This thesis proposes a correction method and a Bayesian estimator for the Pickands dependence function, improving accuracy and ensuring mathematical validity in modeling extreme events. Applications in finance include modeling joint extreme events such as simultaneous market crashes or co-movements in asset returns, improving risk assessment in portfolio management, and stress testing by accurately capturing tail dependencies between financial instruments. (In French)