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Forecasting US bond yields at weekly frequency

Author

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  • Riccardo LUCCHETTI

    () (Universita' Politecnica delle Marche, Dipartimento di Economia)

  • Giulio PALOMBA

    ([n.a.])

Abstract

Forecasting models for bond yields often use macro data to improve their properties. Unfortunately, macro data are not available at frequencies higher than monthly. In order to mitigate this problem, we propose a nonlinear VEC model with conditional heteroskedasticity (NECH) and find that such model has superior in-sample performance than models which fail to encompass nonlinearities and/or GARCH-type effects. Out-of-sample forecasts by our model are marginally superior to competing models; however, the data points we used for evaluating forecasts refer to a period of relative tranquillity on the financial markets, whereas we argue that our model should display superior performance under "unusual" circumstances.

Suggested Citation

  • Riccardo LUCCHETTI & Giulio PALOMBA, 2006. "Forecasting US bond yields at weekly frequency," Working Papers 261, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  • Handle: RePEc:anc:wpaper:261
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    References listed on IDEAS

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    Cited by:

    1. Ugo FRATESI, 2010. "The National and International Effects;of Regional Policy Choices: Agglomeration Economies, Peripherality and Territorial Characteristics," Working Papers 344, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Luca RICCETTI, 2010. "Minimum Tracking Error Volatility," Working Papers 340, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Fabio FIORILLO & Agnese SACCHI, 2010. "I Want to Free-ride. An Opportunistic View on Decentralization Versus Centralization Problem," Working Papers 346, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    More about this item

    Keywords

    conditional heteroskedasticity; forecasting; interest rates; nonlinear cointegration;

    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
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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