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Course 2016-2017 a.y.

20249 - CREDIT RISK MANAGEMENT


CLMG - M - IM - MM - AFC - CLEFIN-FINANCE - CLELI - ACME - DES-ESS - EMIT - GIO
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


Course Objectives

The course focuses on 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. Risk measurement models and management policies are constantly linked in order to provide a comprehensive picture of key methodologies and tools that are currently under implementation in banks, non-financial institutions, and rating agencies. The course capitalizes on large real-world data-bases and statistical software tools such as SPSS, in order to learn how to build, manage and validate risk models; lectures are held in computer room. SPSS is downloaded on students’ lap tops.


Course 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.

Detailed Description of Assessment Methods

Written exam (essay). 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. No difference in exam for students attending and not attending the lectures.


Textbooks
  • G. De Laurentis, R. Maino, L. Molteni, Developing, Validating, and Using Internal Ratings. Methodologies and case studies, Wiley, 2010 (this book also has an Italian translation: I rating a base statistica. Sviluppo, validazione, funzioni d’uso per la gestione del credito, Bancaria Editrice, 2009).
  • R. 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).

Prerequisites
There are no particular prerequisites of statistics and/or SPSS software for those attending the course.
Last change 20/05/2016 14:30