We used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications.(...)

Macroeconomic risk assessments play an important role in the forecasts of manyinstitutions. However, to the best of our knowledge their performance has notbeen investigated yet. In this work, we study the Bank of England’s risk forecastsfor inflation. We find that these forecasts do not...

The estimation of multivariate GARCH models remains a challenging task, even in modern computer environments. This manuscript shows how Independent Component Analysiscan be used to estimate the Generalized Orthogonal GARCH model in a fraction of the time otherwise required. The proposed method...

We consider the modelling of rare events in financial time series,and introduce a marked point process model for the excesses of thetime series over a high threshold that combines a self-exciting processfor the exceedances with a mark (size) dependent process. This allowsrealistic models for...

I examine determinants of stochastic relative risk aversion in conditional asset pricing models. I first develop time-series specification tests with nonlinear state-space models with heteroskedasticity based on Merton (1973)'s ICAPM. I then established the following facts. First, the surplus...

The recent financial crisis has accentuated the fact that extreme outcomes have been overlookedand not dealt with adequately. While extreme value theories have existed for a long time, themultivariate variant is difficult to handle in the financial markets due to the prevalentheteroskedasticity...

Controllability of longer-term interest rates requires that the persis-tence of their deviations from the central bank's policy rate (i.e. thepolicy spreads) remains suffciently low. This paper applies fractionalintegration techniques to assess the persistence of policy spreads ofeuro area money...

This paper proposes a novel approach to the combination of conditional covariancematrix forecasts based on the use of the Generalized Method of Moments (GMM). Itis shown how the procedure can be generalized to deal with large dimensional systemsby means of a two-step strategy. The finite sample...