Quantitative Finance

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• have a good understanding of the characteristics of financial data and interpret them within existing economic and/or financial theories;
• have a good understanding of specific applications on financial econometrics
• have a good understanding of the basic theory of financial econometrics

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• apply your understanding on the theory of financial econometrics to a specific empirical project
• demonstrate competence in using an econometrics software package
• be able to interpret computer output for the estimation and testing of econometric relationships

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• demonstrate skills in utilising analysis software.
• apply knowledge learned in the class to particular problems.

The module offers a comprehensive introduction to Quantitative Finance. Students will learn various methods to analyse financial data.

Structure of linear models

• Functional forms
• Log linear models
• Quadratic models
• Lags
• Dummy variables

Exploring data

• Descriptive statistics
• Outliers
• Missing data

Regression analysis

• Ordinary least squares (OLS) estimator
• Classical OLS assumption
• Statistical significance
• Specification tests

Testing for violation of assumptions and their implications

• Heteroskedasticity
• Autocorrelation
• Multicollinearity
• Endogeneity

Analysing panel data

• Fixed effects
• Random effects
• Serial correlation

Limited dependent variable models

• Logit and probit
• Interpretation of analysis and marginal effects
• Predictive analysis

Time series modelling

• Deterministic models
• Stationarity and unit roots
• ARIMA
• Co-integration

Teaching methods include:

Weekly lectures will provide an overview of the main issues arising in the unit, and will be supplemented by weekly empirical and theoretical exercises. Exercises will support student learning by providing opportunities for students to attempt, and gain feedback on, numerical and problem-solving exercises. Students will also have the opportunity for both directed and non-directed independent reading

The module will be taught by a mixture of methods ranging from guided background reading, lectures, group work and the exploration of mini case studies and datasets. The lecturer will draw upon market developments current at the time of the course.

The lecturer will introduce the concepts, and participants will have the opportunity to practice and apply the methods discussed. We will do a step-by-step analysis of different financial data (stock market data, firm and industry-specific data).

Learning activities include:

• Eviews based exercises in class
• A group assignment
• Discussion of findings in class

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Computer practicals

• Assessment Type: Formative
• Feedback: Students will get feedback in terms of their success in completing the computer labs following each lecture. Their ability to complete the set tasks will provide feedback on their progress and flag any issues.
• Final Assessment: No
• Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.