We examine whether Korean credit spreads are informative enough to help improve the predictive accuracy of Korean government bond yields. To do this, we analyze a joint dynamic Nelson-Siegel (DNS) model of Korean government bond yields and credit spreads. In the model multiple change-points at unknown time points in the factor process are allowed in order to capture the possibility of structural breaks in the yield and credit spread curve dynamics. We find that the joint DNS model of the yield and credit spread curves outperforms the standard DNS model of the yield curve in terms of out-of-sample yield curve prediction. Further, the predictive gains are maximized at the two change-points. The two change-points seem to be closely associated with the beginning of the recent financial crisis and the subsequent stabilization of Korean bond markets.
- Bayesian MCMC simulation
- Dynamic Nelson-Siegel model
- Out-of-sample forecasting
- Posterior predictive criterion
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