Large data sets in finance with millions of observations have becomewidely available. Such data sets enable the construction of reliablesemi-parametric estimates of the risk associated with extreme pricemovements. Our approach is based on semi-parametric statisticalextreme value analysis, and...

We advocate the use of absolute moment ratio statistics in conjunctionwith standard variance ratio statistics in order to disentangle lineardependence, non-linear dependence, and leptokurtosis in financial timeseries. Both statistics are computed for multiple return horizonssimultaneously, and...

Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the...

Using hundreds of significant anomalies as testing portfolios, this paper compares the performance of major empirical asset pricing models. The q-factor model and a closely related five-factor model are the two best performing models among a long array of models. The q-factor model outperforms...