Quantitative Risk Management
2nd Semester, Course Code: ΜΕΧΤΕ201
Credits: 7,5
Learning Outcomes
- Understanding of the principles and frameworks of risk management in the fintech industry.
- Familiarity with various risk measures, including Value at Risk (VaR) and Expected Shortfall (ES), and their application in fintech contexts.
- Knowledge of univariate and multivariate risk modeling techniques, including copulas and tail dependence, tailored for risk management in fintech.
- Understanding of stress testing frameworks and their relevance in assessing and managing risk in fintech environments.
- Knowledge of credit risk modeling techniques and their application in fintech-specific scenarios.
- Familiarity with market risk modeling and volatility estimation methods relevant to fintech risk management.
- Understanding of operational risk management challenges specific to the fintech industry.
- Knowledge of cybersecurity risks and risk management strategies specific to fintech applications.
- Awareness of emerging risks in fintech, such as AI and blockchain, and their implications for risk management.
- Ability to apply risk measurement and modeling techniques, including VaR and ES, in fintech risk scenarios.
- Proficiency in selecting and implementing appropriate univariate and multivariate risk models tailored for fintech environments.
- Competence in credit risk modeling techniques and their practical application in fintech credit assessments.
- Skill in identifying and assessing operational risks specific to fintech companies and implementing risk control frameworks.
- Ability to recognize and address emerging risks in fintech using appropriate risk management techniques.
General Competences
- Proficiency in making informed decisions based on critical analysis and evaluation of available information.
- Capacity to plan and execute individual work effectively and efficiently.
- Ability to contribute actively to team dynamics, share knowledge, and support team objectives.
- Adaptability to different work practices and perspectives in an international context.
- Effective communication and collaboration with professionals from different fields.
- Ability to critically evaluate existing research and identify gaps for further exploration.
- Ability to monitor progress, meet deadlines of project plans.
- Valuing and respecting diverse perspectives, backgrounds, and cultures.
- Incorporating environmentally responsible practices and promoting sustainability.
- Demonstrating ethical behavior, integrity, and professionalism in all aspects of work.
- Openness to feedback, willingness to learn from mistakes, and continuous self-improvement.
- Ability to generate novel ideas, think critically, and propose unique solutions.
Course Content
Quantitative Risk Management and Fintech
Overview: This postgraduate course focuses on the principles and techniques of quantitative risk management with a fintech flavor. The course will provide students with a solid foundation in risk measurement, modeling, mitigation, stress testing, and their applications in the fintech industry.
Course Duration: 12 weeks (final duration not known at the time of preparation of the current syllabus, decision on the final list of topics to be covered conditional on the duration of the course, list of topics below is indicative)
Course Outline:
- Introduction to Risk Management
- Overview of risk management principles and frameworks with a focus in the fintech industry
- Identification and classification of risk types specific including fintech
- Risk governance and regulatory considerations including fintech
- Value at Risk (VaR)
- VaR concept and interpretation
- Parametric VaR estimation techniques
- Historical simulation and Monte Carlo simulation for VaR
- Expected Shortfall (ES)
- ES as a complementary risk measure to VaR
- Parametric, semi-parametric, and nonparametric approaches to ES
- Comparison and interpretation of VaR and ES
- Univariate Risk Models
- Statistical concepts for univariate risk modeling
- Distribution fitting and parameter estimation
- Extreme value theory (EVT) and its applications
- Multivariate Risk Models
- Correlation and dependence modelling
- Copulas and tail dependence for multivariate risk modeling
- Portfolio risk and diversification
- Tail Risk Management
- Understanding tail risk and its impact on risk management
- Tail risk estimation techniques, including extreme value methods
- Tail risk hedging strategies and risk mitigation approaches
- Stress Testing Framework
- Introduction to stress testing and its importance in risk management
- Scenario-based stress testing techniques
- Sensitivity analysis and reverse stress testing customized scenarios
- Backtesting of Risk Models
- Importance of backtesting in evaluating risk models
- Backtesting techniques for VaR and ES models
- Challenges and considerations in backtesting risk models
- Credit Risk Modeling
- Introduction to credit risk
- Credit scoring models and credit risk assessment techniques
- Default probability estimation and credit portfolio risk
- Market Risk Modeling
- Market risk measurement and modeling
- Volatility modeling and estimation techniques
- Risk factors and stress testing in market risk management
- Operational Risk Management
- Understanding operational risk and its challenges including the fintech industry
- Key operational risk indicators and measurement methodologies
- Risk control frameworks and operational risk mitigation strategies
- Advanced Topics in Quantitative Risk Management
- Cybersecurity Risk Management
- Emerging risks in fintech, such as AI and blockchain, and their implications
- Machine learning applications in risk modeling and management
- Case studies and real-world applications of quantitative risk management
Student Performance Evaluation
- Two (2) problem sets to be solved by the students during the course. The delivery of the 1st set is expected at the end of the 4th lecture. The delivery of the 2nd set is expected in the lecture before the last one. Each problem set score counts for 10% of the final course grade. Summary solutions for both problem sets are provided in the last lecture of the course. The summary solutions are available in the e-class platform too (at the end of the course).
- The final examination of the course (in Greek). It includes 40 multiple choice questions covering the entire syllabus (including the two (2) problem sets). No negative marking is applied in the multiple-choice questions. The multiple-choice exam score counts for the 80% of the final grade.
- The following relationship summarizes the weighting scheme of the final grade:
- Final Grade = 10%*(Score on the 1st Problem Set)+ 10%*(Score on the 2nd Problem Set)+ 80%*(Score of Final Exam) (Relationship 1)
- The aforementioned (Relationship 1) for the calculation of the final grade is included in the course syllabus (available in the e-class platform).
Bibliography
Suggested Bibliography
- Risk Management and Financial Institutions by John C. Hull
- Quantitative Risk Management: Concepts, Techniques, and Tools by Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts
- Value at Risk: The New Benchmark for Managing Financial Risk by Philippe Jorion
- Expected Returns: An Investor’s Guide to Harvesting Market Rewards by Antti Ilmanen
- Credit Risk Modeling using Excel and VBA by Gunter Löffler and Peter N. Posch
- Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and MATLAB by Jon Danielsson
- Handbook of Asset and Liability Management: From Models to Optimal Return Strategies by Stavros A. Zenios and William T. Ziemba
- Operational Risk: A Practical Approach to Advanced Measurement by Marcelo G. Cruz
- Cybersecurity Risk Management: A Practical Guide to Securing Your Fintech Environment by Jaco Jacobs and Rudolph Araujo
- Risk Management in Finance: Six Sigma and Other Next Generation Techniques by Anthony Tarantino and Deborah Cernauskas
- Machine Learning for Asset Managers by Marcos López de Prado
- Fintech: The New DNA of Financial Services by Pranay Gupta
- Stress Testing: Approaches, Methods, and Applications by Akhtar Siddique
- Financial Risk Management: Applications in Market, Credit, Asset, and Liability Management by Jimmy Skoglund and Wei Chen
- Risk Management and Financial Derivatives: A Guide to the Mathematics by Charles S. Tapier
Related Academic Journals
- Journal of Risk and Insurance
- Journal of Financial Economics
- Journal of Banking and Finance
- Journal of Financial Risk Management
- Journal of Risk
- Journal of Risk Model Validation
- Journal of Credit Risk
- Journal of Financial Intermediation
- Journal of Operational Risk
- Journal of Cybersecurity
- Journal of Financial Stability
- Journal of Portfolio Management
- Journal of Financial Econometrics
- Journal of Risk and Financial Management
- Journal of Financial Markets