This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite financial risk. Copula-based dependence modelling is a popular tool in financial applications, but is usually...

This paper features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically-compounded returns, in...

This paper features an analysis of the effectiveness of a range of portfolio diversification strategies as applied to a set of daily arithmetically compounded returns on a set of ten market indices representing the major European markets for a nine year period from the beginning of 2005 to the...

Value-at-Risk (VaR) has become the universally accepted metric in the financial services industry for internal control and for regulatory reporting. This has focused attention on methods of measuring, estimating and forecasting lower tail risk. One promising technique is Quantile Regression...

This paper presents an application of a recently developed approach by Matteson and James (2012) for the analysis of change points in a data set, namely major financial market indices converted to financial return series. The general problem concerns the inference of a change in the distribution...

This paper examines the implications for European investors of the recent EU expansion to encompass former Eastern block economies. What were the risk and return characteristics of these markets pre- and post-EU? What are the implications for investors within the Euro zone? Should investors...

Value-at-Risk (VaR) has become the universally accepted metric in the financial services industry for internal control and for regulatory reporting. This has focused attention on methods of measuring, estimating and forecasting lower tail risk. One promising technique is Quantile Regression...