Term structure forecasting in affine framework with time-varying volatility

Wali Ullah ()
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Wali Ullah: Institute of Business Administration (IBA)

Statistical Methods & Applications, 2017, vol. 26, issue 3, No 7, 453-483

Abstract: Abstract This study extends the affine Nelson–Siegel model by introducing the time-varying volatility component in the observation equation of yield curve, modeled as a standard EGARCH process. The model is illustrated in state-space framework and empirically compared to the standard affine and dynamic Nelson–Siegel model in terms of in-sample fit and out-of-sample forecast accuracy. The affine based extended model that accounts for time-varying volatility outpaces the other models for fitting the yield curve and produces relatively more accurate 6- and 12-month ahead forecasts, while the standard affine model comes with more precise forecasts for the very short forecast horizons. The study concludes that the standard and affine Nelson–Siegel models have higher forecasting capability than their counterpart EGARCH based models for the short forecast horizons, i.e., 1 month. The EGARCH based extended models have excellent performance for the medium and longer forecast horizons.

Keywords: Latent factors model; State-space model; Kalman filter; EGARCH; Forecasting; Bond market (search for similar items in EconPapers)
JEL-codes: C32 C51 C53 E43 G12 G17 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10260-017-0378-y

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