EconPapers    
Economics at your fingertips  
 

A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US

Fausto José Araújo Vieira, Fernando Chague () and Marcelo Fernandes ()

No 445, Textos para discussão from FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil)

Abstract: This paper proposes a Factor-Augmented Dynamic Nelson-Siegel (FADNS) model to predict the yield curve in the US that relies on a large data set of weekly financial and macroeconomic variables. The FADNS model significantly improves interest rate forecasts relative to the extant models in the literature. For longer horizons, it beats autoregressive alternatives, with a reduction in mean absolute error of up to 40%. For shorter horizons, it offers a good challenge to autoregressive forecasting models, outperforming them for the 7- and 10-year yields. The out-of-sample analysis shows that the good performance comes mostly from the forward-looking nature of the variables we employ. Including them reduces the mean absolute error in 5 basis points on average with respect to models that reflect only past macroeconomic events.

Date: 2017
New Economics Papers: this item is included in nep-dcm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://bibliotecadigital.fgv.br/dspace/bitstream/1 ... Fernando_Marcelo.pdf (application/pdf)

Related works:
Working Paper: A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US (2016) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fgv:eesptd:445

Access Statistics for this paper

More papers in Textos para discussão from FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil) Contact information at EDIRC.
Bibliographic data for series maintained by Núcleo de Computação da FGV EPGE ().

 
Page updated 2020-09-17
Handle: RePEc:fgv:eesptd:445