16 research outputs found
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