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The Term Structure of Government Bond Yields in an Emerging Market

Author

Listed:
  • Wali ULLAH

    (Department of Economics and Finance, Institute of Business Administration (IBA), Karachi, Pakistan.)

  • Khadija Malik BARI

    (Department of Economics and Finance, Institute of Business Administration (IBA), Karachi, Pakistan.)

Abstract

The accurate modeling of the term structure of interest rates is of vital importance in macroeconomics and finance in general and in the context of monetary policy in particular, as its factors are important in predicting future growth and inflation. This paper investigates the extent to which the so called Nelson-Siegel model (DNS) and its extended version that accounts for time varying volatility (DNS-GARCH and DNS-EGARCH) can optimally fit the yield curve and predict its future path in the context of an emerging economy. The study expands the earlier work (Koopman, et al. 2010) by looking at more elaborate specifications for volatility modeling such as E-GARCH and also evaluates the predictive role of considering the time-varying volatility in the model in terms of out-of-sample forecasting. For the in-sample fit, all three models fit the curve remarkably well even in the emerging markets. However, the DNS-EGARCH model fits the curve slightly better than the other two models. Moreover, all three specifications of the yield curve that are based on the Nelson-Siegel functional form, outperform the benchmark AR(1) forecasts at all three specified forecast horizons. The DNS comes with more precise forecasts than the volatility based extended models for the 1-month ahead forecasts, while the other two outperform the standard DNS for 6- and 12-month horizons.

Suggested Citation

  • 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.
    • Handle: RePEc:rjr:romjef:v::y:2018:i:3:p:5-28
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    2. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.

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    More about this item

    Keywords

    yield curve; forecasting; emerging markets; Kalman filter; EGARCH;
    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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