It is well known that long-term interest rates display a lower volatility than short-term interest rates. The imperfect correlation between changes in long-term and short-term interest rates is also well documented. Simple immunization techniques based on duration and convexity do not take such factors into account. This paper suggests an approach which considers unequal volatility and imperfect correlation when building immunizing portfolios. Our results suggest that this approach may lead to significant improvements over both duration-based and convexity-based immunization strategies. Volatility-adjusted and correlation-adjusted immunization also outperform strategies based on factor analysis of interest rates changes.

We test alternative models of yield curve risk by hedging US Treasury bond portfolios through note/bond futures. We show that traditional implementations of models based on principal component analysis, duration vectors and key rate duration lead to high exposure to model errors and to sizable transaction costs, thus lowering the hedging quality. Also, this quality randomly varies from one model and hedging problem to the other. We show that accounting for the variance of modeling errors substantially reduces both hedging errors and transaction costs for all considered models. Additionally, it leads to much more stable weights in the hedging portfolios and – as a result – to more homogeneous hedging quality. On this basis, error-adjusted principal component analysis is found to systematically and significantly outperform alternative models.