Course Catalogue

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Financial Econometrics offers an in-depth exploration of advanced econometric techniques and their applications in financial economics. The course focuses on critical areas such as financial time series analysis, volatility modelling, asset pricing, and risk management, using state-of-the-art econometric tools. Students will engage with both theoretical foundations and empirical testing methods to address real-world financial challenges. 

Key topics include univariate and multivariate time series models, volatility forecasting, risk assessment, and portfolio analysis. The course emphasizes the application of advanced software tools such as Python and R to financial datasets, allowing students to develop digital skills and implement algorithms for financial forecasting, risk management, and asset pricing.

A future-oriented approach is adopted, focusing on contemporary issues such as big data modelling in finance. Students will gain hands-on experience with mathematical and statistical methods, learning to apply econometric models in complex financial environments, through practice-based learning in labs and tutorials, simulations and interactive lectures.

Collaborative projects, peer and problem-based learning are integral to the course, simulating real-world professional settings. Students will work with international financial datasets, offering a global perspective on market behaviour and enhancing their ability to solve complex financial problems in group settings.

Upon completion, students will be well-prepared for careers in finance, banking, or advanced research in financial econometrics. The skills developed in this course are essential for roles involving advanced econometric techniques, financial risk analysis, asset pricing, and predictive financial modelling in an evolving global economy.

Synchronous:

10 x 2-hour lectures on campus/online as appropriate for the course content

5 x 1-hour labs on campus/online as appropriate for the course content

4 x 1-hour tutorials on campus/online as appropriate for the course content

Asynchronous:
5 hours for set of numerical and computational exercises that will include both theoretical, derivational, and applied type of questions with the corresponding solution guides, which will be further discussed in labs and tutorials.

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

None

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ILO being assessed

This course aims to:

1. Provide an in-depth understanding of linear and nonlinear, univariate and multivariate time series methods, with applications to financial analysis.

2. Prepare students to analyse complex financial data structures using advanced econometric methods.

3. Equip students with the skills to measure and manage financial risks effectively in dynamic market environments.

4. Enhance employability through experiential learning, utilizing real-world financial data and risk management scenarios.

5. Promote ethical awareness and sustainability in financial decision-making processes.

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

1. Critically evaluate technical and practical issues associated with financial econometric models, including time series analysis, volatility models, and asset pricing techniques, considering their relevance in financial markets.

2. Formulate and justify a range of estimators and methodologies specific to financial econometrics, demonstrating their optimal use in empirical financial data analysis.

3. Apply advanced knowledge and understanding of financial econometric concepts and interpret key research ideas from state-of-the-art literature in the field.

4. Implement applied projects using financial data utilizing advanced software while preparing for future challenges in financial data analysis.

5. Develop and program advanced financial algorithms for accurate estimation and statistical inference, focusing on real-world financial scenarios related to risk management and price modelling.

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