Leading indicator properties of US high-yield credit spreads
In this paper we examine the out-of-sample forecast performance of high-yield credit spreads for real-time and revised data regarding employment and industrial production in the US. We evaluate models using both a point forecast and a probability forecast exercise. Our main findings suggest that the best results come from using only a few factors obtained by pooling information from a number of sector-specific high-yield credit spreads. In particular, for employment and at short-run horizons, there is a gain from using a principal components model fitted to high-yield credit spreads compared to the prediction produced by benchmarks. Moreover, forecast results based on revised data are qualitatively similar to those obtained using real-time data.
Year of publication: |
2010
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Authors: | Aslanidis, Nektarios ; Cipollini, Andrea |
Published in: |
Journal of Macroeconomics. - Elsevier, ISSN 0164-0704. - Vol. 32.2010, 1, p. 145-156
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Publisher: |
Elsevier |
Keywords: | Credit spreads Principal components Forecasting Real-time data |
Saved in:
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