The primary focus of this thesis centers on the investigation of an innovative quantitative framework for evaluating credit risk, particularly within the context of margin loans pricing. Given that these credit derivatives are grounded in large credit portfolios, the accurate quantification of their inherent credit risk is imperative, necessitating the evaluation of loss tail quantiles and Conditional Value at Risk. To accomplish this, the study harnesses the power of LT-Archimedean Copulas, specialized mathematical tools for elucidating tail-risk interdependencies among obligors. This analysis begins by deriving precise analytical asymptotic expressions, which serve as the foundation for the development of a variance reduction algorithm known as the Conditional Monte Carlo method, employed to enhance the outcomes of the conventional Monte Carlo approach.