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Does realized volatility help bond yield density prediction?

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  • Minchul Shin

    (Department of Economics, University of Pennsylvania)

  • Molin Zhong

    (Department of Economics, University of Pennsylvania)

Abstract

This paper examines the importance of realized volatility in bond yield density prediction. We incorporate realized volatility into a Dynamic Nelson-Siegel (DNS) model with stochastic volatility and evaluate its predictive performance on US bond yield data. When compared to popular specifications in the DNS literature without realized volatility, we find that having this information improves density forecasting performance.

Suggested Citation

  • Minchul Shin & Molin Zhong, 2013. "Does realized volatility help bond yield density prediction?," PIER Working Paper Archive 13-064, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:13-064
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    References listed on IDEAS

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    Cited by:

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    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2021. "No‐arbitrage priors, drifting volatilities, and the term structure of interest rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 495-516, August.
    3. 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;
    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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