Abstract
The disertation thesis deals with selected problems of financial time series. In particular, it focuses on two fundamental aspects of conditional heteroscedasticity.
The first part introduces self-weighted recursive estimation algorithms for univariate models of the type ARCH. The second part proposes a novel approach to conditional correlation modelling for multivariate financial time series.
The numerical capabilities of suggested procedures are demonstrated by Monte Carlo simulations and real data examples.