chain landscape.