Bachelor 2020/2021

Risk Assessment Models

Type: Elective course (Economics)

Area of studies: Economics

Delivered by: Department of Economics

Where: St.Petersburg School of Economics and Management

When: 4 year, 3 module

Mode of studies: offline

Instructors: Marat Z. Kurbangaleev

Language: English

ECTS credits: 3

Contact hours: 28

Course Syllabus

Abstract

This course is an introduction to the quantitative risk assessment and modeling. It starts with the general concepts of risk analysis and shows how one can think of risk, define its structure, and measure it. Then it turns to the modeling process: discuss the role of the data and assumptions and how they should be handled, reviews key components of risk model and methodological principles of their construction, and finally explores some validation techniques that help to identify if the model performs the way it is expected to perform. Finally, the course provides practical experience in applying learned concepts and methods for modeling market and credit risks. This course can be useful for those who seek their career in the financial industry, and in financial risk management, in particular.

Learning Objectives

• To learn general concepts of risk analysis and modeling.
• To practice applying those concepts and methods to the financial risk in a close-to-real-life setting.

Expected Learning Outcomes

• Analyze risks and identify their components for assessment
• Assess the quality and limitations of risk models in a different context
• Design, estimate, apply and validate several practical risk models
• Formulate basic concepts and principles of risk assessment
• Select and process data for the quantitative risk assessment
• Make conservative assumption and evaluate them from the perspective of practically reliable risk assessment

Course Contents

• Basics of risk analysis and modeling
  Risk definition. Risk factors. Exposure. Horizon. Profit/loss distribution. Expected and unexpected financial result. Risk measures (Value-at-Risk (VaR), Expected Shortfall (ES)). Coherence.
• Data selection and quality assessment
  Risk data structure. Reliability. Representativity. Timeliness. Homogeneity. Completeness. Correctness. Impact on quality of risk assessment.
• Risk model design, assessment, and validation
  Assumptions and approximations. Risks dependency and aggregation. Dimensionality and its reduction. Serial correlation and time scaling. Classical approaches: Variance-Covariance, Historical Simulation, Monte-Carlo. Backtesting. Stress-testing.
• Modeling and assessing portfolio market risk
  Market data. Fund risk: returns algebra, volatility and correlation modeling, factor models, risk of stock portfolio. Interest rate risk: interest rate curve, Fisher-Weil duration, key rate duration, PCA-decomposition, risk of portfolio of non-defaultable bonds. Foreign exchange risk: aggregating with other market risk factors.
• Modeling and assessing portfolio credit risk
  Probability of Default (PD), Exposure-at-Default (EAD), Loss-Given-Default (LGD). Default statistics. Credit ratings and their discriminatory power. PD estimates and their accuracy. Binomial model vs Markov-chain model. Default correlation modelling. Vasicek credit portfolio loss model. Credit VaR and ES. Credit loss provisions (IFRS 9) and capital (Basel III).

Assessment Elements

• Individual homework on risk metrics and risk data
  1 week,1 attempt, online submission, no retake.
• Group assignment on modeling and measuring market risk
  Common deadline – 10 days before Exams, up to 2 attempts for each assignment, online submission.
• Group assignment on modeling and measuring credit risk
  Common deadline – 10 days before Exams, up to 2 attempts for each assignment, online submission.
• Exam
  All students whose unrounded weighted average grade on interim control is less than 4 must take an exam. Other students will be offered to accept this average interim control grade as a final grade. In this case, a student must decide whether to take an exam or not. If a student does not take an exam, the weight of the exam proportionally distributes over other weights and the final grade derives solely from interim control grades. Students who take an exam do not have an option to switch back to interim grade and their final grades must be derived via a basic formula that includes exam results.

Interim Assessment

• Interim assessment (3 module)
  0.2 * Exam + 0.3 * Group assignment on modeling and measuring credit risk + 0.3 * Group assignment on modeling and measuring market risk + 0.2 * Individual homework on risk metrics and risk data

Bibliography

Recommended Core Bibliography

• Alexander J. McNeil, Rüdiger Frey, & Paul Embrechts. (2015). Quantitative Risk Management: Concepts, Techniques and Tools Revised edition. Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.pup.pbooks.10496

Recommended Additional Bibliography

• Gunter Löeffler, & Peter N. Posch. (2007). Credit Risk Modeling Using Excel and VBA. Wiley.
• Jon Danielsson. (2011). Financial Risk Forecasting : The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab. Wiley.