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# Numerical & Computing Techniques in Finance (Online Version) - MAT00031M

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• Department: Mathematics
• Module co-ordinator: Prof. Tomasz Zastawniak
• Credit value: 20 credits
• Credit level: M
• Academic year of delivery: 2022-23

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## Module will run

Occurrence Teaching period
A1 Autumn Term 2022-23 to Spring Term 2022-23
A2 Autumn Term 2022-23 to Summer Term 2022-23
B1 Spring Term 2022-23 to Summer Term 2022-23
B2 Spring Term 2022-23 to Spring Term 2023-24

## Module aims

The aim of the module is to provide programming skills required for the implementation of mathematical models in quantitative finance. The focus will be on the C++ programming language, which is widely accepted as the main tool amongst practitioners in the financial community. The implementation of a given model rarely narrows down to the pricing of a single particular financial instrument. Most often it is possible to devise general numerical schemes which can be applied to various types of derivatives. The code should be designed so that it easily integrates with the work of other developers and can be modified by other users. The student will learn such skills by writing C++ programs designed for pricing various types of derivatives, starting from the simplest discrete time models and finishing with continuous time models based on finite difference or Monte Carlo methods.

## Module learning outcomes

By the end of the module, students should:

• be able to write comprehensive C++ programs;
• be familiar with functions and function pointers;
• be familiar with classes and handle virtual functions, inheritance and multiple inheritance;
• be able to implement non-linear solvers;
• be familiar with data structures and dynamic memory allocation;
• understand and have experience of using class and function templates;
• be familiar with standard numerical methods (finite difference, Monte Carlo) for solving representative problems;
• be able to price European and American options under the CRR model;
• be able to price American options by means of finite difference methods under assumptions of the Black Scholes model;
• be able to price barrier and Asian options by means of Monte Carlo simulation.

## Assessment

Task Length % of module mark
Coursework - extensions not feasible/practicable
Coursework Assignments
N/A 100
Oral presentation/seminar/exam
Online Viva
N/A 0

None

### Reassessment

Task Length % of module mark
Coursework - extensions not feasible/practicable
Coursework Assignments
N/A 100
Oral presentation/seminar/exam
Online Viva
N/A 0

## Module feedback

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