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Term structure forecasting in affine framework with time-varying volatility

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  • Wali Ullah

    (Institute of Business Administration (IBA))

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.

Suggested Citation

  • Wali Ullah, 2017. "Term structure forecasting in affine framework with time-varying volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 453-483, August.
    • Handle: RePEc:spr:stmapp:v:26:y:2017:i:3:d:10.1007_s10260-017-0378-y
    DOI: 10.1007/s10260-017-0378-y
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    Cited by:

    1. Wali Ullah, 2020. "The arbitrage-free generalized Nelson–Siegel term structure model: Does a good in-sample fit imply better out-of-sample forecasts?," Empirical Economics, Springer, vol. 59(3), pages 1243-1284, September.
    2. Wali ULLAH & Khadija Malik BARI, 2018. "The Term Structure of Government Bond Yields in an Emerging Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-28, September.

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    More about this item

    Keywords

    Latent factors model; State-space model; Kalman filter; EGARCH; Forecasting; Bond market;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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