Estimating U.S. Treasury Yield Curves By A Generalized Optimization Framework
(English)Manuscript (preprint) (Other academic)
We show that traditional data sets for the U.S. Treasury yield curves contain large amounts of noise, in e.g. the Fama-Bliss discount file already the second factor loading for innovations in forward rates is a consequence of noise. We implement the quadratic and cubic McCulloch splines, Nelson-Siegel and Svensson models and compare these traditional models with a recently developed generalized optimization framework using daily CRSP data from 1961 to 2011. In out-of-sample tests, it is shown that the generalized optimization framework produces smaller pricing errors compared to the traditional methods. Factor loadings from the generalized optimization framework show that the short and long end of the forward rate curve move independently, where principal component 1-3 explain the long end, and subsequent principal components explain the short end. This is consistent with the behavior of the market where short rates are governed by central bank while long rates are dependent on e.g. the expectation of future inflation.
structure estimation, U.S. Treasury, Principal component analysis, Forward rates
IdentifiersURN: urn:nbn:se:liu:diva-97407OAI: oai:DiVA.org:liu-97407DiVA: diva2:647654