Financial Econometrics


Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Coursework & Exam



This course is designed to give students an understanding of the basic tools for the statistical and econometric analysis of financial data. A good grounding in Statistics will enable students to develop empirical tests and estimate econometric models that can be used, for instance, in asset pricing, forecasting and risk estimation. The course has an applied emphasis and so the students will be given a good grounding on how to use econometric software to conduct formal statistical analysis of real world financial issues.

The course will make it possible for participants:

  • To acquire an in-depth and practical understanding of the basic tools for empirical modelling and statistical inference in Financial Markets.
  • To assess critically the current state-of-the-art of empirical research in a range of topics in Finance.
  • To develop the practical skills required to carry out research in Financial Markets using standard econometric software.
  • To be able to apply for positions, for instance, in the research, portfolio management and foreign exchange units of companies, financial institutions and organizations.


Learning Outcomes

On completing the course the participants will be able to:

  • Understand the basic principles for the statistical analysis of financial data.
  • Understand the mechanics of hypothesis testing in the context of financial markets.
  • Develop empirical models that capture the stylized behaviour of financial data.
  • Use standard econometric software to undertake empirical research.
  • Assess critically other empirical work given the framework developed in the course.
  • Familiarize themselves with the fundamental principles of financial econometrics.
  • Understand the empirical relations between risk and return.
  • Understand the empirical issues related to portfolio theory, asset pricing and behaviour of capital markets.
  • Apply financial and investment decision criteria in a variety of business cases.
  • Utilize valuation concepts as applied to shares and bonds.
  • Use models and their applications in relation to investment and business decisions.



  • Hypothesis Testing, Specification Testing, Dummy Variables
  • Estimation Methods and Inference
  • Regression
  • Introduction to Time Series Analysis
  • Univariate ARMA models, Autocorrelation and partial autocorrelation function
  • Granger causality tests
  • Volatility clustering and ARCH models
  • Nonstationary Variables