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Copula is used to model multivariate data, as it accounts for the dependence structure and provides a flexible representation of the multivariate distribution. Recently a large number of Archimedean copulas have been proposed to deal with various dependence aspects in financial risk management, which invokes several new questions in some important yet underresearched areas.This dissertation comprises three essays and probes into three untouched questions all involving the Archimedeancopulabased models. It provides important empirical evidences that the Archimedean copulabased PVaR model generally has better forecasting performance than the Gaussian copulabased PVaR model. Therefore, financial risk managers should consider the use of the Archimedean copulabased PVaR model when attempting to forecast extreme downside dependent risk.
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