Estimation of the sovereign yield curve of Peru : the role of macroeconomic and latent factors

• Autor: Olivares Ríos, Alejandra
• Formato: tesis de maestría
• Fecha de Publicación: 2016
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/156509
• Enlace del recurso: http://hdl.handle.net/20.500.12404/8049
• Nivel de acceso: acceso abierto
• Materia: Macroeconomía--Medición--Perú; Macroeconomía--Tasa de rendimiento; Política monetaria; Crecimiento económico; https://purl.org/pe-repo/ocde/ford#5.02.01

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.