This course covers econometric and statistical methods that are essential for analysing financial data. This module is one of the core modules taught in Term 1 and a prerequisite for MSIN0106 Advanced Quantitative Methods for Finance taught in Term 2.
Proper implementation of statistical tools and correct interpretation of the results are fundamental for having a career in financial profession. For this goal, this module aims to offer the students to master (i) the fundamental idea and basic theories of estimation and statistical inference, (ii) a variety of estimation methods that are useful for analysing financial data, and (iii) the practical skills of implementing these methods using statistical software.
Upon successful completion of the module, a student will be able to:
- Have solid and sound understanding of the theories of statistical estimation and inference
- Make a right choice of an econometric method in various contexts of empirical practice.
- Perform by themselves the basic yet useful statistical analysis for a various kinds of data including financial data.
- Obtain analytical skills to learn advanced tools in time-series econometrics.
- Obtain practical computational skills by learning how to use R, a statistical software commonly used among researchers and practitioners in econometrics.
- Basic theory of statistical inference (estimation, confidence intervals, and hypothesis testing)
- Law of large numbers, central limit theorems, and asymptotic analysis
- Linear regression, ordinary and generalized least square estimation
- Regression for dependent data
- Maximum likelihood
- Bayesian inference
- Generalized method of moments
- Monte Carlo and resampling methods
- Quantile regression
- Nonparametric density estimation and nonparametric regression
Unseen Examination (2 hours) 80% Group Coursework 20%
Coursework consists of four problem sets given during Term 1, each of which is weighted equally (5%).
Current students should refer to Moodle for specific details of the current year’s assessment.
No textbook. Suggested readings will be introduced during the course. They will be posted on the Moodle course page.