Estimation of the sovereign yield curve of Peru : the role of macroeconomic and latent factors
The study of the yield curve has been a topic that interested economists for a long time since the term structure of interest rates is an important transmission channel of monetary policy to inflation and real activity. In this paper, following Ang and Piazzesi (2003), we study the relevance of macr...
| Autor Principal: | Olivares Ríos, Alejandra |
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| Formato: | Tesis de maestría |
| Idioma: | Inglés |
| Publicado: |
Pontificia Universidad Católica del Perú
2017
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| Materias: | |
| Acceso en línea: |
http://repositorio.pucp.edu.pe/index/handle/123456789/156509 |
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| Sumario: |
The study of the yield curve has been a topic that interested economists for a long time since
the term structure of interest rates is an important transmission channel of monetary policy to
inflation and real activity. In this paper, following Ang and Piazzesi (2003), we study the
relevance of macroeconomic factors on Peruvian sovereign yield curve through an Affine Term
Structure model for the period from November 2005 to December 2015. We estimate a
Gaussian model to understand the joint dynamics of macro variables -inflation and real activity
factors- and Peruvian bond yields in a multifactor model of the term structure. Risk premia are
modeled as time varying and depend on both observable and unobservable factors. A Vector
Autoregressive (VAR) model is estimated considering no-arbitrage assumptions, which let us to
derive Impulse Response Functions and Variance Decompositions. We find evidence that macro
factors help to improve the fit of the model and explain a substantial amount of variation in
bond yields. Variance decompositions show that macro factors explain a significant amount of
the movements in the short and middle segments of the yield curve (up to 50%) while
unobservable factors are the main drivers for most of the movements at the long end of the
yield curve (up to 80%). Furthermore, we find that setting no-arbitrage restrictions improve the
forecasting performance of a VAR and that models that include macro factors forecast better
than models with only unobservable components. |
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