Магистранты будут изучать моделирование кредитных рисков
Во втором модуле студенты магистерской программы "Финансы" будут изучать дисциплину "Credit risk modeling". Курс предложен Международной лабораторией количественных финансов, и к нему будут привлекаться не только исследователи, но и специалисты-практики. Инициатором создания курса является профессор Карминский Александр Маркович
The course “Credit Risk Modeling” introduces contemporary methods of credit risk estimation and modeling applied in the financial sector. Credit risk emerges from the possibility that a borrower will not cover its liabilities. In the beginning of the course, we discuss credit risk for a single borrower, then – portfolio models. We pay attention to Basel II and Basel III supervision norms and their application in Russia. In addition, we review tool of credit risk management including the use of derivatives. We also consider limitations of quantitative methods applied in credit risk management.
The course “Credit Risk Modeling” is designed for implementation at:
· Master’s program specialized in risk-management in financial organization;
· Career enhancement training for young specialists to improve their skills in the field of credit risk managing.
Course Targets
The principal targets of the course “Credit Risk Modeling” are:
· To classify the risks of banking activities. To define the order in classification and significance of credit risk.
· To consider approaches to credit risk management in Russian financial companies.
· To discuss the requirements of financial regulators on building and validating credit risk models.
· To present the expectation of the largest Russian banks from young specialists in risk-management and current career opportunities at that companies to the audience.
Methodological novelty of the course
Each topic is presented by a lecture and an experienced practitioner employed by one of the largest Russian financial institutions (Sberbank, Gazprombank, Alpha Bank, The Central Bank of Russia etc.). Student are offered a unique chance to enquire about the qualifications of high importance for young specialists in risk management and carrier opportunities at the listed above companies.
Course place in the innovative qualifications development system
Extensive growth was a priority for financial organizations before the Recession 2008-2009. As a result, huge risks had been accumulated before they materialized during the recent financial crisis. The issue of sustainable development of financial institutions took the leading place in the world agenda in 2009-2010. This statement is confirmed by the key points stressed in development strategies of the largest Russia banking institutions (e.g. Sberbank and VTB Group) for the period 2010-2013: initially quantitative targets dominated (market share, size of loan portfolio), while after 2010 the focus is on qualitative measures (cost of risk, non-performing loans to total credit portfolio ratio).
Sustainable development of credit institutions is impossible without adequate risk estimation models. Meanwhile credit risk is the most significant for credit institutions.
In line with the IRB approach described in Basel II Accord issued by The Basel Committee on Banking Supervision, building high-quality credit risk models enables a bank to gain in economic capital required and, therefore, increase business marginality. It is also stated in the document that the issue of verifying such models is central under IRB approach implementation.
The Course covers a topic on creating and verification of credit risk managing models. Experts on the Russian financial sector and banking regulations will explicitly present their view on the task and proper policies.
I. Course Outline
Key topics in the course
|
№ |
Sections and topics covered |
Overall (ac. hours) |
Auditorium block (ac. hours) |
Home block (ac. hours) | |
|
including | |||||
|
Lec |
Sem | ||||
|
1. |
Introduction. Course outline and literature recommended. Banking risks classification. |
2 |
1 |
0 |
1 |
|
2. |
Credit risk. Key credit risk measures (default definition, LGD, EL, VaR etc.) |
6 |
3 |
0 |
3 |
|
3. |
Credit risk models for a single borrower / loan (scoring; ratings; rating-based models; structural models, reduced form models). |
10 |
4 |
2 |
4 |
|
4. |
Portfolio credit risk models. |
6 |
2 |
0 |
4 |
|
|
Practicum on topics 1-4. |
4 |
|
4 |
|
|
5. |
Financial regulators' requirements on credit risk models development and validation. Basel II Accord. Basel III Accord. |
8 |
2 |
2 |
4 |
|
6. |
Credit risk management in a financial organization. Relevant derivatives. |
8 |
2 |
2 |
4 |
|
7. |
Market risk. Liquidity risk. Peculiarities, approaches to estimating and managing. |
8 |
2 |
2 |
4 |
|
|
Preparation for an exam. |
8 |
|
2 |
6 |
|
|
TOTAL: |
60 |
16 |
14 |
30 |
Comparison to Russian and foreign analog courses
In this section, we compare the course “Credit Risk Modeling” to three courses of close duration:
