Does realized volatility help bond yield density prediction?

Minchul Shin, Molin Zhong

Research output: Contribution to journalArticlepeer-review

Abstract

We suggest using “realized volatility” as a volatility proxy to aid in model-based multivariate bond yield density forecasting. To do so, we develop a general estimation approach to incorporate volatility proxy information into dynamic factor models with stochastic volatility. The resulting model parameter estimates are highly efficient, which one hopes would translate into superior predictive performance. We explore this conjecture in the context of density prediction of U.S. bond yields by incorporating realized volatility into a dynamic Nelson-Siegel (DNS) model with stochastic volatility. The results clearly indicate that using realized volatility improves density forecasts relative to popular specifications in the DNS literature that neglect realized volatility.

Original languageEnglish (US)
Pages (from-to)373-389
Number of pages17
JournalInternational Journal of Forecasting
Volume33
Issue number2
DOIs
StatePublished - Apr 1 2017

Keywords

  • Dynamic Nelson-Siegel model
  • Dynamic factor model
  • Forecasting
  • Stochastic volatility
  • Term structure of interest rates

ASJC Scopus subject areas

  • Business and International Management

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