Risk Assessment Models
- 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.
- Analyze risks and identify their components for assessment
- 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
- Design, estimate, apply and validate several practical risk models
- Assess the quality and limitations of risk models in a different context
- Basics of risk analysis and modelingRisk 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 assessmentRisk data structure. Reliability. Representativity. Timeliness. Homogeneity. Completeness. Correctness. Impact on quality of risk assessment.
- Risk model design, assessment, and validationAssumptions 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 riskMarket 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 riskProbability 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).
- Individual homework on risk metrics and risk data1 week,1 attempt, online submission, no retake.
- Group assignment on modeling and measuring market riskCommon deadline – 10 days before Exams, up to 2 attempts for each assignment, online submission.
- Group assignment on modeling and measuring credit riskCommon deadline – 10 days before Exams, up to 2 attempts for each assignment, online submission.
- ExamAll 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 (1 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
- 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
- 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.