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Course 2018-2019 a.y.

20249 - CREDIT RISK MANAGEMENT

Department of Finance

Course taught in English


Go to class group/s: 31

CLMG (6 credits - I sem. - OP  |  SECS-P/11) - M (6 credits - I sem. - OP  |  SECS-P/11) - IM (6 credits - I sem. - OP  |  SECS-P/11) - MM (6 credits - I sem. - OP  |  SECS-P/11) - AFC (6 credits - I sem. - OP  |  SECS-P/11) - CLEFIN-FINANCE (6 credits - I sem. - OP  |  SECS-P/11) - CLELI (6 credits - I sem. - OP  |  SECS-P/11) - ACME (6 credits - I sem. - OP  |  SECS-P/11) - DES-ESS (6 credits - I sem. - OP  |  12 credits SECS-P/11) - EMIT (6 credits - I sem. - OP  |  SECS-P/11) - GIO (6 credits - I sem. - OP  |  SECS-P/11)
Course Director:
GIACOMO DE LAURENTIS

Classes: 31 (I sem.)
Instructors:
Class 31: GIACOMO DE LAURENTIS


Prerequisites

There are no prerequisites of finance, statistics and/or statistical software for those attending the course.


Mission & Content Summary
MISSION

Credit risk management is a relatively new and rapidly evolving topic, key for bank management. In terms of regulatory capital absorbed, credit risk account for 90% of total capital required. Banks are recruiting today, ECB and other authorities are recruiting, insurance companies and non-bank financial institutions are recruiting, and also non-financial corporations are recruiting graduates with good knowledge of the field.

CONTENT SUMMARY
  • Introduction: concepts, methodologies and tools of credit risk management.
  • Building statistical-based scoring systems of probability of default: definition of default to be used.
  • Sampling, data mining and transformations (Case study based on SPSS).
  • Univariate analysis. Monotonicity, statistical requirements and predictive power of individual financial ratios (Case study based on SPSS).
  • Transformations of financial ratios in order to maximize their predictive power (Case study based on SPSS).
  • Models estimation (Case study based on SPSS).
  • Models performance measurements, comparability of different models; from scorings to ratings: choosing cut-offs. Scoring calibration and rating quantification (Case study based on SPSS).
  • Internal validation and regulatory validation. Quantitative and qualitative model validation; benchmarking.
  • Credit risk measures taxonomy and their impacts on portfolio models structure
  • Portfolio models.
  • Credit risk pricing and risk adjusted performance measures. Internal data and market data.

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Get the state of the art of methodologies and practices of credit risk management in financial institutions, as well as in finance departments of large non-financial corporations.
  • Link risk measurement models and management policies  in a comprehensive picture of key methodologies and tools that are currently under implementation in banks, non-financial institutions, and rating agencies. Both rating systems and portfolio models development stages will be considered.  
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Capitalizing on large real-world databases and statistical software tools such as SPSS, students will learn how to to build, to manage and to validate risk models, both statistical based rating systems and credit portfolio models.

Teaching methods
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
  • Group assignments
DETAILS

All lectures are held in a computer room. Each topic involves (class or home) exercises and incidents. A unique case study, based on a real world database of SMEs financial data, are used for building and validating a statistical based rating system during many class sessions; SPSS is used as statistical tools, because it is used in real world risk departments, it is quite friendly also for newcomers and a book published by Wiley is based on the same case study and SPSS application (and therefore is a perfect textbook for the course).  A group assignment is optional; each student in a group may earn from 0/30 to 2/30 additional points to those earned in the written exam.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING AND NOT ATTENDING STUDENTS
    • A written exam based on three short essays tries to check the proper understanding of areas of credit risk management that have been covered in class. The final mark is expressed in thirties (X/30).
    • A group assignment is optional. Each student in a group may earn from 0/30 to 2/30 additional points to those earned in the written exam; these additional points can be used up to the end of September 2019. The group assignment is devoted to enhance not only how-to-do capabilities, but also team management, project management and presentation skills.

    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS
    • DE LAURENTIS, MAINO, MOLTENI, Developing, Validating, and Using Internal Ratings. Methodologies and case studies, Wiley, 2010 (this book also has an Italian translation: DE LAURENTIS, MAINO, I rating a base statistica. Sviluppo, validazione, funzioni d’uso per la gestione del credito, Bancaria Editrice, 2009).
    • RENAULT DE SERVIGNY, Measuring and managing credit risk, McGraw-Hill 2004, (chapter 6).
    • Slides sets, SPSS print-outs and case studies (available on the course web site).
    Last change 09/06/2018 17:32