# Financial Risk Management

## 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.

If you are thinking about taking this unit, please note the following. Many people find uncertainty and risk unintuitive, and therefore clarity and effective communication are crucial. If you are uncomfortable writing descriptive text in well-structured sentences, then you should choose a different unit. 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.

## Learning objectives

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.

## Transferable skills

As Paul Erdos once ruefully remarked, "Probability is the only branch of math in which a brilliant mathematician can make an elementary error." Uncertainty is ubiquitous in our lives, and our intuition often lets us down, even if we are Paul Erdos. This unit teaches you to make better choices by providing guidelines for thinking carefully about uncertainty. This is a core life-skill.

## 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.

Unit code: MATH30014
Level of study: H/6
Credit points: 20
Teaching block (weeks): 1 (1-12)
Lecturer: Professor Jonathan Rougier

## Pre-requisites

Calculus 1 (MATH11007), Linear Algebra & Geometry (MATH11005), Analysis 1A (MATH10003), Analysis 1B (MATH10006), Probability 1 (MATH11300), Statistics 1 (MATH11400)

None

## Methods of teaching

Lectures, regular formative problem sheets and office hours

## Methods of Assessment

The pass mark for this unit is 40.

The final mark is calculated as follows:

• 100% 2 hours 30 minutes Examination in January

NOTE: Calculators of an approved type (non-programmable, no text facility) are allowed.

For information resit arrangements, please see the re-sit page on the intranet.

Please use these links for further information on relative weighting and marking criteria.

Further exam information can be found on the Maths Intranet.