Acta Oeconomica Pragensia 2007, 15(1):114-120 | DOI: 10.18267/j.aop.44
Modelling of Stock Returns Time-Series
- Doc. Ing. Jiří Trešl, CSc.; Katedra statistiky a pravděpodobnosti, Fakulta informatiky a statistiky, VŠE v Praze, tresl@vse.cz.
- Doc. Ing. Dagmar Blatná, CSc.; Katedra statistiky a pravděpodobnosti, Fakulta informatiky a statistiky, VŠE v Praze, blatna@vse.cz.
In the study submitted, selected methods of financial time-series analysis are applied to daily returns of the most liquid stocks at Czech capital market. In most cases, symmetric GARCH(1,1) models are quite satisfactory. Further, ARFIMA models enabling to catch "long memory" of underlying processes are suitable for the modelling both absolute values of returns and their volatility. Alternative posibility is to employ bilinear models, which prove to be suitable namely for returns. Hurst exponents computed signalize some tendency to cycles creation in some cases (ČEZ, Unipetrol).
Keywords: financial time-series, stock returns, GARCH models
JEL classification: C22
Published: February 1, 2007 Show citation
References
- BOLLERSLEV, T., 1986: Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 1986, 31, s. 307-327.
Go to original source... - HAMILTON, J., 1994: Time Series Analysis. Princeton, Princeton University Press, 1994.
- HOSKING, J., 1981: Fractional Differencing. Biometrika, 1981, 68, s. 165-176. Go to original source...
- TSAY, R., 2002: Analysis of Financial Time Series. New York, Wiley, 2002. Go to original source...
- PETERS, E., 1994: Fractal Market Analysis. New York, Wiley, 1994.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.