Quantitative Methods for Finance and Business
Overview
- Credit value: 15 credits at Level 7
- Convenor and tutor: Dr Christine Guo
- Assessment: a two-hour examination (100%)
Module description
In this module you will gain skills in statistical modelling techniques and explore the application of these techniques in finance-related topics and research applications. The aim is to provide you with the analytical and programming skills to pursue empirical studies in finance.
Indicative syllabus
- Introduction to quantitative methods for finance and business
- Inferential statistics based on the relationship between two variables, regression analysis
- Multivariate regression analysis, time series and financial forecasting models
- Introduction to panel data analysis, pooled regression, cross-section times series
- Panel regressions analysis: fixed effects, random effects models, Hausman test, instrumental variable estimation, GMM, 2SLS, 3SLS
- Advanced financial econometrics VAR model, panel VAR models, dynamic panel models (DPD)
- Panel models of frontier analysis: DEA, SFA (parametric vs nonparametric estimation)
- Artificial neural network, deep learning, artificial neural networks, support vector machine fuzzy logic
Learning objectives
By the end of this module, you should be able to:
- identify and understand the basic statistical principles
- apply existing models of financial data
- use technical and research skills to tackle specific problems in the area of empirical finance
- apply statistical and computing tools in the analysis of financial data
- critically analyse relevant issues in finance and investment
- apply econometrics analysis in analysing financial data.