Stochastic modeling of yield curve shifts usingfunctional data analysis

Abstract: This thesis approaches the problem of modeling the multivariate distribution of interest rates by implementing a novel tool of statistics known as functional data analysis (FDA). This is done by viewing yield curve shifts as distinct but continuous stochastic objects defined over a continuum of maturities. Based on these techniques, we provide two stochastic models with different assumptions regarding the temporal dependence of yield curve shifts and compare their performance with empirical data. The study finds that both models replicate the distributions of yield changes with medium- and long-term maturities, whereas none of the models perform satisfactory at the short segment of the yield curve. Both models, however, appear to accurately capture the cross-sectional dependence.