1. Risk-management (RM). Part 1. Credit Risk (40-50 ac. h.)
NRU HSE in cooperation with London School of Economics, ICEF, 2nd year Master’s students, taught in English
2. Financial Risk-Management (FRM). Part 2. Credit Risk Models (40 ac. h.)
NRU HSE, Department of Finance, 2nd year Master’s students, taught in Russian
3. Credit Risk (CR) (100-120 ac. h.)
New York University, The Department of Finance, MBA Program[1]
Comparison of the key topics in the courses
|
Credit Risk Models |
RM |
FRM |
CR |
Note |
|
Topic 1 |
+ |
+ |
- |
As opposed to three analog courses, at the first lecture already we will classify the risks of banking and define the place of credit risk in our classification. The issues of risk-management development history as a science is only mentioned. However we focus on the origin and consequences of the recent Financial Crisis 2008-2009. At the same time the discussion on role of derivatives is not principal here yet; we will tackle it in the further topics. |
|
Topic 2 |
+ |
+ |
+ |
In our course, we consider credit risk exactly trying to avoid contiguous topics. |
|
Topic 3 |
+ |
+- |
+ |
In this section we will discuss the main credit risk models for a single borrower. We will also address the use of such models for analysis of different agents: individuals, corporates, sovereign borrowers (we will follow CR in this regard). |
|
Topic 4 |
+ |
+ |
+ |
The peculiarity of out course is segmentation between credit risk models for a single borrower and portfolio ones to stress differences between them. The similar technique was applied in RM and CR. |
|
Topic 5 |
+- |
+- |
- |
Unlike in analog courses, we discuss the issue of international banking regulation standards and their adoption in Russia in details. In additions, we will invite an experienced speaker on behalf of the Central Bank of Russia and business who is able to provide a deep insight on the topic. |
|
Topic 6 |
+ |
+- |
+ |
Following CR, in our course we discussed the opportunities to employ derivatives in credit risk management. We will also define the role of derivative use during the recent World Financial Crisis. |
|
Topic 7 |
+ |
+ |
+ |
This section will be covers with broad participation of industry experts. We focus on the relation between credit, market and liquidity risks as well as on managing these types of risk. |
Generally, in the theoretical part of the course “Credit Risk Modeling” we conform to the structure of the analog courses suggested for comparison. The courses are based on different literature and this partially explains differences in structure and content.
As opposed to the courses “Financial Risk Management” and “Credit Risk”, our program assumes a significant practical part in the forms of solving everyday risk-management problems in business and commutations between students and banking industry gurus.
Another peculiarity of the course “Credit Risk Modeling” is our focus on the documents by The Basel Committee on Banking Supervision, namely Basel II and Basel III Accords. Also we cover an interesting topic of implementing these regulations in Russian banking.
The issue of derivative use in risk-management is often beyond the scope in Russian courses on the topic. Meanwhile in our course this issue is a core one. We try to exhibit the entire role of derivatives in Russian financial system and the recent crisis.
Fulfilling the home assignments planned a students will master skills potentially useful in everyday activity of the risk-management department in a financial company.
