Many static and dynamic models exist to forecast Value-at-Risk other quantile-related metrics used in financial risk management. Industry practice favours simpler, such as historical simulation or its variants. Most academic research focuses on the GARCH family. While numerous studies examine accuracy of multivariate for forecasting metrics, there is little accurately predicting entire distribu...

it is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. hence, at present study, two models including artificial neural networks and nonlinear multivariate regression were used to predict peak discharge in taleghan watershed. maximum daily mean discharge and corresponding daily rainfall, one day antecedent and...

Considering the complexity of hydrological processes, it seems that multivariate methods may enhance the accuracy of time series models and the results obtained from them by taking more influential factors into account. Indeed, the results of multivariate models can improve the results of description, modeling, and prediction of different parameters by involving other influential factors. In th...

The Pareto distribution has long been recognized as a suitable model for many non-negative socio-economic variables. Univariate and multivariate variations abound. Some unification is possible by representing the Pareto variables in terms of independent gamma distributed components. Further unification is sometimes possible since some of the frequently used multivariate Pareto models share the ...

This paper reviews some models for exploring multivariate data. If a xed eeect model is used to deene a linear Principal Components Analysis (PCA), then risk functions can be deened and issues of metric and dimension optimality addressed. The model is then adapted to deene a functional PCA which can be used to the study of smooth sampled curves. Finally, this model is generalised, giving a curv...

A constructive proof of identification of multilinear decompositions of multiway arrays is presented. It can be applied to show identification in a variety of multivariate latent structures. Examples are finite-mixture models and hidden Markov models. The key step to show identification is the joint diagonalization of a set of matrices in the same nonorthogonal basis. An estimator of the latent...

In Böcker and Klüppelberg (2005) we presented a simple approximation of OpVaR of a single operational risk cell. The present paper derives approximations of similar quality and simplicity for the multivariate problem. Our approach is based on modelling of the dependence structure of different cells via the new concept of a Lévy copula. JEL Classifications: G18,G39.

Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from disparate sources and analyze the results for specific and unique sample correlations. This workf...

Age-period-cohort (APC) models are used to analyse age-specific disease or mortality rates provided for several periods in time with respect to three time scales: age, period (calendar period during which the incidence or mortality rates were observed) and cohort (time of birth). Frequently, several sets of such age-specific rates are observed because data were recorded according to one further...