Extreme value theory with oceanographic applications

SIGLEAvailable from British Library Document Supply Centre- DSC:DX83746 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Regular variation and extremal dependence of GARCH residuals with application to market risk measures

Stock returns exhibit heavy tails and volatility clustering. These features, motivating the use of GARCH models, make it difficult to predict times and sizes of losses that might occur. Estimation of losses, like the Value-at-Risk, often assume that returns, normalized by the level of volatility, are Gaussian. Often under ARMA-GARCH modeling, such scaled returns are heavy tailed and show extremal dependence, whose strength reduces when increasing extreme levels. We model heavy tails of scaled returns with generalized Pareto distributions, while extremal dependence can be reduced by declustering data

The extremal index for GARCH(1,1) processes with t-distributed innovations

Economics Department Working Papers, University of Parm

Modelling non-stationary flood frequency in England andWales using physical covariates

Non-stationary methods of flood frequency analysis are widespread in research but rarely implemented by practitioners. One reason may be that research papers on non-stationary statistical models tend to focus on model fitting rather than extracting the sort of results needed by designers and decision makers. It can be difficult to extract useful results from non-stationary models that include stochastic covariates for which the value in any future year is unknown. We explore the motivation for including such covariates, whether on their own or in addition to a covariate based on time. We set out a method for expressing the results of non-stationary models as an integrated flow estimate, which removes the dependence on the covariates. This can be defined either for a particular year or over a longer period of time. The methods are illustrated by application to a set of 375 river gauges across England and Wales. We find annual rainfall to be a useful covariate at many gauges, sometimes in conjunction with a time-based covariate. For estimating flood frequency in future conditions, we advocate exploring hybrid approaches that combine the best attributes of non-stationary statistical models and simulation models that can represent changes in climate and river catchments

Simulation of bedload transport of marine gravel

SIGLEAvailable from British Library Document Supply Centre- DSC:7769.086(SU-DPS-RR--377/91) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Simulation of bedload transport of marine gravel

SIGLEAvailable from British Library Document Supply Centre- DSC:7769.086(SU-DPS-RR--377/91) / BLDSC - British Library Document Supply CentreGBUnited Kingdo