II. Key topics in details
|
Topic 1. Introduction. Course outline and literature recommended. Banking risks classification |
|
· To deliver the aim and targets of the course. · To present the course structure, knowledge control forms, grade determination system and the recommended literature. · To define the place of risk-management in banking. To classify types of risk. |
|
Required reading: · Bluhm C., Overbeck L., Wagner C. (2010) Introduction to Credit Risk Modeling. Chapman and Hall/CRC, 2010. |
|
Lecturer[2]: Alexander Karminsky |
|
Topic 2. Credit risk. Key credit risk measures (default definition, LGD, EL, VaR etc.) |
|
· Credit risk definition and components. · Default definition. · Types of credit risk models and differences between them. · Key quantitative methods of credit risk measurement. |
|
Required reading: · Bluhm C., Overbeck L., Wagner C. (2010) Introduction to Credit Risk Modeling. Chapman and Hall/CRC, 2010. Additional reading: · Karminsky A., Peresetsky A. Ratings as Measure of Financial Risk: Evolution, Function and Usage. Journal of the New Economic Association,issue 1-2, pp. 86-102. Link: http://www.econorus.org/journal/pdf/Karminsky_Peresetsky_1-2.pdf |
|
Lecture: Alexander Kostrov. |
|
Topic 3. Credit risk models for a single borrower / loan. |
|
· Scoring, logit and probit models. · Ratings and rating agencies. · Rating-based models. · Merton’s model and its modifications. |
|
Required reading: · Bluhm C., Overbeck L., Wagner C. (2010) Introduction to Credit Risk Modeling. Chapman and Hall/CRC, 2010. Additional reading: · Karminsky A. M., Kostrov A., Murzenkov T. Comparison of Default Probability Models: Russian Experience / Working papers by NRU Higher School of Economics. Series FE "Financial Economics". 2012. No. WP BRP 06/FE/2012. |
|
Lecture: Alexander Karminsky Banking industry expert: Alexey Morgunov[3] |
|
Topic 4. Portfolio credit risk models. |
|
· Peculiarities of credit risk analysis for a loan portfolio. · Credit Metrix Model. · Credit Risk+ Model. |
|
Required reading: · Bluhm C., Overbeck L., Wagner C. (2010) Introduction to Credit Risk Modeling. Chapman and Hall/CRC, 2010. Additional reading: · Introduction to Credit Metrix: http://homepages.rpi.edu/~guptaa/MGMT4370.10/Data/CreditMetricsIntro.pdf |
|
Lecture: Alexander Kostrov. Banking industry expert: Alexey Morgunov. |
|
Topic 5. Financial regulators' requirements on credit risk models development and validation. Basel II Accord. Basel III Accord. |
|
· The purpose and advantages of building credit risk models in banking. · Regulator’s requirement to risk-management models design in banking. Validation of models. · Basel II, Basel III. Significant shifts in supervision and regulations. Basel Accords and their adoption in Russia. |
|
Required reading: · Information letter of the bank of Russia on “Implementing new approaches to supervision over credit organizations to improve stability of the Russian banking sector”: http://cbr.ru/Eng/press/PR.aspx?file=131007_190625intern1.htm · Basel Committee on Banking Supervision, International regulatory framework for banks (Basel III Accord): http://www.bis.org/bcbs/basel3.htm Additional reading: · Basel Committee on Banking Supervision, International regulatory framework for banks (Basel II Accord): |
|
Lecture: Alexander Karminsky. Banking industry expert: Mikhail Bezdudny,[4] Ksenia Totmyanina[5]. |
|
Topic 6. Credit risk management in a financial organization. Relevant derivatives. |
|
· Purpose of credit risk management in a bank. · Approaches to credit risk reduction in a bank. · Derivative application to handle credit risk. · Securitization. |
|
Required reading: · Damodaran A. Strategic Risk Taking: A Framework for Risk Management. Wharton School Publishing – 2007, 408 p. Additional reading: · Uzun H., Webb E. Securitization and risk: empirical evidence on US banks. Journal of Risk Finance, The Volume: 8 Issue: 1 2007 |
|
Lecture: Alexander Kostrov |
|
Topic 7. |
|
Market risk. Liquidity risk. Peculiarities, approaches to estimating and managing. |
|
· Market risk and its sources. · Liquidity risk and its sources. · Market risk managing. · Liquidity risk management. |
|
Required reading: · Basel Committee on Banking Supervision, International regulatory framework for banks (Basel III Accord): http://www.bis.org/bcbs/basel3.htm Additional reading: · Amit Mehta, Max Neukirchen, Sonja Pfetsch, Thomas Poppensieker Managing market risk: Today and tomorrow. McKinsey Working Papers on Risk, Number 32. May 2012. |
|
Lecture: Alexander Kostrov Banking industry expert: Sergey Zamkovoy[6] |
Sample open questions for self-control
· What are the main risks in banking?
· What is the intuition behind using VAR to measure risks?
· Specify and characterize the key types of credit risk models.
