Volatility forecasting for risk management

Recent research has suggested that forecast evaluation on the basis of standard statistical loss functions could prefer models which are sub-optimal when used in a practical setting. This paper explores a number of statistical models for predicting the daily volatility of several key UK financial time series. The out-of-sample forecasting performance of various linear and GARCH-type models of volatility are compared with forecasts derived from a multivariate approach. The forecasts are evaluated using traditional metrics, such as mean squared error, and also by how adequately they perform in a modern risk management setting. We find that the relative accuracies of the various methods are highly sensitive to the measure used to evaluate them. Such results have implications for any econometric time series forecasts which are subsequently employed in financial decisionmaking.

Impact Assessment in the EU: a tool for better regulation, less regulation or less bad regulation?

Impact Assessments (IAs) were introduced at the EU level under the rhetorical facade of ‘better regulation’. The actual aim was to improve not only the quality but also the reputation of EU regulation before stakeholders. However, evidence brought forward by a number of evaluations pointed out that IAs are yet to achieve acceptable quality standards. The paper offers an overview of different disciplinary approaches for looking at IAs. It suggests that risk regulation encompasses the theoretical foundations to help understand the role of IAs in the EU decisionmaking process. The analysis of 60 early days preliminary IAs provides empirical evidence regarding policy alternatives, methodology of consultation and use of quantitative techniques. Findings suggest that dawn period IAs were used mainly to provide some empirical evidence for regulatory intervention in front of stakeholders. The paper concludes with assumptions about the future role of IAs at EU level.