dc.contributor |
Universitat Rovira i Virgili. Departament d'Economia |
dc.contributor.author |
Aslanidis, Nektarios |
dc.contributor.author |
Cipollini, Andrea |
dc.date.accessioned |
2009-05-19T15:31:10Z |
dc.date.available |
2009-05-19T15:31:10Z |
dc.date.created |
2009 |
dc.date.issued |
2009 |
dc.identifier.issn |
ISSN 1988 - 0812 |
dc.identifier.other |
T - 352 - 2009 |
dc.identifier.uri |
http://hdl.handle.net/2072/15810 |
dc.format.extent |
30 |
dc.format.extent |
474159 bytes |
dc.format.mimetype |
application/pdf |
dc.language.iso |
eng |
dc.relation.ispartofseries |
Documents de treball del Departament d'Economia;2009-02 |
dc.rights |
Aquest document està subjecte a una llicència d'ús de Creative Commons, amb la qual es permet copiar, distribuir i comunicar públicament l'obra sempre que se'n citin l'autor original, la universitat i el departament i no se'n faci cap ús comercial ni obra derivada, tal com queda estipulat en la llicència d'ús (http://creativecommons.org/licenses/by-nc-nd/2.5/es/) |
dc.subject.other |
Sèries temporals--Anàlisi |
dc.subject.other |
Previsió econòmica--Models economètrics |
dc.subject.other |
Cicles econòmics |
dc.subject.other |
Processament de dades en temps real |
dc.subject.other |
Crèdit |
dc.title |
Leading indicator properties of US high-yield credit spreads |
dc.type |
info:eu-repo/semantics/workingPaper |
dc.subject.udc |
338 - Situació econòmica. Política econòmica. Gestió, control i planificació de l'economia. Producció. Serveis. Turisme. Preus |
dc.description.abstract |
In this paper we examine the out-of-sample forecast performance of high-yield credit
spreads regarding real-time and revised data on employment and industrial production
in the US. We evaluate models using both a point forecast and a probability forecast
exercise. Our main findings suggest the use of few factors obtained by pooling
information from a number of sector-specific high-yield credit spreads. This can be
justified by observing that, especially for employment, there is a gain from using a
principal components model fitted to high-yield credit spreads compared to the
prediction produced by benchmarks, such as an AR, and ARDL models that use either
the term spread or the aggregate high-yield spread as exogenous regressor. Moreover,
forecasts based on real-time data are generally comparable to forecasts based on revised
data.
JEL Classification: C22; C53; E32
Keywords: Credit spreads; Principal components; Forecasting; Real-time data. |