· Why do we distinguish between credit risk models for a single borrower and portfolio models?
· What is the moral hazard for rating agencies? Define the business structure of the largest rating agencies.
· What are the principal shifts in supervision with Basel III introduced by The Basel Committee on Banking Supervision?
· Are Basel III norms compulsory or voluntary in Russia? Is Basel I implemented in Russia? What about Basel II?
· What derivatives are useful to deal with credit risk? Define in details.
· What is the difference between liquidity and market risk? Are they related?
Sample questions for mock and final control
1. Suppose there is a company with default probability of x% in the year 1, of y% in the year 2 and of z% in the year 3, and of q% in the year 4. What is the probability of default probability after three years?
2. Using the distributed dataset estimate the probability of default (logit model) for the Russian banks over the period from 2005 to 2006 in Stata or another relevant application.
a. Report and interpret coefficient values, signs and significance.
b. Compute the predicted by the model default probabilities for each bank.
c. What are the marginal effects for each factor? Report the results.
d. What is the forecasted number of insolvent banks if ones with predicted default probability over 20% are classified as potentially insolvent?
3. Using the 1-year transition matrix provided compute the default probabilities for a four-year period. Assume the transition matrix is unchanged over time.
4. Given the individual default probabilities for bonds in a portfolio and cross default probabilities, estimate the default probability of the portfolio.
5. Given the size of the loan portfolio in a bank and its default probability, compute the risk-weighted assets and economic capital required under Basel II.
III. Midterm and final examinations
Forms of current, midterm and final control
During the course, we will grade class participation and activity of students. We expect the students to complete two home assignments after topics 4 and 7 respectively. We allocate two weeks per each home assignment. Note than home assignment 2 should be submitted before the final exam.
There is also a final exam to control the learning results after the course.
Expected volume of written tasks
Each home assignment contains 1-2 tasks on topics covered in the class.
Grade determination (calculation methodology)
|
Control Form |
Weight of activity | |
|
Version 1 |
Version 2 | |
|
Class participation: Sem |
20% |
20% |
|
Home assignment: HW |
0% |
40% |
|
Final exam: Exam |
80% |
40% |
To convert into a sten score apply the standard rules of mathematical rounding for grades above 4.
Appendix to the course syllabus
Methodological recommendation to lecturers and class instructors
Preparing the lecture material it may be useful to use an application MS PowerPoint with Think-Cell add-in for presentations. Besides, it is often beneficial for the class quality to consider case studies during the seminars on the topic discussed. In addition, it is highly recommended for the industry experts invited to share their own experience in finance.
Each topic should be discussed with regard to shifts in the Russian banking sector and economy after the World Financial Crisis 2008-2009. It is also highly recommended to address the limitations of relevant quantitative analysis in the light of each topic discussed.
Methodological recommendation to students
We assume that students are familiar with basic mathematical and econometric tools employed in the course: introduction to statistics, mathematical analysis, stochastic analysis and econometrics.
For successful learning, it is important to attend lectures and participate in class discussions. To cope with the final exam properly it is especially important to scrutinize home assignments. In addition, students will be offered an opportunity to consult a lecture on intricate tasks during the announced office hours. We assume that lecture and seminar materials are enough to understand the topics covered if home assignments are completed responsibly. In addition, student will have an opportunity to inquire banking practitioners about qualification of young specialists highly demanded in the banking industry.
In conclusion, we are ready to support and develop the interested students’ research interest and potential in the fields of credit risk and default probability modeling. We are open to consider the particular candidates to take part in the research projects implemented at the Higher School of Economics on related topics.
[1] Link to the MBA course website, New York University, Stern School of Business: http://www.stern.nyu.edu/experience-stern/about/departments-centers-initiatives/academic-departments/finance/academic-programs/mba-overview
[2] Hereinafter we specify a shortlist of expected lectures / banking industry experts. If multiple persons are mentioned – the final composition is under consideration.
[3] Department of Risk-Management, Gazprombank.
[4] Depatrment of banking regulations and supervision, The Central bank of Russia.
[5] Department of Risk-management, Sberbank of Russia.
[6] Department of Strategy and Risk-management, Nomos-bank.