Forecasting the Yield Curve in a Data-Rich Environment using the Factor-Augmented Nelson-Siegel Model
2010-02-23
Research Paper
pp 1-40.
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Various ways of extracting macroeconomic information from a data-rich environment are compared with the objective of forecasting yield curves using the Nelson-Siegel model. Five issues in factor extraction 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. The data-driven methods perform well in relatively volatile periods, when simpler models do not suffice.
Keywords
Automatically Extracted Terms
- factor
- method
- model
- variable
- macro
- macro factors
- forecast
- number
- forecasting
- maturity
- diebold
- curve
- regression
- group
- fans model
- table
- information
- parameter
- month
- yield
