2023-10-26-美联储-衡量金融机构的利率风险管理_68页_1mb

Measuring Interest Rate Risk by Financial Institutions Celso Brunetti, Nathan Foley-Fisher, Stéphane Verani (2023) proposes a novel method to assess financial intermediaries' residual interest rate risk exposure using high-frequency financial market data. The methodology utilizes realized gamma—a daily estimate derived from high-frequency data—to measure sensitivity of stock returns to changes in interest rates. Subsampling is employed for statistically valid inference, offering robustness to weak assumptions.

Applied primarily to U.S. life insurers, the method reveals effective interest rate risk management strategies, generally keeping stock prices uncorrelated with long-term Treasury rates. However, life insurers exhibit higher sensitivity, particularly to long-term rate changes, compared to property and casualty insurers. Term premium dynamics are identified as a key factor explaining this differential sensitivity. The paper underscores that statistical significance hinges on theoretical innovations, not volatility levels. This approach enhances monitoring of intermediaries' financial stability for supervisors and policymakers.