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In-Sample Bounds for Time-Varying Parameters of Observation Driven Models

Author

Listed:
  • Francisco Blasques

    (VU University Amsterdam)

  • Siem Jan Koopman

    (VU University Amsterdam)

  • Katarzyna Lasak

    (VU University Amsterdam)

  • André Lucas

    (VU University Amsterdam)

Abstract

We study the performance of two analytical methods and one simulation method for computing in-sample confidence bounds for time-varying parameters. These in-sample bounds are designed to reflect parameter uncertainty in the associated filter. They are applicable to the complete class of observation driven models and are valid for a wide range of estimation procedures. A Monte Carlo study is conducted for time-varying parameter models such as generalized autoregressive conditional heteroskedasticity and autoregressive conditional duration models. Our results show clear differences between the actual coverage provided by our three methods of computing in-sample bounds. The analytical methods may be less reliable than the simulation method, their coverage performance is sufficiently adequate to provide a reasonable impression of the parameter uncertainty that is embedded in the time-varying parameter path. We illustrate our findings in a volatility analysis for monthly Standard & Poor's 500 index returns.

Suggested Citation

  • Francisco Blasques & Siem Jan Koopman & Katarzyna Lasak & André Lucas, 2015. "In-Sample Bounds for Time-Varying Parameters of Observation Driven Models," Tinbergen Institute Discussion Papers 15-027/III, Tinbergen Institute, revised 07 Sep 2015.
    • Handle: RePEc:tin:wpaper:20150027
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    autoregressive conditional duration; delta-method; generalized autoregressive conditional heteroskedasticity; score driven models; time-varying mean;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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