Global Term Structure Modeling Using Principal Component Analysis

Global Term Structure Modeling Using Principal Component Analysis Arcady Novosyolov and Daniel Satchkov Abstract Principal Component Analysis (PCA) is a technique commonly applied to the interest rate markets to describe yield curve dynamics in a parsimonious manner. Despite an increase in global investing and the growing interconnectedness of the international markets, PCA has not been widely applied to decomposing joint structure of global yield curves. Our objective is to describe the joint structure with a model that can potentially be used for scenario analysis and for estimating the risk of interest rate sensitive portfolios. In this study, we examine three variations of the PCA technique to decompose global yield curve and interest rate implied volatility structure. We conclude that global yield curve structure can be described with 15 to 20 factors, while implied volatility structure requires at least 20 global factors. The procedure that we identify as preferable is a two-step PCA, with local curves decomposed in the first step and combined local principal components decomposed into a joint structure (PCA of principal components) in the second step. This procedure has a key advantage in that it makes any scenario analysis more meaningful by keeping local PCA factors, which have important economic interpretations as shift, twist and butterfly moves of the yield curve.