Dynamic term structure models with score-driven time-varying parameters: estimation and forecasting

Siem Jan Koopman, Andre Lucas and Marcin Zamojski

No 258, NBP Working Papers from Narodowy Bank Polski

Abstract: We consider score-driven time-varying parameters in dynamic yield curve models and investigate their in-sample and out-of-sample performance for two data sets. In a univariate setting, score-driven models were shown to offer competitive performance to parameter-driven models in terms of in-sample fit and quality of out-of-sample forecasts but at a lower computational cost. We investigate whether this performance and the related advantages extend to more general and higher-dimensional models. Based on an extensive Monte Carlo study, we show that in multivariate settings the advantages of score-driven models can even be more pronounced than in the univariate setting. We also show how the score-driven approach can be implemented in dynamic yield curve models and extend them to allow for the fat-tailed distributions of the disturbances and the time-variation of variances (heteroskedasticity) and covariances.

Keywords: term-structure; dynamic Nelson-Siegel models; non-Gaussian distributions; time-varying parameters; observation-driven models; parameter-driven models (search for similar items in EconPapers)
JEL-codes: C15 C32 C33 C58 C63 E43 E52 E58 (search for similar items in EconPapers)
Pages: 82
Date: 2017
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:nbp:nbpmis:258

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