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Modelling and Forecasting WIG20 Daily Returns

Author

Listed:
  • Cristina Amado

    (University of Minho)

  • Annastiina Silvennoinen

    (Queensland University of Technology)

  • Timo Terasvirta

    () (Aarhus University
    Humboldt-Universität zu Berlin)

Abstract

The purpose of this paper is to model daily returns of the WIG20 index. The idea is to consider a model that explicitly takes changes in the amplitude of the clusters of volatility into account. This variation is modelled by a positive-valued deterministic component. A novelty in specification of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity.

Suggested Citation

  • Cristina Amado & Annastiina Silvennoinen & Timo Terasvirta, 2017. "Modelling and Forecasting WIG20 Daily Returns," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 9(3), pages 173-200, September.
  • Handle: RePEc:psc:journl:v:9:y:2017:i:3:p:173-200
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    References listed on IDEAS

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    Cited by:

    1. Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2504. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.
    2. Cristina Amado & Annastiina Silvennoinen & Timo Ter¨asvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," NIPE Working Papers 07/2018, NIPE - Universidade do Minho.
    3. Cristina Amado & Annastiina Silvennoinen & Timo Terasvirta, 2017. "Modelling and Forecasting WIG20 Daily Returns," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 9(3), pages 173-200, September.

    More about this item

    Keywords

    autoregressive conditional heteroskedasticity; forecasting volatility; modelling volatility; multiplicative time-varying GARCH; smooth transition;

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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