Innovations in dependence modelling for financial applications

The contribution of this thesis is in developing and investigating novel dependence modelling techniques in financial applications. Furthermore, the aim is to understand the key factors driving the dynamic nature of such dependence. When modelling the multivariate distribution of the returns associated to a portfolio of financial assets one is faced with a multitude of considerations and potential choices. For example, in the currency studies undertaken in this thesis suitably heavy-tailed marginal time series models are developed for the returns of each currency exchange rate, and then the multivariate dependence structure of the returns of multiple-currency baskets at each time instant is considered. These dependence relationships can be studied via numerous concordance measures such as correlation, rank correlations and extremal dependences. Such studies can be undertaken in a static or dynamic setting and either parametrically or non-parametrically. Another important aspect of financial time series is the enormous amount of financial data available for statistical analysis and financial econometrics that can be used to better understand economic and financial theories. In this thesis, the focus is on the influence of dependence structures in complex financial data in two asset classes: currencies and commodities. These are challenging data structures as they contain temporal serial dependence, cross dependence and term-structural dependences. Each of these forms of dependence are studied in this thesis in both parametric and non-parametric settings.