Laboratory of Bank Loan Pricing
A.Y. 2021/2022
Learning objectives
Students will gain a thorough understanding of the practices currently used by banks when originating (pricing) a bank loan. The course will offer insight into the various areas of the bank involved when pricing a bank loan (Finance department for funding, Chief Risk Officer area for loans balancesheet provisioning, CFO area for capital allocation). The course will then present students the case for the impact on the Bank Loan Pricing of the new Securitization Framework and advanced Machine Learning techniques for default prediction (internal PD model) and projection (satellite PD model).
Expected learning outcomes
At the end of the course students will gain an overview of the various departments at work within a bank when originating a loan. They will also get a measure of the impact on loan pricing of alternative Machine Learning methodologies for default prediction and the impact of the new securitization framework for loan portfolio capital allocation.
Lesson period: Third trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Third trimester
Classes will take place on Teams if not otherwise comunicated
Course syllabus
Below a list of subjects to be discussed in class:
Bank Loan Pricing via target IRR: CoL (cost of liquidity) + CoC (cost of credit) + CoK (cost of capital).
CoL: Blended curve construction.
CoL: regulatory framework: LCR and NSFR.
CoK: Regulatory capital and Risk Weighted Assets
CoK: Risk Weighted Assets for Standardized and Advanced Internal Rating Based Banks.
CoK: new securitization framework and its impact on the RWA for a securitized portfolio
CoK: Risk Weighted Assets calculation for a securitized portfolio
CoK: Significant Risk Transfer for a securitized portfolio
CoC: Lifetime Expected Loss. Probability of Default and Loss Given Default.
CoC: Default prediction models. Typical dataset structure, module layout and prediction horizon.
CoC: Default prediction models. Univariate and Multivariate Logistic regressions.
CoC: Default prediction models. Input variables encoding and Cross Validation.
CoC: Default prediction models. Regression and Classification Trees.
CoC: Default prediction models. Random Forest: Bagging (Bootstrap aggregating) and Boosting ensemble algorithms.
CoC: Default prediction models. Bank Loan Pricing impact of alternative machine learning and ensemble algorithms.
CoC: Macro-economic scenario projections. Satellite Credit Portfolio Models as sparse problems.
CoC: Satellite Credit Portfolio Models: Bayesian Model Average.
Bank Loan Pricing via target IRR: CoL (cost of liquidity) + CoC (cost of credit) + CoK (cost of capital).
CoL: Blended curve construction.
CoL: regulatory framework: LCR and NSFR.
CoK: Regulatory capital and Risk Weighted Assets
CoK: Risk Weighted Assets for Standardized and Advanced Internal Rating Based Banks.
CoK: new securitization framework and its impact on the RWA for a securitized portfolio
CoK: Risk Weighted Assets calculation for a securitized portfolio
CoK: Significant Risk Transfer for a securitized portfolio
CoC: Lifetime Expected Loss. Probability of Default and Loss Given Default.
CoC: Default prediction models. Typical dataset structure, module layout and prediction horizon.
CoC: Default prediction models. Univariate and Multivariate Logistic regressions.
CoC: Default prediction models. Input variables encoding and Cross Validation.
CoC: Default prediction models. Regression and Classification Trees.
CoC: Default prediction models. Random Forest: Bagging (Bootstrap aggregating) and Boosting ensemble algorithms.
CoC: Default prediction models. Bank Loan Pricing impact of alternative machine learning and ensemble algorithms.
CoC: Macro-economic scenario projections. Satellite Credit Portfolio Models as sparse problems.
CoC: Satellite Credit Portfolio Models: Bayesian Model Average.
Prerequisites for admission
Students are required to have already completed the following courses:
https://www.unimi.it/it/corsi/insegnamenti-dei-corsi-di-laurea/2022/data-mining-and-computational-statistics
https://www.unimi.it/it/corsi/insegnamenti-dei-corsi-di-laurea/2022/risk-management
In order to complete the final assignment, students will need to have programming skills in any language of their choice (matlab, R, python, C)
https://www.unimi.it/it/corsi/insegnamenti-dei-corsi-di-laurea/2022/data-mining-and-computational-statistics
https://www.unimi.it/it/corsi/insegnamenti-dei-corsi-di-laurea/2022/risk-management
In order to complete the final assignment, students will need to have programming skills in any language of their choice (matlab, R, python, C)
Teaching methods
videosharing with the class material
Teaching Resources
Sala-i Martin, X., Doppelhofer, G., and Miller, R. I. (2004). Determinants of longterm
growth: A bayesian averaging of classical estimates (bace) approach. American Eonomic Review,
94(4):813-835.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2):461-464.
Torresetti, R. (2021), A comparison of the performance of alternative Machine Learning algorithms on a credit risk dataset, Working Paper, available at www.ssrn.com
Torresetti, R. (2021), Bayesian Model Averaging for Satellite Models in Bank's Credit Risk Projections, Working Paper, available at www.ssrn.com
growth: A bayesian averaging of classical estimates (bace) approach. American Eonomic Review,
94(4):813-835.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2):461-464.
Torresetti, R. (2021), A comparison of the performance of alternative Machine Learning algorithms on a credit risk dataset, Working Paper, available at www.ssrn.com
Torresetti, R. (2021), Bayesian Model Averaging for Satellite Models in Bank's Credit Risk Projections, Working Paper, available at www.ssrn.com
Assessment methods and Criteria
Assignment to be submitted individually by each student (no group projects)
SECS-P/01 - ECONOMICS
SECS-P/02 - ECONOMIC POLICY
SECS-P/02 - ECONOMIC POLICY
Laboratory activity: 20 hours
Professor:
Torresetti Roberto
Professor(s)
Reception:
all Fridays 18:00-19:00, from 23rd April to 25th June