Corporate Credit Rating Systems: Design, Development, Calibration and Validation
Dates: March 23 - 24, 2020
Price: EUR 1,260
Location: Prague, NH Hotel Prague
Lecturer: Dr. Krassimir Kostadinov
Key points / questions answered:
Which rating models are appropriate for regulations such as Basel III or IFRS 9?
How to gather, structure and maintain the data needed for credit ratings of corporate entities?
How to handle data availability and data quality challenges in practice?
Which statistical tools and rating model development practices are robust and proven?
Which innovative methods might help the bank to obtain forward-looking risk assessments?
How to create a rating system that is useful for risk-adjusted pricing in long-term corporate customer-bank relationships?
The purpose of this seminar is to introduce you to the key methodologies to design, develop, calibrate and validate credit rating systems for corporate customers.
We start with an overview and discussion of the three main types of credit rating systems: the Early Warning systems, the Long-term Corporate (issuer) Ratings and 'Master Scale'-based Rating systems. Particular focus is put on the uses and misuses of each of the three system types, including their applicability to meet regulatory requirements, such as Basel III or IFRS 9, and their appropriateness to address business-related objectives, such as risk-adjusted pricing or operational risk management.
We then take a closer look at the 'Master Scale'-based Rating systems. 'Master scales' allocate a non-overlapping range of probabilities of default (PD) that are stable over time to each rating class. The rating methods for such systems need to produce accurate projections of the 1-year PD based on actually observed defaults. Starting from 'simpler' questions, such as what constitutes a default of a corporate customer, how to handle groups of legally or economically related entities from a data management perspective or how to build and maintain an appropriate historic record of defaults, we gradually dig into the core quantitative modelling methodology. Covered topics include statistical analysis of Corporate Balance Sheet KPIs, design and development of Integrated Rating Models based on quantitative factors and qualitative assessments, and model validation techniques. Throughout this part of the course we give practical advice and examples related to common challenges such as low default portfolios, missing / incomplete data and input data outliers.
After this, we turn to the Early Warning systems, which help the bank to identify reliably upcoming defaults or substantial credit risk increases of specific corporate customers on an ongoing basis. Customer account behaviour variables and expert opinion play a critically important role in the risk differentiation mechanics within such system, making the risk assessment a fully dynamic process. Building upon that, we present a more comprehensive framework to assess the impact of observable factors, such as market prices or macroeconomic indices, on the corporate customer's credit risk in a forward-looking manner.
Finally, we look at the Long-term Corporate (issuer) Ratings which express risk in relative rank order (i.e. they are ordinal measures of credit risk) and are not predictive of a specific frequency of default. The rating model development in this case needs to start from a specific industrial sector and only thereafter to combine the multiple specific models unto a unified rating scale. We briefly present an example of a Long-term Corporate Rating model within the healthcare sector and illustrate the process of mapping the model's results to a generic rating scale, such as S&P or Moody's.
Throughout the entire course, we revisit the topic of IFRS 9 multiple times, highlighting the best practices in applying the different types rating systems to comply with this new regulation. Covered topics include the development of IFRS 9 staging criteria, the estimation of lifetime PD and the calculation of impairment provisions.
Monday, March 23
09.00 - 09.15 Welcome and Introduction
09.15 - 12.00 Overview of the Credit Rating Systems
Corporate Customers: definition and main characteristics
The three main types of Credit Rating Systems
Relationship with regulatory requirements
Basel III: Internal Rating-Based Approach
ICAAP: calculation of Economic Capital
IFRS 9: Expected Loss model
Business process objectives
Using ratings for risk-adjusted pricing
Using ratings for operational decision-taking
Exercises and Q&A: how to approach the IFRS 9 regulation
12.00 - 13.00 Lunch
13.00 - 16.30 The Master Scale Ratings
What is a Master Scale?
Defining the corporate entities to be rated
Groups of related customers
Corporate structures and customer account management over time
The Balance Sheet KPIs
From Long List to Short List KPIs
Handling of incomplete data
Handling of outliers
Quantitative Model development
Integrated Rating Models
Quantification of qualitative assessments
Bringing in account behaviour variables: pros & cons
Low default portfolios
Small sample sizes
Exercises and Q&A: Master-Scale ratings and their applications in IFRS 9
Tuesday, March 24
09.00 - 09.15 Recap
09.15 - 12.00 Early Warning Systems
How to assess imminent credit risk?
Corporate customer behaviour variables
Early risk identification as an organisational challenge
Integrating expert opinions and quantitative rating models
Expected Loss vs. Probability of Default metrics
How to obtain forward-looking credit risk scores?
Generic framework for incorporation of additional observable variables
Case Study: improving an Early Warning system using market prices
Case Study: implementing the IFRS 9 Expected Loss model
12.00 - 13.00 Lunch
13.00 - 16.30 Long-term Corporate Ratings
Understanding ordinal risk measurement
Case study: Mapping internal ratings to a Rating Agency scale
Exercises and Q&A: Long-term corporate ratings and their applications in IFRS 9