CF962-7-AU-CO:
Quantitative Methods in Finance and Trading
2023/24
Computational Finance and Economic Agents (Centre for)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2023
Friday 15 December 2023
15
03 August 2023
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC N30312 Computational Finance,
MSC N35012 Algorithmic Trading
This module focuses on quantitative methods in finance and economics and their application to investment, risk management and trading. The module will introduce students to state-of-the-art statistical modelling of financial markets and will give an overview of the quantitative framework that is necessary to advance to other CCFEA modules.
The first part of this module covers a review of statistical concepts, allowing students to analyse stylised facts such as fat tails, skewness, volatility clustering or long memory. An introduction to financial econometrics will follow, where emphasis will be given to the analysis of financial time series models such as moving average, ARIMA and GARCH. Applying these methods to empirical financial problems, students will investigate topics like value-at-risk, portfolio optimisation, index tracking, pairs trading and statistical arbitrage. The module will also give an overview of the most popular computational methods in quantitative finance, in particular Bootstrapping and Monte Carlo Simulation.
In the computer lab sessions, students will be engaged in MATLAB exercises and financial case studies that will illustrate the practical implementation of the models introduced in the lectures.
The aims of this module are to focus on quantitative methods in finance and economics and apply them to investment risk management and trading. The module introduces statistical modelling and financial markets and gives an overview of the framework necessary to advance to other CFFEA modules.
On successful completion of the module students are expected
(1) to have a solid knowledge of financial econometric methods,
(2) to be able to model stylized facts of financial asset returns, and
(3) to grasp the intuition behind the arsenal of quantitative techniques that attempt to capture them.
No additional information available.
This module will be delivered via:
- Lectures
- Labs
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Progress Test 1 |
|
50.00% |
| Coursework |
Progress Test 2 |
|
50.00% |
| Exam |
Main exam: In-Person, Open Book (Restricted), 120 minutes during Early Exams
|
| Exam |
Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period)
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Michael Kampouridis, email: mkampo@essex.ac.uk.
Dr Michael Kampouridis
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Yes
No
Yes
Dr Anna Jordanous
University of Kent
Senior Lecturer
Available via Moodle
Of 40 hours, 22 (55%) hours available to students:
14 hours not recorded due to service coverage or fault;
4 hours not recorded due to opt-out by lecturer(s), module, or event type.
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