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Does realized volatility help bond yield density prediction?

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  • Shin, Minchul
  • Zhong, Molin

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.

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  • Shin, Minchul & Zhong, Molin, 2017. "Does realized volatility help bond yield density prediction?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 373-389.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:373-389
    DOI: 10.1016/j.ijforecast.2016.11.003
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    4. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.

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    More about this item

    Keywords

    Dynamic factor model; Forecasting; Stochastic volatility; Term structure of interest rates; Dynamic Nelson-Siegel model;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates

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