Premium
Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model
Author(s) -
Exterkate Peter,
Dijk Dick Van,
Heij Christiaan,
Groenen Patrick J. F.
Publication year - 2013
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1258
Subject(s) - ranking (information retrieval) , econometrics , yield curve , principal component analysis , macro , model selection , factor analysis , multivariate statistics , selection (genetic algorithm) , dynamic factor , computer science , yield (engineering) , statistics , mathematics , economics , interest rate , machine learning , materials science , metallurgy , programming language , monetary economics
This paper compares various ways of extracting macroeconomic information from a data‐rich environment for forecasting the yield curve using the Nelson–Siegel model. Five issues in extracting factors from a large panel of macro variables are addressed; namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data‐driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. Factor‐augmented methods perform well in relatively volatile periods, including the crisis period in 2008–9, when simpler models do not suffice. The macroeconomic information is exploited best by partial least squares methods, with principal component methods ranking second best. Reductions of mean squared prediction errors of 20–30% are attained, compared to the Nelson–Siegel model without macro factors. Copyright © 2011 John Wiley & Sons, Ltd.