Time-varying copula models for financial time series

The main objective of this thesis is an analysis of the potential time-inhomogeneity in the dependence between multiple financial time series. We focus on suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result on the asymptotic properties of the aforementioned model is used to derive the joint limiting distribution of local maximum likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we succeed in establishing the limiting law of the maximum of the estimator series. Based on the limiting distributions we develop statistical homogeneity tests and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are rather an exception than the rule