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Unit information: Financial Risk Management in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Financial Risk Management
Unit code MATH30014
Credit points 20
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Ayalvadi Ganesh
Open unit status Not open

MATH11007 Calculus 1 (or MATH10012 ODEs, Curves and Dynamics), MATH11005 Linear Algebra and Geometry, MATH10003 Analysis 1A and MATH10006 Analysis 1B (or MATH10011 Analysis), MATH11300 Probability 1 and MATH11400 Statistics 1 (or MATH10013 Probability and Statistics)



School/department School of Mathematics
Faculty Faculty of Science


Unit Aims

To explore the theory and practice of financial risk management in a variety of common settings, including the casino, sports betting, business, and financial markets.

Unit Description

The unit covers the theory of uncertainty assessment, choice under uncertainty, and risk management (see the Learning Objectives below), and illustrates with many practical examples, often involving computing in R. Familiarity with R is not required for the unit, but if you are thinking about a job in finance or data science then you should be aiming to be proficient in R or Python by the time you graduate.

Clarity and effective communication are crucial and you will also need to be comfortable writing descriptive text in well-structured sentences. You will be expected to explore more qualitative aspects of human capacity and desires, as a necessary part of understanding the practice of risk management.

Intended learning outcomes

At the end of this unit you should be able to:

  • Use probability theory to structure and quantify uncertainty.
  • Justify the use of expected gain as a method for choosing among small gambles.
  • Evaluate simple gambles, such as those found in casinos.
  • Explain the role of statistical models, and give examples.
  • State, prove, and explain the Von Neumann-Morgenstern theorem for expected utility.
  • Provide simple guidelines for assessing individual utility functions.
  • Use decision trees to evaluate linked decisions, and to value information.
  • State and critique mean-variance portfolio theory.

Teaching details

Lectures, regular formative problem sheets and office hours

Assessment Details

100% examination (2.5 hours)

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

Reading and References


  • John S. Hammond, Ralph L. Keeney, and Howard Raiffa, Smart Choices: A Practical Guide to making Better Decisions, Harvard Business School Press, 1999
  • Dennis V. Lindley, Understanding Uncertainty, revised edition, John Wiley & Sons, 2014