University of Technology Sydney

25879 Statistics and Financial Econometrics

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2020 is available in the Archives.

UTS: Science: Mathematical and Physical Sciences
Credit points: 8 cp

Subject level:


Result type: Grade and marks

There are course requisites for this subject. See access conditions.


The subject reviews the necessary statistical and econometric tools for modelling financial data and describing stylised facts about asset returns. It also introduces students to statistical inference and model estimation for financial time series.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Define the main probability-theoretic and statistical concepts required in modern finance
2. Apply a range of statistical techniques to the analysis of financial data
3. Apply econometric techniques to estimate popular models using financial time series

Contribution to the development of graduate attributes

The subject provides a foundation for a mathematical treatment of stochastic modelling. It contributes to the objectives of the course by focussing on the principles and methods of modern probability theory, and their application to financial data.

This subject contributes to the development of the following graduate attributes:

  • Business knowledge and concepts
  • Business practice oriented skills

This subject also contributes specifically to develop the following Program Learning Objectives for the Master of Quantitative Finance:

  • 5.1: Master quantitative finance technical skills necessary for professional practice

Teaching and learning strategies

The subject is presented in seminar format. Essential principles are presented and analysed in the lecture component, after which students are guided through practical application exercises.

Content (topics)

  • Random variables
  • Univariate and multivariate distributions
  • Copulas
  • Principal component analysis
  • Sampling
  • Goodness-of-fit tests
  • Maximum likelihood estimation
  • Estimation of diffusion processes
  • Regression analysis


Assessment task 1: Assignments (Individual)


This assessment task addresses subject learning objective(s):

1, 2 and 3

Weight: 50%

Assessment task 2: Final Exam (Individual)


This assessment task addresses subject learning objective(s):

1, 2 and 3

This assessment task contributes to the development of course intended learning outcome(s):


Weight: 50%

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

Recommended texts

Hinz, Juri: Statistics and Financial Econometrics (Lecture Notes)


Gut, Allan: An Intermediate Course in Probability

Wackerly, D., Mendenhall, W. and Scheaffer: Mathematical Statistics with Applications

Brockwell, Peter J., Davis, Richard A.: Introduction to Time Series and Forecasting

Ivchenko, Medvedev, Chistyakov: Problems in Mathematical Statistics

Green, W., Econometric Analysis, 6th edition, Prentice Hall, 2008

Miller, M., and Miller, M., Mathematical Statistics, 6th edition, Prentice Hall, 1999

Rice, J., Mathematical Statistics and Data Analysis, 2nd edition, Duxbury Press, 1995

Ross, S. M., Introduction to Probability Models, 10th edition, Academic Press, 2010