CF962-7-AU-CO:
Quantitative Methods in Finance and Trading
2021/22
Computational Finance and Economic Agents (Centre for)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 07 October 2021
Friday 17 December 2021
15
31 March 2021
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC N30312 Computational Finance,
MSC N35012 Artificial Intelligence in Finance
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.
Every lecture will be followed by a MATLAB lab session where the ideas will be put into practice using Matlab.
This module does not appear to have a published bibliography for this year.
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: 120 minutes during Early Exams
|
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.
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 56 hours, 48 (85.7%) hours available to students:
4 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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