Does realized volatility help bond yield density prediction?

Minchul Shin and Molin Zhong

International Journal of Forecasting, 2017, vol. 33, issue 2, 373-389

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.

Keywords: Dynamic factor model; Forecasting; Stochastic volatility; Term structure of interest rates; Dynamic Nelson-Siegel model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Does Realized Volatility Help Bond Yield Density Prediction? (2015)
Working Paper: Does realized volatility help bond yield density prediction? (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:2:p:373-389

DOI: 10.1016/j.ijforecast.2016.11.003

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