Subjects

Credit Risk in Banking - NMFM537

Title:	Kreditní riziko v bankovnictví
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2022
Semester:	winter
E-Credits:	3
Hours per week, examination:	winter s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	English, Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	RNDr. Václav Kozmík, Ph.D. Marek Teller
Class:	M Mgr. FPM M Mgr. FPM > Volitelné M Mgr. PMSE M Mgr. PMSE > Povinně volitelné
Classification:	Economics > Financial Economics Mathematics > Financial and Insurance Math.
Incompatibility :	NMFP461
Interchangeability :	NMFP461
Is incompatible with:	NMFP461
Is interchangeable with:	NMFP461, NFAP042

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: RNDr. Jitka Zichová, Dr. (16.05.2019)

First part of this course covers most popular statistical models for credit risk scoring - logistic regression, decision trees, gradient boosting method. In following lectures, students will get familiar with procedures how to use scoring models in practice and how to estimate risk of single loan and whole portfolios. Emphasis will be put on the link between theoretical knowledge and procedures used in banking practice.

Aim of the course -

Last update: RNDr. Jitka Zichová, Dr. (16.05.2019)

The objective of the lecture is to give an overview of the methods connected with credit risk management. The lecture will make students acquainted with the current trends in credit risk management.

Course completion requirements -

Last update: RNDr. Václav Kozmík, Ph.D. (26.09.2020)

Home assignments and oral exam. Home assignment will cover preparation of both classical and modern predictive models on real data.

Literature -

Last update: RNDr. Václav Kozmík, Ph.D. (26.09.2020)

[1] Hosmer, David W. and Stanley Lemeshow, Applied Logistic Regression, 2nd ed., New York; Chichester, Wiley, 2000, ISBN 0-471-35632-8.

[2] Chen T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, https://arxiv.org/abs/1603.02754

Teaching methods -

Last update: RNDr. Václav Kozmík, Ph.D. (27.09.2021)

Lecture supported by slides. There is a study text which covers most of the lecture content.

Requirements to the exam -

Last update: Sebastiano Vitali, Ph.D. (12.10.2017)

The requirements for the exams follow the syllabus of the course and they are limited to presented topics at the lectures.

The exam consists of an oral examination.

Syllabus -

Last update: RNDr. Jitka Zichová, Dr. (16.05.2019)

1) Most popular statistical models for credit risk scoring - logistic regression, decision trees, gradient boosting method.

2) Procedures how to use scoring models in practice and how to estimate risk of single loan and whole portfolios. Emphasis will be put on the link between theoretical knowledge and procedures used in banking practice.

Entry requirements -

Last update: RNDr. Jitka Zichová, Dr. (16.05.2019)

Basic knowledge of mathematical statistics (particularly linear regression), theory of probability and mathematical analysis. For practical usage of the lecture content it is ideal to have basic knowledge of R or Python languages.