F71SR Research and Industry Topics

Dr Simon Malham

Course co-ordinator(s): Dr Simon Malham (Edinburgh).

Aims:

The aim of this course is to provide students with experience of recent developments in quantitative financial mathematics and of the practice of risk management.

Detailed Information

Pre-requisites: none.

Location: Edinburgh.

Semester: 2.

Syllabus:

At the heart of the Research and Industry Topics are four equally weighted guided reading projects, two of which will be freely selected from the Scottish Financial Risk Academy’s annual portfolio of industry offered projects, and two of which will be offered by academic staff. Of each of these two pairs of projects, one will be assessed through a written report, and one project will be assessed through a presentation.

The academic projects will be offered at the start of the semester and will be examined midway through the semester (usually week 6). The industry topics are usually undertaken in the latter part of the semester, and will be examined at the end of the semester.

The industry projects are offered by a variety of Scottish financial services companies through the Scottish Financial Risk Academy on topics such as

  • Market risk
  • Credit risk
  • Operational risk
  • Understanding hedge funds
  • Liquidity risk
  • Energy and commodity risk
  • Risk management in insurance: Solvency II

Learning Outcomes:

Subject Mastery:

On completion of this course the student should be able to:

  • read one or more research papers for each Research and Industry Topics subject
  • show detailed and critical understanding of selected recent developments in quantitative finance and mathematics.
  • demonstrate expertise in applying a variety of techniques in quantitative finance and mathematics in the context of relevant problems

Learning Outcomes

At the end of the course students should be able to:

  • be able to write a coherent essay on each of two topics in a way that demonstrates they have understood the material
  • present effectively their results in a way that demonstrates they have understood the relevant research papers and that they have accurately performed any numerical calculations
  • demonstrate the ability to learn independently
  • demonstrate skills in the understanding and processing of numerical information and interpretation of statistics
  • perform numerical calculations using a suitable computer language or package as required for individual topics
  • show an appreciation of the interface between academic theory and industrial practice
  • demonstrate the ability to learn independently and as part of a group
  • manage time, work to deadlines and prioritise workloads

Assessment Methods:

  • Essay on two topics 25% x 2=50%
  • Presentation on two topics 25% x 2=50%

SCQF Level: 11.

Credits: 15.