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In the paper we investigate the dynamics of the stock index WIG20 volatility. We consider return series calculated in different frequencies. For each return series we estimate GARCH(1,1) model and, basing on its parameters, we calculate the corresponding diffusion GARCH model parameters. The question is whether the dynamics of the WIG20 is driven by some kind of diffusion GARCH process. We try to check it by calculating the diffusion GARCH parameters based on the GARCH(1,1) model fitted to the returns in different frequencies. Our results do not reject that presumption.(fragment of text)
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autor
- Poznań University of Economics, Poland
Bibliografia
- Andersen, T.G., Bollerslev, T. (1998), Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts, International Economic Review 39, 885- 905.
- Bollerslev, T. (1986), Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics 31, 307-327.
- Brockwell, P.J., Davis, R.A. (1995), Time Series: Theory and Methods, Springer, New York.
- Davidson, J. (2003), Time Series Modelling Version 3.23, Cardiff University.
- Drost, F.C., Nijman, T.E. (1993), Temporal Aggregation of GARCH Processes, Econometrica 61, 909-927.
- Drost, F.C., Werker, B.J.M. (1996), Closing the GARCH Gap: Continuous Time GARCH Modeling, Journal of Econometrics 74, 31-57.
- Ghysels, E., Jasiak, J., (1998), GARCH for Irregularly Spaced Financial Data: The ACD-GARCH Model, Studies in Nonlinear Dynamics and Econometrics 2, 133-149.
- Janicki, A., Izydorczyk, A. (2001), Computer Methods in Stochastic Modeling (in Polish), Wydawnictwa Naukowo Techniczne, Warszawa.
- Nelson, D.B. (1990), ARCH Models as Diffusion Approximations, Journal of Econometrics 45, 7-38.
Typ dokumentu
Bibliografia
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bwmeta1.element.ekon-element-000171294785