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Quantitative Techniques

Overview

  • Credit value: 30 credits at Level 6
  • Convenor: to be confirmed
  • Assessment: examinations in September and coursework

Module description

This module is taught mostly as three pre-sessional parts in September, and will ensure that you have the basic quantitative techniques for the MSc programme. If you are studying part-time, you review basic mathematics in September and study static and dynamic optimisation later in the academic year. Prior to the start of the second year, you review statistical techniques. Full-time students cover all parts in one year. The three parts are:

  1. Mathematics
  2. Statistics
  3. Introduction to Finance

1. Mathematics

This will help you:

  • understand the basics of sets and functions, including standard
  • understand the basics of linear algebra and the use of matrices
  • learn how to find the constrained optima of multivariate functions
  • compute definite and indefinite integrals
  • solve simple difference and differential equations
  • use these techniques to solve simple problems.

Assessment: a two-hour written examination held at the end of September.

2. Statistics

This covers:

  • probability and distribution theories (probability, random variables and probability distributions, expectations and moments, univariate and multi-variate distributions, functions of random variables)
  • statistical inference (sampling, large sample theory, point estimation, parametric interval estimation, tests of statistical hypotheses).

Assessment: a two-hour written examination held at the end of September.

3. Introduction to Finance

This introduces key ideas and concepts, such as:

  • no-arbitrage pricing
  • risk-return trade-offs
  • the Capital Asset Pricing Model
  • the basics of derivative pricing.

Assessment: a short multiple choice test taken at the same time as the qualifying examination in statistics.

Learning objectives

By the end of this module, you should be able to:

  • use matrices for algebraic manipulations
  • understand and use the techniques of static and dynamic optimisation
  • compute definite and indefinite integrals
  • solve simple difference and differential equations
  • understand the basic of probability distributions
  • understand statistical inference.