The first day will review specific credit risk mandates from the perspective of the model, the full landscape of credit risk will be outlined and will be evolved over the coming days. An emphasis is on investigation of the variables that need to be captured for modelling loss from credit portfolios.

Model Overview

• A look at the basic premise of regulation
• The key areas of the credit model, top down view 
• Key dynamic datasets that have to be captured 
• Key variables that are to be modelled 

Conditional and Undconditional Models

• Various models of default and Mark-to-Market
• How DCCF can be integrated into the model 
• Risk Neutral Valuation Approach 
• Which approach is most suitable for rating 

Credit Scoring Fundamentals

• Key outline of the complete scoring process
• The rating agencies main scoring criteria 
• How can a bank create a scoring system 
• How to handle differing factors agency model

Rating Agency Perspectives

• A detailed look at the rating agency methods
• How effective have they been overtime 
• What are the weaknesses in these models 

A framework which connects front & middle office

• How to integrate the credit decision
• How to dimension default from expert systems 
• Effects of rating data on PD
• Effects of rating data on LGD

Duration and Portfolio Terminal Value Overview

• Duration and its importance on a portfolio
• Preparation for more sophisticated models 
• Rating agency data on term structures
• How can a bank manage such risk weighted gaps

SESSION 2 [ Expanding the model ]

In the second day, the course will expand on the foundational model by taking the fundamentals explained on day one to a more complex level.  The emphasis on day two is to how rating agency data can be implemented into a scoring model and how a portfolio can be re-adjusted overtime.

Portfolio decay models with and without rating data

• How traditional curve fitted decay is represented
• Building a transition matrix
• How to connect and aggregate rating data
• Bringing the elements together

Default Adjusted Returns on Term Structures

• Using Markov models to understand losses
• How to calculate expected return
• Understand multi-period and multi position
• How to build up a transition matrix for multi state

Conditional models using rating agency grades

• How rating agency data is integrated
• What is industry practice for banks
• How can these systems be back tested
• Reporting the expected and unexpected loss

Dynamic Scoring and Weighting process

• How to reweight the scoring process
• Using changes in multiple discriminant ratios
• Rebalance the portfolio for change in ratios
• How can this drive credit & securitisation decisions

LGD / EAD Gap Analysis for collateral discounting

• How Rating models the LGD and EAD ratios
• What is the effect across the portfolio
• Dynamic capital and RAROC
• How is concentration risk factored into the model

The third day will look at growing industry trends in Credit Risk Modelling and management of other important data sets that can bbe used in the model, how to set benchmarks and maintain positions against moving scores year in year out.

Keeping credit scoring models concurrent

• How to keep the score model up to date
• How to adjust portfolio for default
• What are the effects of concentration risk on the portfolio
• Understanding the 2nd derivative of relationship of the portfolio and expected returns

How to implement CDX & iTrax credit indexes

• Using indexes in the model
• How to use market index data in the model
• What are the trends in European banks for CDS quote data at origination.
• How are European banks restructuring their lending approaches to emerging markets.

Transactional management of default

• What are the effects of a collapse of rating data on the model?
• Hoe to stress relationships in a scenario model
• How to combine these factors to generate different perspectives of value at risk