Finance Research Techniques Using Matlab
Essex Business School
Postgraduate: Level 7
Thursday 08 October 2020
Friday 18 December 2020
25 June 2020
Requisites for this module
MSC N34212 Financial Engineering and Risk Management,
MSC N34224 Financial Engineering and Risk Management,
MSC N3G312 Finance and Data Analytics
The aim of this module is to introduce students to the software package Matlab and to provide them with the necessary skills to utilise the software to analyse financial data.
Students will be taught the usefulness of matrix algebra and how vectors and matrices can be manipulated in Matlab. Following this introduction, they will be taught how the software can be used in a number of financial applications including portfolio optimisation, testing for unit roots, cointegration and option pricing. A brief introduction to simulation will also be covered.
The module aims to:
1. Develop and transmit knowledge about the Matlab software package and its usefulness in financial applications.
2. Give students knowledge of a number of key concepts in empirical finance and how Matlab can be used to analyse empirical data.
3. Provide students with a firm foundation for developing their own programmes in Matlab to tackle non-standard testing problems.
After completing this module students will be able to:
1. Have an understanding of matrix algebra and how matrices and vectors can be manipulated in Matlab;
2. Import data and manipulate series in Matlab to calculate financial measures;
3. Construct an optimal or minimum variance portfolio and the efficient frontier;
4. Understand the concept of regression and perform regressions in Matlab;
5. Test for non-stationarity in financial data and examine the possibility of cointegration between one or more series;
6. Have a basic understanding of the Black-Scholes option pricing model and how option prices can be calculated using a Matlab toolbox;
7. Use loop commands in Matlab to perform multiple calculations and understand the relevance of these commands in performing Monte Carlo simulation exercises.
No additional information available.
This Module is normally delivered through:
1. 1-hour lecture each week;
2. 1-hour weekly class.
You will be assigned to a class and you must attend that class. Your class attendance will be monitored!
In academic year 2020-2021 the delivery is likely to be different and involve online learning.
This module does not appear to have a published bibliography.
Assessment items, weightings and deadlines
|Coursework / exam
||120 minutes during Summer (Main Period) (Main)
Module supervisor and teaching staff
Dr Yuqian Zhao, email: firstname.lastname@example.org.
Yuqian Zhao & Mark Hallam
Prof Donal Gregory McKillop
Queen’s University Belfast
Professor of Financial Services
Available via Moodle
Of 21 hours, 11 (52.4%) hours available to students:
10 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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