Multivariate Volatility Modelling and Risk Diversification

This research considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. It also addresses the problem of evaluating volatility models using Value-at-Risk (VaR) diagnostic tests for individual as well as `average' models. The aim is to develop reliable (and feasible) tools for risk monitoring and portfolio decisions in the case of a large number of assets. This research will be integrated into the global modelling work and aims to provide a more integrated approach to risk management.