Does Realized Volatility Help Bond Yield Density Prediction?
Minchul Shin and
Molin Zhong
No 2015-115, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)
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)
JEL-codes: C5 E4 G1 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2015-12-18
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
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http://www.federalreserve.gov/econresdata/feds/2015/files/2015115pap.pdf Full text (application/pdf)
http://dx.doi.org/10.17016/FEDS.2015.115 DOI (application/pdf)
Related works:
Journal Article: Does realized volatility help bond yield density prediction? (2017) 
Working Paper: Does realized volatility help bond yield density prediction? (2013) 
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2015-115
DOI: 10.17016/FEDS.2015.115
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