The course will provide attendees a comprehensive knowledge of credit risk modelling. A hands-on approach is followed by providing both the theoretical and practical toolkit to use on a day-by-day basis. The opensource statistical software R paves the way for grasping all details required to create customized analysis.
During the first day, the key instruments used for modelling are explored. A wide use of the software R characterizes the course from the very beginning. In day one, the emphasis is on developing point-in-time Probability of Default (PD). An extensive interaction with R paves the way for the next two-day program.
The focus of the second day is to expand the time horizon to encompass the entire lifetime. On this, generalized linear models together with survival analysis are investigated for deriving lifetime PD curves. As during the first day, a specific attention is devoted to the so-called low default portfolios. Then, the focus shifts towards
Exposure at Default (EAD) modelling.
During the third day specific attention is devoted to loss given default (LGD). Few alternative models are considered by focusing on the key elements of the workout process. Finally, hints are provided on Expected Credit Losses (ECLs) computed according to IFRS 9 principles.
Working-level knowledge of modelling and corresponding hands-on R software development.
Working knowledge of lifetime PD modelling based on generalised linear modelling (GLM) and survival analysis based on R implementation capability.
Knowledge of the key EAD modelling techniques for wholesale as well as retail products.
Knowledge of LGD structural modelling technique and understanding of regression modelling (e.g., Tobit regression).
Working-level-knowledge of staging allocation process and practical implementation.
Deep understanding of ECL computational mechanisms.
Deep grasp of ECL validation techniques.