Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters

Koopman, Siem Jan; Mallee, Max; Wel, Michel van der

doi:10.1198/jbes.2009.07295

Cited by 112 publications

(58 citation statements)

References 23 publications

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“…Examples are Duffie-Kan restrictions (Duffie and Kan, 1996), smoothing restrictions (Koopman and Van der Wel, 2010) Yu and Zivot (2010) find that using the state-space estimation method results in poor out-of-sample performance compared with the two-step procedure.…”

Section: General Factor Modelmentioning

confidence: 99%

Prediction Bias Correction for Dynamic Term Structure Models

Raviv¹

2012

SSRN Journal

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Section: General Factor Modelmentioning

confidence: 99%

Prediction Bias Correction for Dynamic Term Structure Models

Raviv¹

2012

SSRN Journal

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“…Nelson-Siegel-Measurement Error Stochastic Volatility (+ Realized Volatility) (DNS-ME) Koopman et al (2010) argue that putting the time-varying conditional volatility on the measurement errors provides an improvement for the in-sample fit of the DNS class of models.…”

Section: Dynamicmentioning

confidence: 99%

Does Realized Volatility Help Bond Yield Density Prediction?

Shin

Zhong

2015

FEDS

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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.

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“…However, a constant λ t can also be estimated along with the other model parameters using the Kalman filter, as in De Pooter et al (2007). Koopman et al (2010) propose ways of allowing for time-varying λ t . For simplicity, we will use a time-invariant λ, which is estimated along with the other model parameters.…”

Section: Nelson-siegel Modelmentioning

confidence: 99%

Forecasting the Yield Curve in a Data-Rich Environment Using the Factor-Augmented Nelson-Siegel Model

et al. 2010

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Various ways of extracting macroeconomic information from a data-rich environment are compared with the objective of forecasting yield curves using the Nelson-Siegel model. Five issues in factor extraction are addressed, namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data-driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. The data-driven methods perform well in relatively volatile periods, when simpler models do not suffice.

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Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters

Cited by 112 publications

References 23 publications

Prediction Bias Correction for Dynamic Term Structure Models

Prediction Bias Correction for Dynamic Term Structure Models

Does Realized Volatility Help Bond Yield Density Prediction?

Forecasting the Yield Curve in a Data-Rich Environment Using the Factor-Augmented Nelson-Siegel Model

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