Modelling and Forecasting WIG20 Daily Returns
DOI:
https://doi.org/10.24425/cejeme.2017.122208Keywords:
autoregressive conditional heteroskedasticity, forecasting volatility, modelling volatility, multiplicative time-varying GARCH, smooth transitionAbstract
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.
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Published
2017-05-15
How to Cite
Teräsvirta, T. (2017). Modelling and Forecasting WIG20 Daily Returns. Central European Journal of Economic Modelling and Econometrics, 9(3), 173–200. https://doi.org/10.24425/cejeme.2017.122208
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Copyright (c) 2025 Timo Teräsvirta
This work is licensed under a Creative Commons Attribution 4.0 International License.
