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Does realized volatility help bond yield density prediction?
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DOI：10.1016/j.ijforecast.2016.11.003.png)
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Abstract
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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. (C) 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Dynamic factor model
Forecasting
Stochastic volatility
Term structure of interest rates
Dynamic Nelson-Siegel model
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