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. 

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.

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1. Project output; Group; 3000 words; 50%; ILOs 1,4-5.

2. Degree exam: in-person; Individual; 90 minutes; 50%; ILOs 1-3.

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.