Course Catalogue

View Specification Document | Reading List

This course offers a thorough exploration of stochastic modelling techniques and quantitative analysis in financial risk management. Students will develop the skills to construct and apply key risk measures, such as Value at Risk (VaR) and Expected Shortfall, while gaining practical insights into risk theory and its application to financial markets and investment strategies. The course also delves into credit risk analysis and credit derivatives, emphasizing both structural and reduced-form models as well as credit scoring techniques. Throughout the course, students will leverage programming languages to implement risk models and tackle real-world financial and economic problems.

Financial Risk Analytics offers a comprehensive exploration of stochastic modelling techniques and quantitative analysis for managing financial risk. The course equips students with the skills to construct and apply key risk measures, while gaining practical insights into risk theory and its application to financial markets and investment strategies. Students will also delve into credit risk analysis, including both structural and reduced-form models, as well as credit scoring and the valuation of credit derivatives. A key feature of this course is the emphasis on digital skills development, where students will leverage programming languages, such as Python and R, to implement risk models and tackle real-world financial and economic problems. The use of these tools will enhance students' technical proficiency in financial risk management, fostering research and analytical skills crucial for modern financial industries.

More specifically, the course will enable the students to develop technical skills such as applying stochastic and quantitative techniques to solve real-world risk management problems, accurately implementing risk models using mathematical methods, critically evaluating financial risks and propose risk management and hedging strategies based on quantitative analysis. In addition to developing technical skills, students will engage with international financial datasets, gaining a global perspective on risk management, which is critical for understanding cross-border financial risks and regulatory frameworks.

The course integrates experiential learning and practice-based activities through a report based on empirical case studies, where students will apply theoretical concepts to real-world financial scenarios. Peer-based learning forms a core part of this course, helping students develop the skills to analyse and devise risk management solutions. By engaging in projects and problem-based learning activities, students will replicate real-world professional scenarios, working with global financial datasets to understand market behaviour from an international perspective. Continuous feedback throughout the course will support a learning-through-assessment approach, sharpening students' problem-solving abilities and enhancing their ability to address complex financial issues.

Through simulations, interactive labs, and the use of digital technologies, students will engage in practice-based learning that reflects real-world professional environments. Upon completion, students will be well-prepared for careers in risk management, financial analysis, investment banking, or advanced research in financial risk analytics. The skills developed in this course are essential for roles involving risk modelling, credit risk analysis, financial forecasting, and the application of cutting-edge financial technologies in a dynamic and evolving global economy.

Synchronous:

One 2-hour lecture per week for 10 weeks (on-campus)

Six 1-hour labs (on-campus)

Asynchronous:

This will take place with directed reading, a set of exercises including theoretical, derivational, and applied type of questions with the corresponding solution guides, which will be further discussed in lectures and labs, for approximately 5 hours each week.

Students must be registered on one of the associated programmes listed in this course specification.

None

None

ILO (covered)

This course aims to:

■ Provide students with an in-depth understanding of stochastic modelling techniques in financial markets.

■ Equip students with the knowledge to construct and apply risk measures such as Value at Risk (VaR) and Expected Shortfall, and critically assess these methods in financial risk management.

■ Develop students' ability to perform credit risk modelling and analysis using both structural and reduced-form approaches, as well as apply credit scoring techniques and data-driven approaches on real-world data to analyse financial risk models.

By the end of this course students will be able to:

1. Critically appraise stochastic modelling techniques and different risk measures for financial markets.

2. Apply quantitative methods to construct risk measures and manage financial risks, while understanding their practical applications.

3. Use appropriate programming tools to apply data-driven approaches in measuring financial risks and conducting risk analysis.

4. Apply appropriate investment and liability risk models to real-world financial scenarios.

5. Perform credit risk analysis using statistical methods, applying them to economic and financial data.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.