Developing and calibrating tractable cutting-edge multivariate financial models.

The increased trading in multi-name financial products has required state-of-the-art multivariate models that are, at the same time, computationally tractable and flexible enough to explain the stylized facts of asset returns and of their dependence structure. The project is aimed at developing and calibrating multivariate models that can replicate financial market data whatever the level of investor's fear in the market. To this end, we will use advanced stochastic processes, such as Lévy processes, Sato processes and continuous time Markov chains. We will also develop fast and accurate calibration algorithms based on series expansions and on the matching of market implied moments and co-moments extracted from current market quotes. Particular attention will be given to the models' ability to explain the asset dependence structure, which plays a crucial role in the assessment of correlation risk. A correct management of this new kind of financial risk, which is inherent to any multi-name financial product, has indeed appeared to be vital during recent systemic crashes, such as the global financial crisis of 2007-2008.

Keywords:FINANCIAL MODELS

Disciplines:Statistics and numerical methods, Game theory, economics, social and behavioural sciences