Unit information: Financial Risk Management in 2020/21

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. Skevi Michael
Open unit status	Not open
Pre-requisites	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)
Co-requisites	None
School/department	School of Mathematics
Faculty	Faculty of Science

Description including Unit Aims

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 Information

The unit will be taught through a combination of

• synchronous online and, if subsequently possible, face-to-face lectures
• asynchronous online materials, including narrated presentations and worked examples
• guided asynchronous independent activities such as problem sheets and/or other exercises
• synchronous weekly group problem/example classes, workshops and/or tutorials
• synchronous weekly group tutorials
• synchronous weekly office hours

Assessment Information

90% Timed, open-book examination 10% Coursework

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.

If you fail this unit and are required to resit, reassessment is by a written examination in the August/September Resit and Supplementary exam period.

Reading and References

Recommended

• 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