Yield Curve Risk Factors: Domestic And Global Contexts

CONTENTS Introduction: Handling multiple risk factors 1. Methodological introduction 2. Non-parallel risk, duration bucketing and partial durations 3. Limitations of key rate duration analysis I Principal component analysis 1. Definition and examples from US Treasury market 2. Meaningfulness of factors: dependence on dataset 3. Correlation structure and other limitations of the approach II International bonds 1. Principal component analysis for international markets 2. Co-movements in international bond yields 3. Correlations: between markets, between yield and volatility III Practical implications 1. Risk management for a leveraged trading desk 2. Total return management and benchmark choice 3. Asset/liability management and the use of risk buckets Appendix: Economic factors driving the curve 1. Macroeconomic explanation of parallel and slope risk 2. Volatility shocks and curvature risk 3. The short end and monetary policy distortions Literature guide and references 2 Traditional interest rate risk management focuses on duration and duration management. In other words, it assumes that only parallel yield curve shifts are important. In practice, of course, non-parallel shifts in the yield curve often occur, and represent a significant source of risk. What is the most efficient way to manage non-parallel interest rate risk? This chapter is mainly devoted to an exposition of principal component analysis, a statistical technique that attempts to provide a foundation for measuring non-parallel yield curve risk, by identifying the " most important " kinds of yield curve shift that empirically occur. The analysis turns out to be remarkably successful. It gives a clear justification for the use of duration as the primary measure of interest rate risk, and it also suggests how one may design " optimal " measures of non-parallel risk. Principal component analysis is a popular tool, not only in theoretical studies but in practical risk management applications. We discuss such applications at the end of the chapter. However, it is first important to understand that principal component analysis has limitations, and should not be applied blindly. In particular, it is important to distinguish between results that are economically meaningful, and results that are statistical artifacts without economic significance. There are two ways to determine whether the results of a statistical analysis are meaningful. The first is to see whether they are consistent with theoretical results; the Appendix gives a sketch of this approach. The second is simply to carry out as much exploratory data analysis as possible, with different data sets and different historical time …