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Euromoney Learning Solutions

Credit Portfolio Risk Management

Sep 23—26
4 days
London, United Kingdom
GBP 4195 ≈USD 5330
GBP 1048 per day

How it works


Educate yourself in credit portfolio modelling & management

This course is designed to help participants understand the significant components and features of credit portfolio modelling and management (CPM). The aim is to elucidate how a broad range of risk modelling and risk assessment approaches can be brought together to enable risk-based pricing and assessment—ultimately enabling portfolio managers to choose investments based upon fundamentals as well as market dynamics. During the course, the instructor—a former senior executive, board member and CRO of a large, emerging markets, publicly-listed banking group—will also endeavor to offer his experience in developing CPM techniques to fit the emerging markets landscape. This would include discussions of how the CPM framework can be developed in lieu of a complete systems architecture, when credit reference and credit rating bureaus are not available and when data and past history on customers is sparse. Primary focus is also given to best-practice and to quantitative methods that are actually demonstrated to work in practice across many of the 40 countries and 4 continents in which instructor has direct experience. In addition, participants will learn:

  • The elements necessary for internally developing and testing a ratings and scoring system that can be used with various exposure types—including privately listed, small to medium-sized enterprises (SMEs)
  • How to integrate a quantitative, credit scoring platform with a qualitative ratings system in Basel II/III-compliance fashion
  • How to develop the necessary CPM databases for estimating and validating scoring models and risk components, such as Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD)
  • Portfolio-level measures of risk, including measures of concentration using Copulae, tail dependence and other advanced measures
  • How to use Monte Carlo simulation and basic programming to develop and test scoring models and to model portfolio dependence, persistence, dynamics and stress-testing
  • How to use this integrated system in both origination and portfolio management activities
  • How to assess Expected Loss (EL) for provisioning and Unexpected Loss (UL) for capital allocation—both on a standalone and portfolio basis
  • How to create a Risk-Adjusted-Performance-Measurement (RAPM, aka RAROC) system

As well as useful techniques related to specific topics, such as:

  • Strategies for extracting important information from problem accounts
  • How to explain quantitative model results to qualitative-oriented directors and shareholders


Day 1

Overview of Credit Portfolio Management

What Credit Portfolio Management boils down to

  • Applications, scorecards and credits
  • Flat pricing
  • Risk-based pricing
  • Portfolio management and the portfolio manager
  • Portfolio performance metrics
  • Capital allocation and provisioning
  • Basel II related issues
  • Some structural hurdles in the emerging, Small-to-Medium Enterprise (SME) market
  • Hurdles in emerging markets

Foundations of the Credit portfolio management system:

  • Metrics for managing a portfolio
  • Creating an internal scoring and rating system
  • Exemplary ratings systems
  • Establishing the number of grades
    • Excel exercises
  • Relevant rating criteria
  • In Class Exercises in developing a ratings system

Day 2

Foundations of the Credit Portfolio management system (cont’d):

Developing the SME scoring model

  • Public companies
  • Dealing with private, unaudited companies
  • Structural models: Black-Scholes-Merton
    • Public firm variants
    • Private firm variants
    • What will like work in African markets
  • Excel exercises
  • Statistical models
  • Actuarial Models
  • Excel exercises

Testing and Validating these models

  • Excel Exercises

Overcoming hurdles in acquiring the necessary information in emerging markets

  • Scope for publicly listed companies and exposures
  • Databases and data management
  • Statistical models
  • Portfolio models
    • Asymptotic single risk factoring (ASRF) and loss measurement
    • Copulae based measurement

Day 3

From scores to PDs

  • Why we do not like statistical obligor PDs in retail
  • Segmentation
    • Vintage analysis
    • Delinquency status
  • Developing a PD model
    • Smoothing

Loss given default measurement (LGD)

  • Various loss model techniques
  • Workout LGDs
  • Actuarial LGDs
  • Statistically based LGD
  • Portfolio level (risk pool) LGDs

Exposure at default (EAD)

  • EAD modeling techniques
  • ASRF-based EAD
  • Statistically based EAD 

Day 4

Expected Loss (EL) and Unexpected Loss (UL) for Single exposures

  • Provisioning and Basel II-related issues
  • Economic capital allocation

Using your risk model for capital allocation Developing a Risk-adjusted-performance measurement (RAPM) system

  • Using EL and UL
  • Excel Exercises

EL and UL for Portfolios

  • Correlation and joint default estimation
  • Obtaining a Credit Value-at-Risk (CreditVaR)
  • Setting Economic Capital
  • Excel Exercises

Using RAPM in the portfolio setting

Testing and Validating the ratings system

  • Database validation
    • Default definition issues
    • The search for randomness—sampling issues
    • Ensuring database size is sufficient
    • Matching the data type with the model scope
    • Missing data issues and problems
  • Out-of-sample validation of scoring models
    • Construction of the testing sample
    • Confusion matrices and how to use them
    • Error cost determination
    • Non-parametric test statistics Rank tests and accuracy ratios ROC-Mann-Whitney-U versus Cumulative Accuracy Profiles
    • Various other tests
  • Model Selection using Accuracy Ratios and Costs
    • Confidence intervals for U-statistics
    • Why there is no target, “best” accuracy ratio
    • How to select models using in-sample and out-of-sample results in combination with error cost estimates

Day 5

Risk Component Backtesting

  • Probability of Default (PD) backtesting
    • Hosmer/Lemeshow
    • Binomial Tests
    • Brier Score
    • Other tests
    • Problems with the Central Limit Theorem in practice
  • Loss Given Default (LGD) backtesting
    • Choosing a low operational risk LGD estimation method
    • Backtesting and confidence intervals

Portfolio stress testing, provisioning and recapitalisation

  • Defining stress tests
  • Distinguishing scenarios and sensitivity analysis
  • Interpreting results
  • Articulating results internally and to investors and regulators
  • Incorporating in the Internal Capital Adequacy Assessment Process (ICAAP)

Concluding Remarks

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Maurice is a global management consultant, former banking executive and experienced, public company board member that has worked in over 60 countries. He has been an advisor and consultant to boards and executive teams with numerous, major banks, investment banks, central banks and investment fun...


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