Quantification of operational risks: efficient capital calculation methods based on internal data.

Summary Operational risks are risks of loss resulting from inadequacy or failure of the institution's procedures (missing or incomplete analysis or control, unsecured procedures), its personnel (error, malice and fraud), internal systems (computer breakdown, etc.) or external risks (flood, fire, etc.). They have attracted the attention of the authorities, following the losses and bankruptcies they have generated. To model them, the regulator requires institutions to use internal data, external data, scenarios and certain qualitative criteria. Using internal data, within the framework of the Loss Distribution Approach, we propose several innovative methods to estimate the provisions inherent to these risks. The first innovation dealing with convolution methods suggests mixing Monte Carlo simulations, kernel density estimation and Panjer's algorithm to construct the loss distribution. The second solution focuses on modeling the right tail of severity distributions using several results from extreme value theory and parameter estimation methods of truncated distributions. The third method we present focuses on multivariate VaR calculations. Implementing copula clusters to capture particular behaviors such as tail dependence, we provide a new type of architectures to compute global VaR.