Module Information

Module Identifier
MA37810
Module Title
Stochastic Models in Finance
Academic Year
2018/2019
Co-ordinator
Semester
Semester 1
Pre-Requisite
Reading List
Other Staff

Course Delivery

Delivery Type Delivery length / details
Lecture 22 x 1 Hour Lectures
 

Assessment

Assessment Type Assessment length / details Proportion
Semester Assessment Coursework  20%
Semester Exam 2 Hours   (Written Examination)  80%
Supplementary Exam 2 Hours   (Written Examination)  100%

Learning Outcomes

On successful completion of this module students should be able to:

Set up mathematical models for modeling random systems over time (stochastic process modeling).

Apply probability and stochastic process theory to model financial models

Analyse and synthesise mathematical models of financial markets.

Aims

The module builds on probability and stochastic processes to introduce continuous time stochastic processes aimed at modelling the stock exchange. We aim to derive the Black-Scholes model of arbitrage option pricing.

Content

Introduction
Stock/Bonds
Arbitrage Pricing
Cox-Ross-Rubenstein Model
Binomial branch/tree models
Binomial Representation theorem
Continuous Processes
The Wiener Process
Black-Scholes Model
Portfolios/Strategies
The Black-Scholes model
Further Topics

Module Skills

Skills Type Skills details
Application of Number throughout the module.
Communication Students will be expected to submit written worksheet solutions.
Improving own Learning and Performance Feedback via tutorials
Information Technology Indicative use of computational modeling stochastic processes.
Personal Development and Career planning Students will be exposed to an area of application that they have not previously encountered.
Problem solving All situations considered are problem-based to a greater or lesser degree.
Research skills Students will be encouraged to consult various books and journals for examples of application.
Subject Specific Skills using probabilistic and stochastic techniques in financial modeling.
Team work N/A

Notes

This module is at CQFW Level 6