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MFE1102 — Project in Quantitative risk management | Chương trình đào tạo NEU

MFE1102 — Project in Quantitative risk management

Credits
4
Showing intake
K68-2026

Course description

Project in Quantitative Risk Management is designed to equip students with the ability to analyze and manage financial risks using quantitative methods in real-world institutional settings such as banks, financial institutions, and investment firms. The course emphasizes the application of advanced knowledge in Mathematics, Statistics, Econometrics, and Financial Modeling to identify, measure, and control various types of risks.

Through a project-based learning approach, students will implement a full risk management cycle, including:

Risk Identification, Data Collection and Preprocessing, Quantitative Risk Modeling (e.g., market risk, credit risk, liquidity risk), Risk Measurement and Stress Testing, and Risk Reporting and Visualization.

Students will engage with practical datasets and case studies from the banking, insurance, and financial markets, and may interact with industry practitioners to gain insights into how quantitative risk models are applied in regulatory compliance (Basel III/IV), internal risk control, and strategic decision-making.

Upon completion of the course, students are required to develop a quantitative risk product (e.g., risk model, stress-testing framework, VaR/ES report, or risk dashboard) and submit a comprehensive project report that documents the methodology, empirical results, risk assessment, and data-driven risk management recommendations.

Course requirements

Attendance Regulations

Students are responsible for attending all Seminars, instructor guidance sessions, and team activities.

Students absent for more than 3 sessions (or not contributing to the group project) will be considered as failing the course and will receive a grade of 0.

Submission Regulations

Project products (Code, Data, Dashboard) must be uploaded to the Class Repository (GitHub/Google Drive) on time.

Copying code/reports from other groups or old projects is strictly prohibited. Any plagiarism will be handled according to University regulations.

Teams that do not submit reports on time will not be allowed to participate in the defense.

Lecturers

  • Dr. Hoang Duc Manh
  • Dr. Nguyen Thi Lien

Teaching plan

Week 1

Phase 1: Kick-off & Orientation Course introduction, learning outcomes, assessment criteria, and team formation. Industry Seminar: Practitioners from banking, finance, insurance, and investment institutions present real-world risk management challenges (market risk, credit risk, liquidity risk, regulatory compliance).
References
Seminar materials, Reference papers
Activities
Lecturer: - Introduce syllabus & grading. - Invite experts. - Guide topic selection. Students: - Form teams. - Search for ideas and preliminary data.
Assessment
CLO 2.1 CLO 4.1

Week 3

Developing Project Proposal Define risk management problems (e.g., VaR estimation, credit default risk, stress testing). Identify risk factors, scope of analysis, and expected data sources (market data, macroeconomic indicators, financial statements).
References
Lecture slides, Proposal template
Activities
Lecturer: - Approve team topics. - Feedback on approach. Students: - Present ideas. - Finalize project scope.
Assessment
CLO 2.1 CLO 2.2

Week 4

Phase 2: Risk Data Collection & Engineering Risk Data Collection (financial databases, APIs, central bank data, internal datasets). Data cleaning and preprocessing: handling missing values, outliers, non-stationarity, and data transformation. Exploratory Data Analysis (EDA): statistical properties, correlations, volatility clustering, and stylized facts.

Learning resources

  • 1. Textbooks
  • 2. Reference Materials
  • . Quantitative Risk Management: Concepts — McNeil, A. J., Frey, R., & Embrechts, P., Techniques and Tools. Princeton University Press, 2015
  • . Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill — Jorion, P., 2007
  • . Risk Management and Financial Institutions. Wiley — Hull, J. C., 2021
  • 3. Software
References
Python/Pandas docs, Preprocessing textbooks
Activities
Lecturer: - Guide practice in Lab. - Review code and EDA results per team. Students: - Write data processing scripts. - Build descriptive statistics reports.
Assessment
CLO 3.1 CLO 4.1

Week 8

MID-TERM REVIEW - Present progress, clean data, and initial insights.
Activities
Lecturer: Evaluate progress. Students: Team presentation.
Assessment
Formative Assessment (40%)

Week 9

hase 3: Quantitative Risk Modeling & Advanced Analysis Model selection and estimation: Market risk models (VaR, ES, GARCH-family models) Credit risk models (PD, LGD, scorecards, ML-based models) Model calibration and hyperparameter tuning. Model validation and performance evaluation (backtesting, stress testing, robustness checks).
References
Scikit-learn docs, ML algorithms
Activities
Lecturer: - Advanced technical consulting (feature selection, fixing overfitting...). Students: - Run models. - Compare and evaluate results.
Assessment
CLO 1.2 CLO 3.2

Week 12

Phase 4: Risk Visualization & Reporting Develop risk dashboards and reporting tools (Power BI, Tableau, Python/R-based apps). Write the final project report: methodology, results, risk interpretation, and managerial implications. Apply data storytelling to communicate risk exposure and recommendations effectively.
References
Data Visualization docs
Activities
Lecturer: - Feedback on report drafts. - Feedback on Dashboard design. Students: - Finalize product. - Rehearse presentation.
Assessment
CLO 3.3 CLO 4.2

Week 15

FINAL DEFENSE - Present product and report to the council.
Activities
Council: Grading, Q&A. Students: Defend research results.
Assessment
Final Assessment (50%)